[
  {
    "path": ".gitignore",
    "content": "build\ndevel\nbag\nscreen.txt\n.catkin_workspace\nsrc/CMakeLists.txt"
  },
  {
    "path": ".vscode/c_cpp_properties.json",
    "content": "{\n    \"configurations\": [\n        {\n            \"browse\": {\n                \"databaseFilename\": \"\",\n                \"limitSymbolsToIncludedHeaders\": true\n            },\n            \"includePath\": [\n                \"/home/ji/ros_ws/simulator/devel/include/**\",\n                \"/opt/ros/noetic/include/**\",\n                \"/home/ji/ros_ws/simulator/src/uav_simulator/mockamap/include/**\",\n                \"/home/ji/ros_ws/simulator/src/uav_simulator/so3_controller/include/**\",\n                \"/home/ji/ros_ws/simulator/src/uav_simulator/so3_quadrotor/include/**\",\n                \"/usr/include/**\"\n            ],\n            \"name\": \"ROS\",\n            \"compilerPath\": \"/usr/bin/gcc\"\n        }\n    ],\n    \"version\": 4\n}"
  },
  {
    "path": ".vscode/settings.json",
    "content": "{\n  \"python.autoComplete.extraPaths\": [\n    \"/home/ji/ros_ws/simulator/devel/lib/python3/dist-packages\",\n    \"/opt/ros/noetic/lib/python3/dist-packages\"\n  ],\n  \"files.associations\": {\n    \"*.ipp\": \"cpp\",\n    \"alignedvector3\": \"cpp\",\n    \"csignal\": \"cpp\",\n    \"*.tcc\": \"cpp\",\n    \"codecvt\": \"cpp\",\n    \"forward_list\": \"cpp\",\n    \"filesystem\": \"cpp\",\n    \"regex\": \"cpp\",\n    \"future\": \"cpp\",\n    \"valarray\": \"cpp\"\n  }\n}"
  },
  {
    "path": "README.md",
    "content": "# CMPCC\nCorridor-based Model Predictive Contouring Control for Aggressive Drone Flight\n\n## 0. Overview\n**CMPCC** is an efficient, receding horizon, local adaptive low level planner as the middle layer between our original planner and controller [trr](https://github.com/USTfgaoaa/Teach-Repeat-Replan). \n\n**Author**: Jialin Ji, Xin Zhou and [Fei Gao](https://ustfei.com/) from the [ZJU Fast Lab](www.kivact.com/). \n\n**Related Paper**: [CMPCC: Corridor-based Model PredictiveContouring Control for Aggressive Drone Flight](https://arxiv.org/abs/2007.03271), Jialin Ji, Xin Zhou, Chao Xu, Fei Gao, accepted, International Symposium on Experimental Robotics (ISER 2020).\n\n**Video Links**: [youtube](https://www.youtube.com/watch?v=_7CzBh-0wQ0) or [bilibili](https://www.bilibili.com/video/BV1cZ4y1u7Ur)\n\n![](figs/1.gif)\n![](figs/2.gif)\n\n### Features\n-  online flight speed optimization\n-  strict safety and feasibility\n-  real-time performance\n### File Structure\n- **src/cmpcc**: key module of CMPCC, which is a single ros package \n- **src/simualtion**: a lightweight simulator for quadrotors \n- **src/utils**: some functional codes and plugins\n- **osqp**: a copy of [OSQP](https://osqp.org/) source code for solving qp problem.\n- **key2joy.py**: a user-interface tool based on pygame for setting disturbance\n\n## 1. Prerequisites\nOur software is developed and tested in Ubuntu 18.04, ROS Melodic. Other version may require minor modification. \n\nYou can clone this repository: \n```\ngit clone https://github.com/ZJU-FAST-Lab/CMPCC.git\ncd CMPCC\n```\nTo install the following dependencies, you can run the auto-install script by\n```\nchmod +x install_tools.sh\n./install_tools.sh\n```\nIf failed, you can manually install them one by one:\n- install dependencies\n```\nsudo apt-get install libyaml-cpp-dev\nsudo apt-get install libarmadillo-dev\n```\n- install pygame for key2joy.py\n```\nsudo apt-get install python-pip\npip install pygame\n```\n- install osqp\n\n```\ncd osqp\nmkdir build\ncd build\ncmake -G \"Unix Makefiles\" ..\ncmake --build .\nsudo cmake --build . --target install\n```\n\n## 2. Build on ROS\nAfter the prerequisites are satisfied, you can catkin_make in this repository directory, which is already a ros-workspace:\n```\ncatkin_make\n``` \n\n## 3. Run the Simulation\n```\nsource devel/setup.bash\n./simulation.sh\n```\nYou can set wind disturbance with keyboard \"WASD\". The default disturbance is quite fierce. If the drone flew outside the corridor, you would have to relauch the nodes.\n\n## 4. Licence\nThe source code is released under [GPLv3](http://www.gnu.org/licenses/) license.\n\n## 5. Maintaince\n\nFor any technical issues, please contact Jialin JI (jlji@zju.edu.cn) or Fei GAO (fgaoaa@zju.edu.cn).\n\nFor commercial inquiries, please contact Fei GAO (fgaoaa@zju.edu.cn).\n"
  },
  {
    "path": "install_tools.sh",
    "content": "# install dependencies\nsudo apt-get install libyaml-cpp-dev\nsudo apt-get install libarmadillo-dev\n# install pygame\nsudo apt-get install python-pip\npip install pygame\n# install osqp\ncd osqp\nmkdir build\ncd build\ncmake -G \"Unix Makefiles\" ..\ncmake --build .\nsudo cmake --build . --target install\n"
  },
  {
    "path": "key2joy.py",
    "content": "import os\nimport pygame\nfrom pygame.locals import *\nimport time\nimport sys\nimport rospy\nfrom geometry_msgs.msg import Twist\nfrom sensor_msgs.msg import Joy\n\n\ndef main():\n    # initialize pygame to get keyboard event\n    pygame.init()\n    window_size = Rect(0, 0, 438, 344)\n    screen = pygame.display.set_mode(window_size.size)\n    path = os.path.dirname(os.path.abspath(__file__))\n    print(path)\n    img = pygame.image.load(path+\"/figs/key2joy.png\")\n\n    # initialize ros publisher\n    twist_pub = rospy.Publisher('keyboard/twist', Twist, queue_size=10)\n    joy_pub = rospy.Publisher('/joy', Joy, queue_size=10)\n    rospy.init_node('key2joy')\n    rate = rospy.Rate(50)\n\n    # init joy message\n    joy_ = Joy()    \n    joy_.header.frame_id = 'world'\n    for i in range(8):\n      joy_.axes.append(0.0)\n    for i in range(11):\n      joy_.buttons.append(0)\n\n    while not rospy.is_shutdown():\n        rate.sleep()\n        screen.blit(img, (1,1))\n        pygame.display.flip()\n\n        for event in pygame.event.get():\n            # ---------------------- key dowm message ----------------------\n            \n            if event.type == KEYDOWN:\n                if event.key == pygame.K_w:\n                    # print 'up'\n                    joy_.axes[1] = 1.0\n                if event.key == pygame.K_s:\n                    # print 'down'\n                    joy_.axes[1] = -1.0\n                if event.key == pygame.K_a:\n                    # print 'left'\n                    joy_.axes[0] = -1.0\n                if event.key == pygame.K_d:\n                    # print 'right'\n                    joy_.axes[0] = 1.0\n                joy_pub.publish(joy_)\n\n            # when keyup, reset wind velocity\n            elif event.type == pygame.KEYUP:\n                # wind direction\n                if event.key == pygame.K_w:\n                    joy_.axes[1] = 0.0\n                if event.key == pygame.K_s:\n                    joy_.axes[1] = -0.0\n                if event.key == pygame.K_a:\n                    joy_.axes[0] = 0.0\n                if event.key == pygame.K_d:\n                    joy_.axes[0] = -0.0\n                joy_pub.publish(joy_)\n\n\nif __name__ == '__main__':\n    try:\n        main()\n    except rospy.ROSInterruptException:\n        pass\n"
  },
  {
    "path": "osqp/.bumpversion.cfg",
    "content": "[bumpversion]\ncurrent_version = 0.6.0\ncommit = True\ntag = True\nparse = (?P<major>\\d+)\\.(?P<minor>\\d+)\\.(?P<patch>\\d+)(\\.(?P<release>[a-z]+)(?P<dev>\\d+))?\nserialize = \n\t{major}.{minor}.{patch}.{release}{dev}\n\t{major}.{minor}.{patch}\n\n[bumpversion:part:release]\noptional_value = gamma\nvalues = \n\tdev\n\tgamma\n\n[bumpversion:part:dev]\n\n[bumpversion:file:README.md]\n\n[bumpversion:file:docs/conf.py]\n\n[bumpversion:file:docs/doxygen.conf]\n\n[bumpversion:file:docs/get_started/CC++.rst]\n\n[bumpversion:file:docs/get_started/matlab.rst]\n\n[bumpversion:file:include/constants.h]\n\n[bumpversion:file:.travis.yml]\n\n[bumpversion:file:appveyor.yml]\n\n"
  },
  {
    "path": "osqp/.editorconfig",
    "content": "# https://editorconfig.org\n\nroot = true\n\n[*.{c, h, cpp, hpp}]\ncharset = utf-8\nindent_style = space\nindent_size = 2\nend_of_line = lf\ntrim_trailing_whitespace = true\n\n\n# Python\n[*.py]\nindent_size = 4\n\n# Python\n[*.yml]\nindent_size = 2\n\n# CMake\n[CMakeLists.txt]\nindent_size = 4\n"
  },
  {
    "path": "osqp/.gitattributes",
    "content": "* text=auto\n*.c text\n*.h text\n*.cpp text"
  },
  {
    "path": "osqp/.gitmodules",
    "content": "[submodule \"lin_sys/direct/qdldl/qdldl_sources\"]\n\tpath = lin_sys/direct/qdldl/qdldl_sources\n\turl = https://github.com/oxfordcontrol/qdldl.git\n"
  },
  {
    "path": "osqp/.travis.yml",
    "content": "language: C\n\nmatrix:\n  include:\n    - os: linux\n      dist: xenial\n    - os: osx\n      # Specify version to avoid bug (https://github.com/travis-ci/travis-ci/issues/6522)\n      osx_image: xcode9.3\nsudo: required\nenv:\n  global:\n    - OSQP_VERSION=\"0.6.0\"\n    - DEPS_DIR=\"${HOME}/deps\"\n    # Bintray API key\n    - secure: \"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\"\n    - PYTHON_VERSION=\"3.6\"\n    # OSQP_DOCS_DEPLOY_EMAIL\n    - secure: \"EOnlXwP6GSEq4gm9lxTG6QHH4PUA2r2KOZULbdFXbn11Nr0z/ubjjzOGwGLCQdJcvz40G4Ks9Lty8RZqFvrIXXfMgli1jPR9N9jTPANog9abB+GOzjYn5IqBJMN4ZaR8DDpzHB4n9QE2w5I1ihTLIEnZ0Jxm9ckxYBlx9ndeRPEE0qqePkM1oP+GiT+WIidv9r9z3p6kKG+x4BF6d2SgzNGVL8v7yyPtVDGyUvLu/CGvY6pDgUqp0KImpOgjtrKTtFtYergBHTBPoLpxRLjw0RZRlaPquiitvwm4QxdYDmlyDahnoST330m0vvca2oCA76E3AlobUK8MDGxB+CiUfTM3e51mKCq3UO4o7g6BKT5ZF+0YpCFyfvvJZg6TupdfWdtlNG15rcgVgzOKAPrlaEe0qtCOUduPTHECCkOO/yUyEOU8+LEEvnESNdrJhfnPtkWBRJ607TjfKS6tLFcHjHobPtG4yB0ZBoOSWVjngIkJc/1vQNfrDXuBhWcF861GPnStv44ziNLl8+KL+9ekjPitVubInsxGNDtz0qVtMGigTK8xGxE0EjoUroIXc/DSfuFcPj4t7j56C7xFjxRz+nREGhq158XOiq0qLUtEdBPSvh9yxdn35n1L3D1kGM4oSHWhAslV55GZqorJfI/HBWelVhsVM+6HAoezQ9aJaQ4=\"\n    # OSQP_DOCS_DEPLOY_GH_TOKEN\n    - secure: \"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\"\n\ninstall:\n # Source to export variables\n - source ${TRAVIS_BUILD_DIR}/ci/travis/install.sh\n\nscript:\n - bash ${TRAVIS_BUILD_DIR}/ci/travis/script.sh\n\ndeploy:\n  # Deploy shared libraries to bintray\n  - provider: script\n    script: \"bash ${TRAVIS_BUILD_DIR}/ci/travis/deploy.sh\"\n    on:\n      tags: true\n    skip_cleanup: true # to upload artifacts created during the build\n  # Deploy docs\n  - provider: script\n    script: \"bash ${TRAVIS_BUILD_DIR}/ci/travis/docs.sh\"\n    on:\n      branch: master\n"
  },
  {
    "path": "osqp/.valgrind-suppress.supp",
    "content": "{\n   ignore_get_max_threads_intel\n   Memcheck:Param\n   sched_setaffinity(mask)\n   fun:syscall\n   fun:__kmp_affinity_determine_capable\n   fun:_Z20__kmp_env_initializePKc\n   fun:__kmp_middle_initialize\n   fun:omp_get_num_procs@@VERSION\n   fun:mkl_serv_domain_get_max_threads\n   obj:*\n}\n\n{\n   ignore mkl_get_max_threads\n   Memcheck:Cond\n   fun:mkl_serv_domain_get_max_threads\n   obj:*\n}\n\n{\n   ignore_libiomp_intel\n   Memcheck:Cond\n   fun:__intel_sse2_strrchr\n   fun:*_INTERNAL_45_______src_thirdparty_tbb_omp_dynamic_link_cpp_*\n   fun:__sti__$E\n   obj:*libiomp*.so\n}\n\n\n{\n   mkl_malloc\n   Memcheck:Leak\n   match-leak-kinds: definite\n   fun:malloc\n   fun:_dl_signal_error\n   fun:_dl_open\n   fun:dlopen_doit\n   fun:_dl_catch_error\n   fun:_dlerror_run\n   fun:dlopen@@GLIBC_2.2.5\n   fun:mkl_serv_inspector_suppress\n   fun:mkl_serv_lock\n   fun:mkl_serv_core_register_cleanup\n   fun:__sti__$E\n   fun:call_init.part.0\n}\n\n{\n   another_intel\n   Memcheck:Addr16\n   fun:__intel_sse2_strrchr\n   fun:_ZN67_INTERNAL_45_______src_thirdparty_tbb_omp_dynamic_link_cpp_d77f5ef55__kmp12init_dl_dataEv\n   fun:__sti__$E\n   obj:*libiomp5.so\n   obj:*libiomp5.so\n}\n"
  },
  {
    "path": "osqp/CHANGELOG.md",
    "content": "Version 0.6.0 (30 August 2019)\n------------------------------\n* Added meaningful return values to internal functions. Changed syntax of `osqp_setup` function. It now returns an exitflag.\n* `osqp_setup` function requires `P` to be upper triangular. It returns a nonzero exitflag otherwise.\n* Custom memory allocators via cmake and the configure file.\n* Changed interfaces to linsys solver functions. The solve function now computes `(x_tilde,z_tilde)` instead of `(x_tilde,nu)`. This allows to implement custom linear system solvers (also indirect).\n* Added `solve` function in Python interface that performs `setup` `solve` and `cleanup` for you directly and disables GIL.\n* Improved code generation folder structure.\n\n\nVersion 0.5.0 (10 December 2018)\n----------------\n* Added `update_time` to the info structure.\n* Fixed [#101](https://github.com/oxfordcontrol/osqp/issues/101).\n\n\nVersion 0.4.1 (25 September 2018)\n---------------------------------\n* Updated QDLDL to version 0.1.3.\n* Added check for nonconvex cost function (non-positive semidefinite `P`) after factorization is performed.\n* Added complete sources distribution on bintray.com (including QDLDL).\n\n\nVersion 0.4.0 (23 July 2018)\n----------------------------\n* Added check for nonconvex cost function (non-positive semidefinite `P`).\n* Removed SuiteSparse LDL in favor of [QDLDL](https://github.com/oxfordcontrol/qdldl).\n* Static library `libosqpstatic` now renamed as `libosqp`.\n\n\nVersion 0.3.1 (10 June 2018)\n----------------------------\n* Fixed [#62](https://github.com/oxfordcontrol/osqp/issues/62).\n* Moved interfaces to separate repositories\n* Fixed [#54](https://github.com/oxfordcontrol/osqp/issues/54).\n* Changes to support Matlab 2018a\n* Added support for new interface in R\n\nVersion 0.3.0 (5 March 2018)\n----------------------------\n* Added `time_limit` option\n* Added CUTEst interface\n* Fixed bug in upper triangular `P` extraction. Now the solver can accept both complete `P` matrix or just the upper triangular part.\n* Fixed [#33](https://github.com/oxfordcontrol/osqp/issues/33)\n* Fixed [#34](https://github.com/oxfordcontrol/osqp/issues/34)\n* Allow `eps_rel=0` [#40](https://github.com/oxfordcontrol/osqp/issues/40)\n* Fixed bug when calling `osqp_solve` or `osqp_cleanup` after failed linear system initialization\n* Add \"install\" CMake target and installation of CMake configuration files\n* Fixed potential name conflict with SCS [47](https://github.com/oxfordcontrol/osqp/issues/47)\n* Changed `set_default_settings` to `osqp_set_default_settings` and brought function to main API header `osqp.h`\n* Fixed [#49](https://github.com/oxfordcontrol/osqp/issues/49)\n\n\nVersion 0.2.1 (25 November 2017)\n---------------------------------\n* Fixed problem with code generation and pypi `data_files` (everything now in [MANIFEST.in](https://github.com/pypa/sampleproject/issues/30))\n\n\nVersion 0.2.0 (23 November 2017)\n---------------------------------\n*   Added adaptive rho -> Much more reliable convergence!\n*   Simplified several settings\n    *  \"early_terminate\" and \"early_terminate_interval\" -> \"check_termination\"\n    *  \"scaling_iter\" removed and put inside \"scaling\" parameter\n*   Julia interface [OSQP.jl](https://github.com/oxfordcontrol/OSQP.jl)\n*   Shared libraries available on bintray.com\n*   Added inaccurate return statuses\n*   Added new object-oriented structure for linear system solvers\n*   Added MKL Pardiso interface using shared dynamic library loader\n*   Added diagonal rho vector with different values for equality/inequality constraints (interface still have scalar rho)\n*   Return certificates of infeasibility in results structure\n*   Now code generation produces a static library\n\n\nVersion 0.1.3 (21 September 2017)\n---------------------------------\n* Fixed sources distribution on Python\n\n\nVersion 0.1.2 (20 July 2017)\n------------------------------\n*   Added option to terminate with scaled or unscaled residual\n*   Now Matlab interface does support logical entries for the settings\n*   Fixed bug in index ordering of sparse matrices of Python interface\n*   Changed 2-norms to inf-norms\n*   Fixed code generation bug when scaling is disabled [#7](https://github.com/oxfordcontrol/osqp/issues/7)\n*   Removed warnings in code-generation for standard <= C99\n*   Fixed MATLAB 2015b compatibility [#6](https://github.com/oxfordcontrol/osqp/issues/6)\n\n\nVersion 0.1.1 (11 April 2017)\n-----------------------------\n*   Fixed crashes during polishing when factorization fails\n*   Added package to Pypi\n*   Fixed relative paths Matlab\n\n\nVersion 0.1.0 (10 April 2017)\n-----------------------------\n*   Linux, Mac and Windows\n*   Interface to Python 2.7, 3.5+\n*   Interface to Matlab 2015b+\n*   Embedded code generation\n"
  },
  {
    "path": "osqp/CMakeLists.txt",
    "content": "# Minimum version required\ncmake_minimum_required (VERSION 3.2)\n\n# Project name\nproject (osqp)\n\n# Export compile commands\nset(CMAKE_EXPORT_COMPILE_COMMANDS ON)\n\n# Set the output folder where your program will be created\nset(EXECUTABLE_OUTPUT_PATH ${PROJECT_BINARY_DIR}/out)\nset(LIBRARY_OUTPUT_PATH ${PROJECT_BINARY_DIR}/out)\n\n# Some non-standard CMake modules\nLIST(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/configure/cmake)\nINCLUDE(FindPythonModule)\nINCLUDE(Utils)\n# include(FindMKL)  # Find MKL module\n\n\n# Detect operating system\n# ----------------------------------------------\nmessage(STATUS \"We are on a ${CMAKE_SYSTEM_NAME} system\")\nif(${CMAKE_SYSTEM_NAME} STREQUAL \"Linux\")\n    set(IS_LINUX ON)\nelseif(${CMAKE_SYSTEM_NAME} STREQUAL \"Darwin\")\n    set(IS_MAC ON)\nelseif(${CMAKE_SYSTEM_NAME} STREQUAL \"Windows\")\n    set(IS_WINDOWS ON)\nendif()\n\n\n\n# Set options\n# ----------------------------------------------\n\n# Are unittests generated?\noption (UNITTESTS \"Enable unittests generation\" OFF)\n\n# Is the code generated for embedded platforms?\n#   1 :   Yes. Matrix update not allowed.\n#   2 :   Yes. Matrix update allowed.\nif (NOT DEFINED EMBEDDED)\n    message(STATUS \"Embedded is OFF\")\nelse()\n    message(STATUS \"Embedded is ${EMBEDDED}\")\nendif()\n\n# Is printing enabled?\noption (PRINTING \"Enable solver printing\" OFF)\nif (DEFINED EMBEDDED)\n    message(STATUS \"Disabling printing for embedded\")\n    set(PRINTING OFF)\nendif()\nset(PRINTING OFF)\nmessage(STATUS \"Printing is ${PRINTING}\")\n\n\n# Is profiling enabled?\noption (PROFILING \"Enable solver profiling (timing)\" ON)\nif (DEFINED EMBEDDED)\n    message(STATUS \"Disabling profiling for embedded\")\n    set(PROFILING OFF)\nendif()\nmessage(STATUS \"Profiling is ${PROFILING}\")\n\n# Is user interrupt enabled?\noption (CTRLC \"Enable user interrupt (Ctrl-C)\" ON)\nif (DEFINED EMBEDDED)\n    message(STATUS \"Disabling user interrupt for embedded\")\n    set(CTRLC OFF)\nendif()\nmessage(STATUS \"User interrupt is ${CTRLC}\")\n\n# Use floats instead of integers\noption (DFLOAT \"Use float numbers instead of doubles\" OFF)\nmessage(STATUS \"Floats are ${DFLOAT}\")\n\n# Use long integers for indexing\noption (DLONG \"Use long integers (64bit) for indexing\" OFF)\nif (NOT (CMAKE_SIZEOF_VOID_P EQUAL 8))\n\tmessage(STATUS \"Disabling long integers (64bit) on 32bit machine\")\n\tset(DLONG OFF)\nendif()\nmessage(STATUS \"Long integers (64bit) are ${DLONG}\")\n\n\noption (DEBUG \"Debug mode\" OFF)\nif (CMAKE_BUILD_TYPE STREQUAL \"Debug\")\n\tset (DEBUG ON)\n\tmessage(STATUS \"Debug mode is ${DEBUG}\")\nendif()\n\n# Add code coverage\noption (COVERAGE \"Perform code coverage\" OFF)\nmessage(STATUS \"Code coverage is ${COVERAGE}\")\n\n\n# Memory allocators\n# ----------------------------------------------\n\n#Report on custom user header options.  This is intended to allow\n#users to provide definitions of their own memory functions\n# The header should define the functions as follows\n#\n# define c_malloc mymalloc\n# define c_calloc mycalloc\n# define c_realloc myrealloc\n# define c_free myfree\n\nif(OSQP_CUSTOM_MEMORY)\n\tmessage(STATUS \"User custom memory management header: ${OSQP_CUSTOM_MEMORY}\")\nendif()\n\n\n\n# Linear solvers dependencies\n# ---------------------------------------------\noption (ENABLE_MKL_PARDISO \"Enable MKL Pardiso solver\" ON)\nif (DFLOAT)\n\tmessage(STATUS \"Disabling MKL Pardiso Solver with floats\")\n\tset(ENABLE_MKL_PARDISO OFF)\nelseif(DEFINED EMBEDDED)\n\tmessage(STATUS \"Disabling MKL Pardiso Solver for embedded\")\n\tset(ENABLE_MKL_PARDISO OFF)\nendif()\nmessage(STATUS \"MKL Pardiso: ${ENABLE_MKL_PARDISO}\")\n\n\n# Generate header file with the global options\n# ---------------------------------------------\nconfigure_file(${CMAKE_CURRENT_SOURCE_DIR}/configure/osqp_configure.h.in\n               ${CMAKE_CURRENT_SOURCE_DIR}/include/osqp_configure.h\n               NEWLINE_STYLE LF)\n\n# Set Compiler flags\n# ----------------------------------------------\nset(CMAKE_POSITION_INDEPENDENT_CODE ON)  # -fPIC\n\n\nif (NOT MSVC)\n\n    if (COVERAGE)\n        set(CMAKE_C_FLAGS \"${CMAKE_C_FLAGS} --coverage\")\n\tif(FORTRAN)\n\t\tset(CMAKE_FORTRAN_FLAGS \"${CMAKE_FORTRAN_FLAGS} --coverage\")\n\tendif(FORTRAN)\n    endif()\n\n    if (DEBUG)\n        set(CMAKE_C_FLAGS \"${CMAKE_C_FLAGS} -O0 -g\")\n    else()\n        set(CMAKE_C_FLAGS \"${CMAKE_C_FLAGS} -O3\")\n    endif()\n\n    set(CMAKE_C_STANDARD_LIBRARIES \"${CMAKE_C_STANDARD_LIBRARIES} -lm\")      # Include math\n    # Include real time library in linux\n    if(${CMAKE_SYSTEM_NAME} STREQUAL \"Linux\")\n        set(CMAKE_C_STANDARD_LIBRARIES \"${CMAKE_C_STANDARD_LIBRARIES} -lrt -ldl\")\n    endif()\nendif (NOT MSVC)\n\n# Set sources and includes\n# ----------------------------------------------\nadd_subdirectory (src)\nadd_subdirectory (include)\n\n\n# if we are building the Python interface, let's look for Python\n# and set some options\n# -----------------------------------------------------------------\nif (PYTHON)\n\n    # Python include directories need to be passed by the python compilation process\n    if (NOT PYTHON_INCLUDE_DIRS)\n            message( FATAL_ERROR \"You need Python include directories to build the Python interface\" )\n    endif (NOT PYTHON_INCLUDE_DIRS)\n\n    # Include directories for Python headers\n    include_directories(${PYTHON_INCLUDE_DIRS})\n\n    # Pass PYTHON flag to C compiler\n    add_definitions(-DPYTHON)\n\n    if (UNITTESTS)\n        # Disable unittests\n        message(STATUS \"Disabling UNITTESTS because we are building Python interface\")\n        set(UNITTESTS OFF)\n    endif (UNITTESTS)\n\nendif (PYTHON)\n\n\n# if we are building the Matlab interface, let's look for Matlab\n# and set some options\n# -----------------------------------------------------------------\nif (MATLAB)\n\n    find_package(Matlab)\n\n    if (NOT Matlab_FOUND)\n        message( FATAL_ERROR \"You need Matlab libraries to build the Matlab interface\" )\n    endif (NOT Matlab_FOUND)\n\n    # Include directories for Matlab headers\n    include_directories(${Matlab_INCLUDE_DIRS})\n\n    message(STATUS \"Matlab root is \" ${Matlab_ROOT_DIR})\n\n    # Pass MATLAB flag to C compiler\n    add_definitions(-DMATLAB)\n\n    # Insist on the pre 2018 complex data API\n    # so that mxGetPr will work correctly\n    add_definitions(-DMATLAB_MEXSRC_RELEASE=R2017b)\n\n    message(STATUS \"Using Matlab pre-2018a API for mxGetPr compatibility\")\n\n    if (UNITTESTS)\n        # Disable unittests\n        message(STATUS \"Disabling UNITTESTS because we are building Matlab interface\")\n        set(UNITTESTS OFF)\n    endif (UNITTESTS)\n\nendif (MATLAB)\n\n# if we are building the R interface, let's look for R\n# and set some options\n# -----------------------------------------------------------------\nif (R_LANG)\n\n    message(STATUS \"We are building the R interface\")\n\n    # Look for R libraries\n    find_package(R)\n\n    if (NOT R_FOUND)\n            message( FATAL_ERROR \"You need R libraries to build the R interface\" )\n    endif (NOT R_FOUND)\n\n    message(STATUS \"R exec is: \" ${R_EXEC})\n    message(STATUS \"R root dir is: \" ${R_ROOT_DIR})\n    message(STATUS \"R includes are in: \" ${R_INCLUDE_DIRS})\n\n    # Include directories for R headers\n    include_directories(${R_INCLUDE_DIRS})\n\n    # Pass R_LANG flag to C compiler\n    add_definitions(-DR_LANG)\n\n    if (UNITTESTS)\n        # Disable unittests\n        message(STATUS \"Disabling UNITTESTS because we are building the R interface\")\n        set(UNITTESTS OFF)\n    endif (UNITTESTS)\n\nendif (R_LANG)\n\n\n# Create Static Library\n# ----------------------------------------------\n\n# Add linear system solvers cumulative library\nadd_subdirectory(lin_sys)\n\n# Static library\nadd_library (osqpstatic STATIC ${osqp_src} ${osqp_headers} ${linsys_solvers})\n# Give same name to static library output\nset_target_properties(osqpstatic PROPERTIES OUTPUT_NAME osqp)\n\n# Include directories for linear system solvers\ntarget_include_directories(osqpstatic PRIVATE ${linsys_solvers_includes})\n\n# Declare include directories for the cmake exported target\ntarget_include_directories(osqpstatic\n                           PUBLIC \"$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>\"\n                                  \"$<INSTALL_INTERFACE:$<INSTALL_PREFIX>/${CMAKE_INSTALL_INCLUDEDIR}/osqp>\")\n\n# Install Static Library\n# ----------------------------------------------\n\ninclude(GNUInstallDirs)\n\ninstall(TARGETS osqpstatic\n        EXPORT  ${PROJECT_NAME}\n        ARCHIVE       DESTINATION \"${CMAKE_INSTALL_LIBDIR}\"\n        LIBRARY       DESTINATION \"${CMAKE_INSTALL_LIBDIR}\"\n        RUNTIME       DESTINATION \"${CMAKE_INSTALL_BINDIR}\")\n\n\n# Install Headers\n# ----------------------------------------------\n\ninstall(FILES ${osqp_headers} DESTINATION \"${CMAKE_INSTALL_INCLUDEDIR}/osqp\")\n\n\n\nif (MATLAB)\ntarget_link_libraries (osqpstatic ${Matlab_LIBRARIES})\nendif (MATLAB)\n\n# If we are building Python/Matlab/R interface:\n#   - do not build shared library\n#   - do not build demo\nif (NOT PYTHON AND NOT MATLAB AND NOT R_LANG AND NOT EMBEDDED)\n    # Create osqp shared library\n    # NB: Add all the linear system solvers here\n    add_library (osqp SHARED ${osqp_src} ${osqp_headers} ${linsys_solvers})\n\n    # Include directories for linear system solvers\n    target_include_directories(osqp PRIVATE ${linsys_solvers_includes})\n\n    # Declare include directories for the cmake exported target\n    target_include_directories(osqp\n                               PUBLIC \"$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>\"\n                                      \"$<INSTALL_INTERFACE:$<INSTALL_PREFIX>/${CMAKE_INSTALL_INCLUDEDIR}/osqp>\")\n\n    # Install osqp shared library\n    install(TARGETS osqp\n            EXPORT  ${PROJECT_NAME}\n            LIBRARY       DESTINATION \"${CMAKE_INSTALL_LIBDIR}\"\n            ARCHIVE       DESTINATION \"${CMAKE_INSTALL_LIBDIR}\"\n            RUNTIME       DESTINATION \"${CMAKE_INSTALL_BINDIR}\")\n\n    # Create demo executable (linked to static library)\n    add_executable (osqp_demo ${PROJECT_SOURCE_DIR}/examples/osqp_demo.c)\n    target_link_libraries (osqp_demo osqpstatic)\n\nendif (NOT PYTHON AND NOT MATLAB AND NOT R_LANG AND NOT EMBEDDED)\n\n# Create CMake packages for the build directory\n# ----------------------------------------------\ninclude(CMakePackageConfigHelpers)\n\nexport(EXPORT ${PROJECT_NAME}\n  FILE \"${CMAKE_CURRENT_BINARY_DIR}/osqp-targets.cmake\"\n  NAMESPACE osqp::)\n\nif(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/osqp-config.cmake)\n  file(WRITE ${CMAKE_CURRENT_BINARY_DIR}/osqp-config.cmake \"include(\\\"\\${CMAKE_CURRENT_LIST_DIR}/osqp-targets.cmake\\\")\\n\")\nendif()\n\n\n# Create CMake packages for the install directory\n# ----------------------------------------------\n\nset(ConfigPackageLocation lib/cmake/osqp)\n\ninstall(EXPORT ${PROJECT_NAME}\n        FILE osqp-targets.cmake\n        NAMESPACE osqp::\n        DESTINATION ${ConfigPackageLocation})\n\ninstall(FILES ${CMAKE_CURRENT_BINARY_DIR}/osqp-config.cmake\n        DESTINATION ${ConfigPackageLocation})\n\n\n\n# Add uninstall command\n# ----------------------------------------------\nif(NOT TARGET uninstall)\n    configure_file(\n        \"${CMAKE_CURRENT_SOURCE_DIR}/configure/cmake/cmake_uninstall.cmake.in\"\n        \"${CMAKE_CURRENT_BINARY_DIR}/cmake_uninstall.cmake\"\n        IMMEDIATE @ONLY)\n\n    add_custom_target(uninstall\n        COMMAND ${CMAKE_COMMAND} -P ${CMAKE_CURRENT_BINARY_DIR}/cmake_uninstall.cmake)\nendif()\n\n\n\n# Add testing\n# ----------------------------------------------\n# Add custom command to generate tests\nif (UNITTESTS)\n    find_package(PythonInterp)\n    if(NOT PYTHONINTERP_FOUND)\n        message( FATAL_ERROR \"You need python installed to generate unittests. If you do not want to compile the unittests pass -DUNITTESTS=OFF to cmake.\" )\n    endif()\n\n    INCLUDE(FindPythonModule)\n    find_python_module(numpy)\n    IF(NOT NUMPY_FOUND)\n        message( FATAL_ERROR \"You need numpy python module installed to generate unittests. If you do not want to compile the unittests pass -DUNITTESTS=OFF to cmake.\" )\n    ENDIF()\n\n    find_python_module(scipy)\n    # Check scipy version for sparse.random functionalities\n    IF((NOT SCIPY_FOUND) OR (SCIPY_VERSION VERSION_LESS 0.17.0))\n        message( FATAL_ERROR \"You need scipy python module installed to generate unittests. If you do not want to compile the unittests pass -DUNITTESTS=OFF to cmake.\" )\n    ENDIF()\n\n    find_python_module(__future__)\n    IF(NOT __FUTURE___FOUND)\n        message( FATAL_ERROR \"You need future python module installed to generate unittests. If you do not want to compile the unittests pass -DUNITTESTS=OFF to cmake.\" )\n    ENDIF()\n\n    # Add test_headers and codegen_test_headers\n    add_subdirectory(tests)\n\n    # Generating tests.stamp so that the test data are not always generated\n    # set(data_timestamp ${PROJECT_SOURCE_DIR}/tests/tests_data.stamp)\n    add_custom_command(\n        WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n        COMMAND ${PYTHON_EXECUTABLE} generate_tests_data.py\n        DEPENDS ${PROJECT_SOURCE_DIR}/tests/generate_tests_data.py\n        OUTPUT ${codegen_test_headers}\n        COMMENT \"Generating unittests data files using Python\"\n    )\n\n    # Direct linear solver testing\n    include_directories(tests)\n    add_executable(osqp_tester\n                ${PROJECT_SOURCE_DIR}/tests/osqp_tester.c\n                ${PROJECT_SOURCE_DIR}/tests/osqp_tester.h\n\t\t${PROJECT_SOURCE_DIR}/tests/minunit.h\n                ${test_headers}\n                ${codegen_test_headers})\n    target_link_libraries (osqp_tester osqpstatic)\n\n    # Add custom memory target\n    add_executable(osqp_tester_custom_memory\n                   EXCLUDE_FROM_ALL\n                   ${PROJECT_SOURCE_DIR}/tests/osqp_tester.c\n                   ${PROJECT_SOURCE_DIR}/tests/osqp_tester.h\n\t           ${PROJECT_SOURCE_DIR}/tests/minunit.h\n                   ${test_headers}\n                   ${codegen_test_headers}\n\t\t   ${PROJECT_SOURCE_DIR}/tests/custom_memory/custom_memory.c\n\t\t   ${PROJECT_SOURCE_DIR}/tests/custom_memory/custom_memory.h\n\t\t)\n    target_link_libraries (osqp_tester_custom_memory osqpstatic)\n\n    # Add testing\n    include(CTest)\n    enable_testing()\n    add_test(NAME tester COMMAND $<TARGET_FILE:osqp_tester>)\nendif()\n"
  },
  {
    "path": "osqp/LICENSE",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. 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The text should be enclosed in the appropriate\n      comment syntax for the file format. We also recommend that a\n      file or class name and description of purpose be included on the\n      same \"printed page\" as the copyright notice for easier\n      identification within third-party archives.\n\n   Copyright 2019 Bartolomeo Stellato, Goran Banjac, Paul Goulart, Stephen Boyd\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.\n   You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an \"AS IS\" BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License.\n"
  },
  {
    "path": "osqp/NOTICE",
    "content": "OSQP\nCopyright (c) 2019 Bartolomeo Stellato, Goran Banjac, Paul Goulart, Stephen Boyd\n\nThis product includes software developed at Stanford University and at the University of Oxford.\n\n\nThe following external modules are included in this software library\n\nQDLDL\nCopyright (c) 2018, Paul Goulart, Bartolomeo Stellato, Goran Banjac.\n\nAMD\nCopyright (c) 1996-2015, Timothy A. Davis, Patrick R. Amestoy, and Iain S. Duff.\n\n"
  },
  {
    "path": "osqp/README.md",
    "content": "# The Operator Splitting QP Solver\n\n[![Build status of the master branch on Linux/OSX](https://img.shields.io/travis/oxfordcontrol/osqp/master.svg?label=Linux%20%2F%20OSX%20build)](https://travis-ci.org/oxfordcontrol/osqp)\n[![Build status of the master branch on Windows](https://img.shields.io/appveyor/ci/bstellato/osqp/master.svg?label=Windows%20build)](https://ci.appveyor.com/project/bstellato/osqp/branch/master)\n[![Code coverage](https://coveralls.io/repos/github/oxfordcontrol/osqp/badge.svg?branch=master)](https://coveralls.io/github/oxfordcontrol/osqp?branch=master)\n![License](https://img.shields.io/badge/License-Apache%202.0-brightgreen.svg)\n\n\n![PyPI - downloads](https://img.shields.io/pypi/dm/osqp.svg?label=Pypi%20downloads)\n![Conda - downloads](https://img.shields.io/conda/dn/conda-forge/osqp.svg?label=Conda%20downloads)\n\n[**Join our forum on Discourse**](https://osqp.discourse.group) for any questions related to the solver!\n\n**The documentation** is available at [**osqp.org**](https://osqp.org/)\n\nThe OSQP (Operator Splitting Quadratic Program) solver is a numerical optimization package for solving problems in the form\n```\nminimize        0.5 x' P x + q' x\n\nsubject to      l <= A x <= u\n```\n\nwhere `x in R^n` is the optimization variable. The objective function is defined by a positive semidefinite matrix `P in S^n_+` and vector `q in R^n`. The linear constraints are defined by matrix `A in R^{m x n}` and vectors `l` and `u` so that `l_i in R U {-inf}` and `u_i in R U {+inf}` for all `i in 1,...,m`.\n\n\nThe latest version is `0.6.0`.\n\n## Citing OSQP\n\nIf you are using OSQP for your work, we encourage you to\n\n* [Cite the related papers](https://osqp.org/citing/),\n* Put a star on this repository.\n\n**We are looking forward to hearing your success stories with OSQP!** Please [share them with us](mailto:bartolomeo.stellato@gmail.com).\n\n\n## Bug reports and support\n\nPlease report any issues via the [Github issue tracker](https://github.com/oxfordcontrol/osqp/issues). All types of issues are welcome including bug reports, documentation typos, feature requests and so on.\n\n\n## Numerical benchmarks\nNumerical benchmarks against other solvers are available [here](https://github.com/oxfordcontrol/osqp_benchmarks).\n\n"
  },
  {
    "path": "osqp/ROADMAP.md",
    "content": "## TODO\n-   [ ] Add GPU solver\n-   [ ] Add indirect solver\n\n"
  },
  {
    "path": "osqp/appveyor.yml",
    "content": "# Not a .NET project. We build it in the install script\nbuild: false\n\nplatform:\n  - x64\n  - x86\n\nenvironment:\n  BINTRAY_API_KEY:\n    secure: ng7NUNQat2LczU5XjKKyDLITkZbZsJeAdPGDjuTdQDHDiaL03JJF6Usab+miNVsO\n  MINICONDA: C:\\Miniconda\n  CMAKE_PROJECT: MinGW Makefiles\n  PYTHON_VERSION: 3.7\n  OSQP_VERSION: 0.6.0\n\ninit:\n  # Uncomment for remote desktop debug\n  # - ps: iex ((new-object net.webclient).DownloadString('https://raw.githubusercontent.com/appveyor/ci/master/scripts/enable-rdp.ps1'))\n  - ECHO %PYTHON_VERSION% %MINICONDA%\n\ninstall:\n  - \"%APPVEYOR_BUILD_FOLDER%\\\\ci\\\\appveyor\\\\install.cmd\"\n\ntest_script:\n  - \"%APPVEYOR_BUILD_FOLDER%\\\\ci\\\\appveyor\\\\script.cmd\"\n\ndeploy_script:\n  - \"%APPVEYOR_BUILD_FOLDER%\\\\ci\\\\appveyor\\\\deploy.cmd\"\n\non_finish:\n  # Uncomment for remote desktop debug\n  # - ps: $blockRdp = $true; iex ((new-object net.webclient).DownloadString('https://raw.githubusercontent.com/appveyor/ci/master/scripts/enable-rdp.ps1'))\n"
  },
  {
    "path": "osqp/ci/appveyor/deploy.cmd",
    "content": "@echo on\n\nREM Needed to enable to define OSQP_BIN within the file\n@setlocal enabledelayedexpansion\n\nIF \"%APPVEYOR_REPO_TAG%\" == \"true\" (\n\n    IF NOT \"%OSQP_VERSION%\"==\"%OSQP_VERSION:dev=%\" (\n    rem We are using a development version\n    set OSQP_PACKAGE_NAME=\"OSQP-dev\"\n    ) ELSE (\n    rem We are using standard version\n    set OSQP_PACKAGE_NAME=\"OSQP\"\n    )\n    \n    REM Build C libraries\n    cd %APPVEYOR_BUILD_FOLDER%\n    del /F /Q build\n    mkdir build\n    cd build\n    cmake -G \"%CMAKE_PROJECT%\" ..\n    cmake --build .\n\n    REM Go to output folder\n    cd %APPVEYOR_BUILD_FOLDER%\\build\\out\n\n    IF \"%PLATFORM%\" == \"x86\" (\n    set OSQP_BIN=\"osqp-!OSQP_VERSION!-windows32\"\n    ) ELSE (\n    set OSQP_BIN=\"osqp-!OSQP_VERSION!-windows64\"\n    )\n    REM Create directories\n    REM NB. We force expansion of the variable at execution time!\n    mkdir !OSQP_BIN!\n    mkdir !OSQP_BIN!\\lib\n    mkdir !OSQP_BIN!\\include\n\n    REM Copy License\n    xcopy ..\\..\\LICENSE !OSQP_BIN!\n\n    REM Copy includes\n    xcopy ..\\..\\include\\*.h !OSQP_BIN!\\include\n\n    REM Copy static library\n    xcopy libosqp.a !OSQP_BIN!\\lib\n\n    REM Copy shared library\n    xcopy libosqp.dll !OSQP_BIN!\\lib\n\n    REM Compress package\n    7z a -ttar !OSQP_BIN!.tar !OSQP_BIN!\n    7z a -tgzip !OSQP_BIN!.tar.gz !OSQP_BIN!.tar\n\n    REM Deploy to Bintray\n    curl -T !OSQP_BIN!.tar.gz -ubstellato:!BINTRAY_API_KEY! -H X-Bintray-Package:!OSQP_PACKAGE_NAME! -H X-Bintray-Version:!OSQP_VERSION! -H \"X-Bintray-Override: 1\" https://api.bintray.com/content/bstellato/generic/!OSQP_PACKAGE_NAME!/!OSQP_VERSION!/\n    if errorlevel 1 exit /b 1\n\n    REM Publish\n    curl -X POST -ubstellato:!BINTRAY_API_KEY! https://api.bintray.com/content/bstellato/generic/!OSQP_PACKAGE_NAME!/!OSQP_VERSION!/publish\n    if errorlevel 1 exit /b 1\n\nrem End of IF for appveyor tag\n)\n\n@echo off\n"
  },
  {
    "path": "osqp/ci/appveyor/install.cmd",
    "content": "@echo on\n\n:: Make sure all the submodules are updated correctly\ncd %APPVEYOR_BUILD_FOLDER%\ngit submodule update --init --recursive\n\n:: Remove entry with sh.exe from PATH to fix error with MinGW toolchain\n:: (For MinGW make to work correctly sh.exe must NOT be in your path)\n:: http://stackoverflow.com/a/3870338/2288008\nset PATH=%PATH:C:\\Program Files\\Git\\usr\\bin;=%\n\n\nIF \"%PLATFORM%\"==\"x86\" (\n    set MINGW_PATH=C:\\mingw-w64\\i686-6.3.0-posix-dwarf-rt_v5-rev1\\mingw32\\bin\n) ELSE (\n    set MINGW_PATH=C:\\mingw-w64\\x86_64-6.3.0-posix-seh-rt_v5-rev1\\mingw64\\bin\n)\nset PATH=%MINGW_PATH%;%PATH%\n\n\n:: Activate test environment anaconda\nIF \"%PLATFORM%\"==\"x86\" (\n\tset MINICONDA_PATH=%MINICONDA%\n) ELSE (\n\tset MINICONDA_PATH=%MINICONDA%-%PLATFORM%\n)\nset PATH=%MINICONDA_PATH%;%MINICONDA_PATH%\\\\Scripts;%PATH%\n\n\nconda config --set always_yes yes --set changeps1 no\nREM This, together with next line, disables conda auto update (fixes problem with tqdm)\nconda config --set auto_update_conda false\nREM conda update -q conda\nconda info -a\nconda create -q -n test-environment python=%PYTHON_VERSION% numpy scipy future\nif errorlevel 1 exit /b 1\n:: NB: Need to run with call otherwise the script hangs\ncall activate test-environment\n\n\n:: Set environment for build if 64bit\n:: NB: Needed during conda build!\nIF \"%PLATFORM%\"==\"x64\" (\ncall \"C:\\Program Files\\Microsoft SDKs\\Windows\\v7.1\\Bin\\SetEnv.cmd\" /x64\n) ELSE (\nREM Set environment for 32bit\ncall \"C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\vcvarsall.bat\" x86\n)\n\n@echo off\n"
  },
  {
    "path": "osqp/ci/appveyor/script.cmd",
    "content": "@echo on\n\n\n:: Perform C Tests with standard configuration\n:: -----------------------------------------------------\ncd %APPVEYOR_BUILD_FOLDER%\nmkdir build\ncd build\ncmake -G \"%CMAKE_PROJECT%\" -DUNITTESTS=ON ..\ncmake --build .\n%APPVEYOR_BUILD_FOLDER%\\build\\out\\osqp_tester.exe\nif errorlevel 1 exit /b 1\n\n:: Perform C Tests with floats\n:: -----------------------------------------------------\ncd %APPVEYOR_BUILD_FOLDER%\nrmdir /s /q build\nmkdir build\ncd build\ncmake -G \"%CMAKE_PROJECT%\" -DDFLOAT=ON -DUNITTESTS=ON ..\ncmake --build .\n%APPVEYOR_BUILD_FOLDER%\\build\\out\\osqp_tester.exe\nif errorlevel 1 exit /b 1\n\n:: Perform C Tests with short integers\n:: -----------------------------------------------------\ncd %APPVEYOR_BUILD_FOLDER%\nrmdir /s /q build\nmkdir build\ncd build\ncmake -G \"%CMAKE_PROJECT%\" -DDLONG=OFF -DUNITTESTS=ON ..\ncmake --build .\n%APPVEYOR_BUILD_FOLDER%\\build\\out\\osqp_tester.exe\nif errorlevel 1 exit /b 1\n\n:: Perform C Tests without printing\n:: -----------------------------------------------------\ncd %APPVEYOR_BUILD_FOLDER%\nrmdir /s /q build\nmkdir build\ncd build\ncmake -G \"%CMAKE_PROJECT%\" -DPRINTING=OFF -DUNITTESTS=ON ..\ncmake --build .\n%APPVEYOR_BUILD_FOLDER%\\build\\out\\osqp_tester.exe\nif errorlevel 1 exit /b 1\n\ncd %APPVEYOR_BUILD_FOLDER%\n@echo off\n"
  },
  {
    "path": "osqp/ci/travis/deploy.sh",
    "content": "#!/bin/bash\nset -e\necho \"Creating Bintray shared library package...\"\n\nif [[ ${OSQP_VERSION} == *\"dev\"* ]]; then\n    # Using dev version\n    OSQP_PACKAGE_NAME=\"OSQP-dev\";\nelse\n    OSQP_PACKAGE_NAME=\"OSQP\";\nfi\n\n# Compile OSQP\ncd ${TRAVIS_BUILD_DIR}\nrm -rf build\nmkdir build\ncd build\ncmake -G \"Unix Makefiles\" ..\nmake\n\ncd ${TRAVIS_BUILD_DIR}/build/out\nif [[ \"$TRAVIS_OS_NAME\" == \"osx\" ]]; then\n    OS_NAME=\"mac\"\n    OS_SHARED_LIB_EXT=\"dylib\"\nelse\n    OS_NAME=\"linux\"\n    OS_SHARED_LIB_EXT=\"so\"\nfi\nOSQP_BIN=osqp-${OSQP_VERSION}-${OS_NAME}64\nmkdir $OSQP_BIN/\nmkdir $OSQP_BIN/lib\nmkdir $OSQP_BIN/include\n# Copy license\ncp ../../LICENSE $OSQP_BIN/\n# Copy includes\ncp ../../include/*.h  $OSQP_BIN/include\n# Copy static library\ncp libosqp.a $OSQP_BIN/lib\n# Copy shared library\ncp libosqp.$OS_SHARED_LIB_EXT $OSQP_BIN/lib\n# Compress package\ntar -czvf ${TRAVIS_BUILD_DIR}/$OSQP_BIN.tar.gz  $OSQP_BIN\n\n\necho \"Creating Bintray sources package...\"\n\nOSQP_SOURCES=osqp-${OSQP_VERSION}\n# Clone OSQP repository\ncd ${TRAVIS_BUILD_DIR}\nmkdir sources/\ncd sources/\ngit clone https://github.com/$TRAVIS_REPO_SLUG.git ${OSQP_SOURCES} --recursive\ncd ${OSQP_SOURCES}\ngit checkout -qf $TRAVIS_COMMIT\ngit submodule update\ncd ..\n# Create archive ignoring hidden files\ntar --exclude=\".*\" -czvf ${TRAVIS_BUILD_DIR}/${OSQP_SOURCES}.tar.gz ${OSQP_SOURCES}\n\n# Upload to Bintray\n# Deploy sources\ncurl -T ${TRAVIS_BUILD_DIR}/${OSQP_SOURCES}.tar.gz -ubstellato:$BINTRAY_API_KEY -H \"X-Bintray-Package:${OSQP_PACKAGE_NAME}\" -H \"X-Bintray-Version:${OSQP_VERSION}\" -H \"X-Bintray-Override: 1\" https://api.bintray.com/content/bstellato/generic/${OSQP_PACKAGE_NAME}/${OSQP_VERSION}/\n\n# Deploy shared library binaries\ncurl -T ${TRAVIS_BUILD_DIR}/${OSQP_BIN}.tar.gz -ubstellato:$BINTRAY_API_KEY -H \"X-Bintray-Package:${OSQP_PACKAGE_NAME}\" -H \"X-Bintray-Version:${OSQP_VERSION}\" -H \"X-Bintray-Override: 1\" https://api.bintray.com/content/bstellato/generic/${OSQP_PACKAGE_NAME}/${OSQP_VERSION}/\n\n# Publish deployed files\ncurl -X POST -ubstellato:$BINTRAY_API_KEY https://api.bintray.com/content/bstellato/generic/${OSQP_PACKAGE_NAME}/${OSQP_VERSION}/publish\n\nset +e\n"
  },
  {
    "path": "osqp/ci/travis/docs.sh",
    "content": "#!/bin/bash\n\necho \"Deploying docs to website osqp.org...\"\n\n# Update variables from install\n\n# Anaconda\nexport PATH=${DEPS_DIR}/miniconda/bin:$PATH\nhash -r\nsource activate testenv\n\n\nif [[ $TRAVIS_OS_NAME == \"linux\" ]]; then\n\n\t# Install dependencies\n\t# Add PPA to make doxygen installation work\n\tsudo add-apt-repository ppa:libreoffice/ppa -y\n\tsudo apt-get update -q -y\n\tsudo apt-get install -y doxygen\n\tconda install -y sphinx\n\tconda install -y -c conda-forge sphinx_rtd_theme breathe\n\n\t# Enter in docs folder\n\tcd ${TRAVIS_BUILD_DIR}/docs/\n\n\t# Create docs\n\tmake html\n\n\t# Clone website repository\n\tcd $HOME/\n\tgit clone \"https://${OSQP_DOCS_DEPLOY_GH_TOKEN}@github.com/oxfordcontrol/osqp.org.git\"\n\tcd osqp.org/src/docs/\n\n\t# Update git config to push\n\tgit config user.name \"OSQP Docs Builder\"\n\tgit config user.email \"$OSQP_DOCS_DEPLOY_EMAIL\"\n\n\t# Copy in the docs HTML\n\tcp -R ${TRAVIS_BUILD_DIR}/docs/_build/html/* ./\n\n\t# Add and commit changes.\n\tgit add -A .\n\tgit commit --allow-empty -m \"Generated docs for commit $TRAVIS_COMMIT\"\n\t# -q is very important, otherwise you leak your GH_TOKEN\n\tgit push -q origin master\n\nfi\n"
  },
  {
    "path": "osqp/ci/travis/install.sh",
    "content": "#!/bin/bash\n\n# Create deps dir\nmkdir ${DEPS_DIR}\ncd ${DEPS_DIR}\n\n# Install lcov and valgrind on linux\nif [[ \"$TRAVIS_OS_NAME\" == \"linux\" ]]; then\n    sudo apt-get update -y\n    sudo apt-get install -y lcov\n    sudo apt-get install -y valgrind\nelse if [[ \"$TRAVIS_OS_NAME\" == \"osx\" ]]; then\n    brew update\n    brew install lcov\n    fi\nfi\n\n# Install Anaconda\n# Use the miniconda installer for faster download / install of conda\n# itself\nif [[ $TRAVIS_OS_NAME == 'osx' ]]; then\n    wget http://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -O miniconda.sh;\nelse\n    wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;\nfi\nexport CONDA_ROOT=${DEPS_DIR}/miniconda\nchmod +x miniconda.sh && ./miniconda.sh -b -p $CONDA_ROOT\nexport PATH=${DEPS_DIR}/miniconda/bin:$PATH\nhash -r\nconda config --set always_yes yes --set changeps1 no\nconda update --yes -q conda\nconda create -n testenv --yes python=$PYTHON_VERSION mkl numpy scipy future\nsource activate testenv\n\n# Install cmake\nconda install --yes -c conda-forge cmake\n\n# Install coveralls lcov\ngem install coveralls-lcov\n\ncd ${TRAVIS_BUILD_DIR}\n"
  },
  {
    "path": "osqp/ci/travis/script.sh",
    "content": "#!/bin/bash\nset -e\n\n# Add MKL shared libraries to the path\n# This unfortunately does not work in travis OSX if I put the export command\n# in the install.sh (it works on linux though)\nexport MKL_SHARED_LIB_DIR=`ls -rd ${CONDA_ROOT}/pkgs/*/ | grep mkl-2 | head -n 1`lib:`ls -rd ${CONDA_ROOT}/pkgs/*/ | grep intel-openmp- | head -n 1`lib\n\necho \"MKL shared library path: ${MKL_SHARED_LIB_DIR}\"\n\nif [[ \"${TRAVIS_OS_NAME}\" == \"linux\" ]]; then\n    export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MKL_SHARED_LIB_DIR}\nelse if [[ \"$TRAVIS_OS_NAME\" == \"osx\" ]]; then\n    export DYLD_LIBRARY_PATH=${DYLD_LIBRARY_PATH}:${MKL_SHARED_LIB_DIR}\nfi\nfi\n\n\n# Test C interface\n# ---------------------------------------------------\n\n# Compile and test OSQP\necho \"Change directory to Travis build ${TRAVIS_BUILD_DIR}\"\necho \"Testing OSQP with standard configuration\"\ncd ${TRAVIS_BUILD_DIR}\nmkdir build\ncd build\ncmake -G \"Unix Makefiles\" -DCOVERAGE=ON -DUNITTESTS=ON ..\nmake\n${TRAVIS_BUILD_DIR}/build/out/osqp_tester\n# Pefrorm code coverage (only in Linux case)\nif [[ $TRAVIS_OS_NAME == \"linux\" ]]; then\n    cd ${TRAVIS_BUILD_DIR}/build\n    lcov --directory . --capture -o coverage.info # capture coverage info\n    lcov --remove coverage.info \"${TRAVIS_BUILD_DIR}/tests/*\" \\\n        \"${TRAVIS_BUILD_DIR}/lin_sys/direct/qdldl/amd/*\" \\\n        \"${TRAVIS_BUILD_DIR}/lin_sys/direct/qdldl/qdldl_sources/*\" \\\n        \"/usr/include/x86_64-linux-gnu/**/*\" \\\n        -o coverage.info # filter out tests and unnecessary files\n    lcov --list coverage.info # debug before upload\n    coveralls-lcov coverage.info # uploads to coveralls\nfi\n\nif [[ $TRAVIS_OS_NAME == \"linux\" ]]; then\n    echo \"Testing OSQP with valgrind (disabling MKL pardiso for memory allocation issues)\"\n    cd ${TRAVIS_BUILD_DIR}\n    rm -rf build\n    mkdir build\n    cd build\n    #disable PARDISO since intel instructions in MKL\n    #cause valgrind 3.11 to fail\n    cmake -G \"Unix Makefiles\" -DENABLE_MKL_PARDISO=OFF -DUNITTESTS=ON ..\n    make\n    valgrind --suppressions=${TRAVIS_BUILD_DIR}/.valgrind-suppress.supp --leak-check=full --gen-suppressions=all --track-origins=yes --error-exitcode=42 ${TRAVIS_BUILD_DIR}/build/out/osqp_tester\nfi\n\necho \"Testing OSQP with floats\"\ncd ${TRAVIS_BUILD_DIR}\nrm -rf build\nmkdir build\ncd build\ncmake -G \"Unix Makefiles\" -DDFLOAT=ON -DUNITTESTS=ON ..\nmake\n${TRAVIS_BUILD_DIR}/build/out/osqp_tester\n\necho \"Testing OSQP without long integers\"\ncd ${TRAVIS_BUILD_DIR}\nrm -rf build\nmkdir build\ncd build\ncmake -G \"Unix Makefiles\" -DDLONG=OFF -DUNITTESTS=ON ..\nmake\n${TRAVIS_BUILD_DIR}/build/out/osqp_tester\n\necho \"Building OSQP with embedded=1\"\ncd ${TRAVIS_BUILD_DIR}\nrm -rf build\nmkdir build\ncd build\ncmake -G \"Unix Makefiles\" -DEMBEDDED=1 ..\nmake\n\necho \"Building OSQP with embedded=2\"\ncd ${TRAVIS_BUILD_DIR}\nrm -rf build\nmkdir build\ncd build\ncmake -G \"Unix Makefiles\" -DEMBEDDED=2 ..\nmake\n\necho \"Testing OSQP without printing\"\ncd ${TRAVIS_BUILD_DIR}\nrm -rf build\nmkdir build\ncd build\ncmake -G \"Unix Makefiles\" -DPRINTING=OFF -DUNITTESTS=ON ..\nmake\n${TRAVIS_BUILD_DIR}/build/out/osqp_tester\n\n\n# Test custom memory management\n# ---------------------------------------------------\n\necho \"Test OSQP custom allocators\"\ncd ${TRAVIS_BUILD_DIR}\nrm -rf build\nmkdir build\ncd build\ncmake -DUNITTESTS=ON \\\n    -DOSQP_CUSTOM_MEMORY=${TRAVIS_BUILD_DIR}/tests/custom_memory/custom_memory.h \\\n    ..\nmake osqp_tester_custom_memory\n${TRAVIS_BUILD_DIR}/build/out/osqp_tester_custom_memory\n\n\ncd ${TRAVIS_BUILD_DIR}\n\nset +e\n"
  },
  {
    "path": "osqp/configure/cmake/FindPythonModule.cmake",
    "content": "# Find python module and version\n#\n# It sets the variables (given module called MODULE)\n# unless MODULE is __FUTURE__. In that case\n# it only checks if it has been found.\n#\n# MODULE_FOUND               - has the module been found?\n# MODULE_VERSION             - module version as a string\n# MODULE_VERSION_MAJOR       - major version number\n# MODULE_VERSION_MINOR       - minor version number\n# MODULE_VERSION_PATCH       - patch version number\n# MODULE_VERSION_DECIMAL     - e.g. version 1.6.1 is 10601\n#\n\n\nfunction(find_python_module module)\n    # Write module in upper and lower case\n\tstring(TOUPPER ${module} module_upper)\n\tstring(TOLOWER ${module} module_lower)\n\n    unset(${module_upper}_VERSION)\n    #\n    # if(ARGC GREATER 1 AND ARGV1 STREQUAL \"REQUIRED\")\n    #     set(${module_upper}_FIND_REQUIRED TRUE)\n    # endif()\n\n    if(PYTHONINTERP_FOUND)\n        if (NOT ${module} STREQUAL \"__future__\")\n            execute_process(COMMAND \"${PYTHON_EXECUTABLE}\" \"-c\"\n              \"import ${module_lower} as n; print(n.__version__);\"\n              RESULT_VARIABLE __result\n              OUTPUT_VARIABLE __output\n              ERROR_QUIET\n              OUTPUT_STRIP_TRAILING_WHITESPACE)\n\n              if(__result MATCHES 0)\n                string(REGEX REPLACE \";\" \"\\\\\\\\;\" __values ${__output})\n                string(REGEX REPLACE \"\\r?\\n\" \";\"    __values ${__values})\n                list(GET __values 0 ${module_upper}_VERSION)\n\n                string(REGEX MATCH \"^([0-9])+\\\\.([0-9])+\\\\.([0-9])+\" __ver_check \"${${module_upper}_VERSION}\")\n\n                if(NOT \"${__ver_check}\" STREQUAL \"\")\n                  set(${module_upper}_VERSION_MAJOR ${CMAKE_MATCH_1})\n                  set(${module_upper}_VERSION_MINOR ${CMAKE_MATCH_2})\n                  set(${module_upper}_VERSION_PATCH ${CMAKE_MATCH_3})\n                  math(EXPR ${module_upper}_VERSION_DECIMAL\n                    \"(${CMAKE_MATCH_1} * 10000) + (${CMAKE_MATCH_2} * 100) + ${CMAKE_MATCH_3}\")\n                else()\n                 unset(${module_upper}_VERSION)\n                 message(STATUS \"Requested ${module_lower} version, but got instead:\\n${__output}\\n\")\n                endif()\n\n                find_package_handle_standard_args(${module_upper}\n                    FOUND_VAR ${module_upper}_FOUND\n                    REQUIRED_VARS ${module_upper}_VERSION\n                    VERSION_VAR  ${module_upper}_VERSION)\n\n              endif()\n        else()\n            execute_process(COMMAND \"${PYTHON_EXECUTABLE}\" \"-c\"\n              \"import ${module_lower} as n\"\n              RESULT_VARIABLE __result\n              OUTPUT_VARIABLE __output\n              ERROR_QUIET\n              OUTPUT_STRIP_TRAILING_WHITESPACE)\n              if(NOT __result)\n                  set(${module_upper}_FOUND ON)\n              endif()\n              message(STATUS \"Found Python __FUTURE__\")\n        endif()\n\n\n    else()\n        message(STATUS \"Python interpreter not found. To find ${module} you need the Python interpreter.\")\n    endif()\n\n\n    # Set variables in parent scope\n    if(${module_upper}_FOUND)\n        set(${module_upper}_FOUND ON PARENT_SCOPE)\n        if (NOT ${module} STREQUAL \"__future__\")\n            set(${module_upper}_VERSION ${${module_upper}_VERSION} PARENT_SCOPE)\n            set(${module_upper}_VERSION_MAJOR ${${module_upper}_VERSION_MAJOR} PARENT_SCOPE)\n            set(${module_upper}_VERSION_MINOR ${${module_upper}_VERSION_MINOR} PARENT_SCOPE)\n            set(${module_upper}_VERSION_PATCH ${${module_upper}_VERSION_PATCH} PARENT_SCOPE)\n            set(${module_upper}_VERSION_DECIMAL ${${module_upper}_VERSION_DECIMAL} PARENT_SCOPE)\n        endif()\n    endif()\n\n    # Clear variables\n    osqp_clear_vars(__result __output __values __ver_check)\n\nendfunction(find_python_module)\n"
  },
  {
    "path": "osqp/configure/cmake/FindR.cmake",
    "content": "# CMake module to find R\n# - Try to find R.  If found, defines:\n#\n#  R_FOUND        - system has R\n#  R_EXEC         - the system R command\n#  R_ROOT_DIR     - the R root directory\n#  R_INCLUDE_DIRS - the R include directories\n\nset(TEMP_CMAKE_FIND_APPBUNDLE ${CMAKE_FIND_APPBUNDLE})\nset(CMAKE_FIND_APPBUNDLE \"NEVER\")\nfind_program(R_EXEC NAMES R R.exe)\nset(CMAKE_FIND_APPBUNDLE ${TEMP_CMAKE_FIND_APPBUNDLE})\n\n#---Find includes and libraries if R exists\nif(R_EXEC)\n\n  set(R_FOUND TRUE)\n\n  if((CMAKE_HOST_SOLARIS) AND (DEFINED ENV{R_HOME}))\n      message(STATUS \"Unsetting R_HOME on Solaris.\")\n      unset(ENV{R_HOME})\n  endif()\n\n  execute_process(WORKING_DIRECTORY .\n  COMMAND ${R_EXEC} RHOME\n  OUTPUT_VARIABLE R_ROOT_DIR\n  OUTPUT_STRIP_TRAILING_WHITESPACE)\n\n  find_path(R_INCLUDE_DIRS R.h\n            PATHS /usr/local/lib /usr/local/lib64 /usr/share /usr/include ${R_ROOT_DIR} PATH_SUFFIXES include R R/include)\n\nendif()\n\nmark_as_advanced(R_FOUND R_EXEC R_ROOT_DIR R_INCLUDE_DIRS)\n"
  },
  {
    "path": "osqp/configure/cmake/Utils.cmake",
    "content": "# Clears variables from list\n# Usage:\n#   osqp_clear_vars(<variables_list>)\nmacro(osqp_clear_vars)\n  foreach(_var ${ARGN})\n    unset(${_var})\n  endforeach()\nendmacro()\n"
  },
  {
    "path": "osqp/configure/cmake/cmake_uninstall.cmake.in",
    "content": "if(NOT EXISTS \"@CMAKE_BINARY_DIR@/install_manifest.txt\")\n  message(FATAL_ERROR \"Cannot find install manifest: @CMAKE_BINARY_DIR@/install_manifest.txt\")\nendif(NOT EXISTS \"@CMAKE_BINARY_DIR@/install_manifest.txt\")\n\nfile(READ \"@CMAKE_BINARY_DIR@/install_manifest.txt\" files)\nstring(REGEX REPLACE \"\\n\" \";\" files \"${files}\")\nforeach(file ${files})\n  message(STATUS \"Uninstalling $ENV{DESTDIR}${file}\")\n  if(IS_SYMLINK \"$ENV{DESTDIR}${file}\" OR EXISTS \"$ENV{DESTDIR}${file}\")\n    exec_program(\n      \"@CMAKE_COMMAND@\" ARGS \"-E remove \\\"$ENV{DESTDIR}${file}\\\"\"\n      OUTPUT_VARIABLE rm_out\n      RETURN_VALUE rm_retval\n      )\n    if(NOT \"${rm_retval}\" STREQUAL 0)\n      message(FATAL_ERROR \"Problem when removing $ENV{DESTDIR}${file}\")\n    endif(NOT \"${rm_retval}\" STREQUAL 0)\n  else(IS_SYMLINK \"$ENV{DESTDIR}${file}\" OR EXISTS \"$ENV{DESTDIR}${file}\")\n    message(STATUS \"File $ENV{DESTDIR}${file} does not exist.\")\n  endif(IS_SYMLINK \"$ENV{DESTDIR}${file}\" OR EXISTS \"$ENV{DESTDIR}${file}\")\nendforeach(file)"
  },
  {
    "path": "osqp/configure/osqp_configure.h.in",
    "content": "#ifndef OSQP_CONFIGURE_H\n# define OSQP_CONFIGURE_H\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif /* ifdef __cplusplus */\n\n/* DEBUG */\n#cmakedefine DEBUG\n\n/* Operating system */\n#cmakedefine IS_LINUX\n#cmakedefine IS_MAC\n#cmakedefine IS_WINDOWS\n\n/* EMBEDDED */\n#cmakedefine EMBEDDED (@EMBEDDED@)\n\n/* PRINTING */\n#cmakedefine PRINTING\n\n/* PROFILING */\n#cmakedefine PROFILING\n\n/* CTRLC */\n#cmakedefine CTRLC\n\n/* DFLOAT */\n#cmakedefine DFLOAT\n\n/* DLONG */\n#cmakedefine DLONG\n\n/* ENABLE_MKL_PARDISO */\n#cmakedefine ENABLE_MKL_PARDISO\n\n/* MEMORY MANAGEMENT */\n#cmakedefine OSQP_CUSTOM_MEMORY\n#ifdef OSQP_CUSTOM_MEMORY\n#include \"@OSQP_CUSTOM_MEMORY@\"\n#endif\n\n\n\n# ifdef __cplusplus\n}\n# endif /* ifdef __cplusplus */\n\n#endif /* ifndef OSQP_CONFIGURE_H */\n"
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    "path": "osqp/docs/Makefile",
    "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line.\nSPHINXOPTS    =\nSPHINXBUILD   = sphinx-build\nSPHINXPROJ    = osqp\nSOURCEDIR     = .\nBUILDDIR      = _build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\t\n"
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  },
  {
    "path": "osqp/docs/_templates/layout.html",
    "content": "{% extends '!layout.html' %}\n\n{% block extrahead %}\n<meta name=\"twitter:card\" content=\"summary_large_image\" >\n<meta name=\"twitter:creator\" content=\"@bstellato\" >\n<meta name=\"twitter:image\"\n      content=\"https://osqp.org/docs/_static/img/logo.png\">\n<meta name=\"twitter:description\" content=\"The OSQP (Operator Splitting\nQuadratic Program) solver is a numerical optimization package for solving\nconvex quadratic programs.\" >\n<meta property=\"og:url\" content=\"https://osqp.org\" >\n<meta property=\"og:title\" content=\"OSQP solver documentation\" >\n<meta property=\"og:description\" content=\"The OSQP (Operator Splitting Quadratic\nProgram) solver is a numerical optimization package for solving convex\nquadratic programs.\" >\n<meta property=\"og:image\"\ncontent=\"https://osqp.org/docs/_static/img/logo.png\">\n<meta property=\"fb:app_id\" content=\"129555174389689\">\n<meta property=\"og:type\" content=\"website\" >\n{% endblock %}\n"
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  {
    "path": "osqp/docs/citing/index.rst",
    "content": ".. _citing :\n\nCiting OSQP\n===========\n\nIf you use OSQP for published work, we encourage you to put a star on `GitHub <https://github.com/oxfordcontrol/osqp>`_ and cite the accompanying papers:\n\n\n.. glossary::\n\n    Main paper\n        Main algorithm description, derivation and benchmark available in this `paper <https://web.stanford.edu/~boyd/papers/pdf/osqp.pdf>`__.\n\n        .. code:: latex\n\n          @article{osqp,\n            author  = {Stellato, Bartolomeo and Banjac, Goran and Goulart, Paul and Bemporad, Alberto and Boyd, Stephen},\n            title   = {{{OSQP}}: An Operator Splitting Solver for Quadratic Programs},\n            journal = {Mathematical Programming Computation},\n            year    = {2020},\n            doi     = {10.1007/s12532-020-00179-2},\n            url     = {https://doi.org/10.1007/s12532-020-00179-2}\n          }\n\n    Infeasibility detection\n        Infeasibility detection proofs using ADMM (also for general conic programs) in this `paper <https://stanford.edu/~boyd/papers/pdf/admm_infeas.pdf>`__.\n\n        .. code:: latex\n\n          @article{osqp-infeasibility,\n            author  = {Banjac, G. and Goulart, P. and Stellato, B. and Boyd, S.},\n            title   = {Infeasibility detection in the alternating direction method of multipliers for convex optimization},\n            journal = {Journal of Optimization Theory and Applications},\n            year    = {2019},\n            volume  = {183},\n            number  = {2},\n            pages   = {490--519},\n            doi     = {10.1007/s10957-019-01575-y},\n            url     = {https://doi.org/10.1007/s10957-019-01575-y},\n          }\n\n    Code generation\n        Code generation functionality and example in this `paper <https://stanford.edu/~boyd/papers/pdf/osqp_embedded.pdf>`_.\n\n        .. code:: latex\n\n          @inproceedings{osqp-codegen,\n            author    = {Banjac, G. and Stellato, B. and Moehle, N. and Goulart, P. and Bemporad, A. and Boyd, S.},\n            title     = {Embedded code generation using the {OSQP} solver},\n            booktitle = {IEEE Conference on Decision and Control (CDC)},\n            year      = {2017},\n            doi       = {10.1109/CDC.2017.8263928},\n            url       = {https://doi.org/10.1109/CDC.2017.8263928},\n          }\n"
  },
  {
    "path": "osqp/docs/codegen/index.rst",
    "content": "Code generation\n===============\n\nOSQP can generate tailored C code that compiles into a fast and reliable solver\nfor the given family of QP problems in which the problem data, but not its\ndimensions, change between problem instances.\n\n\nThe generated code is:\n\n.. glossary::\n\n      Malloc-free\n          It does not perform any dynamic memory allocation.\n\n      Library-free\n          It is not linked to any external library.\n\n      Division-free\n          There are no division required in the ADMM algorithm\n\n\nWe make a distinction between two cases depending on which of the data are to be\ntreated as parameters.\n\n.. glossary::\n\n    Vectors as parameters\n        Vectors :math:`q`, :math:`l` and :math:`u` can change between problem instances.\n        This corresponds to the compiler flag :code:`EMBEDDED=1`.\n        :math:`\\rho` adaptation is not enabled. \n        The generated code is division-free and has a very small footprint.\n\n    Matrices as parameters\n        Both vectors :math:`q`, :math:`l`, :math:`u` and values in matrices\n        :math:`P` and :math:`A` can change between problem instances.\n        This corresponds to the compiler flag :code:`EMBEDDED=2`.\n        :math:`\\rho` adaptation is enabled but the frequency does not rely on any timing. \n        We assume that the sparsity patterns of :math:`P` and :math:`A` are fixed.\n\n\n.. toctree::\n   :maxdepth: 1\n   :glob:\n\n   *\n"
  },
  {
    "path": "osqp/docs/codegen/matlab.rst",
    "content": "Matlab\n======\n\nBefore generating code for a parametric problem, the problem should be first\nspecified in the setup phase. See :ref:`matlab_setup` for more details.\n\n\nCodegen\n-------\nThe code is generated by running\n\n.. code:: matlab\n\n    m.codegen(dir_name, varargin)\n\nThe argument :code:`dir_name` is the name of a directory where the generated\ncode is stored.\nThe second argument :code:`varargin` specifies additional codegen options\nshown in the following table\n\n\n+-----------------------+---------------------------------------------------+--------------------------------+\n| Option                | Description                                       | Allowed values                 |\n+=======================+===================================================+================================+\n| :code:`project_type`  | Build environment                                 | | :code:`''` (default)         |\n|                       |                                                   | | :code:`'Makefile'`           |\n|                       |                                                   | | :code:`'MinGW Makefiles'`    |\n|                       |                                                   | | :code:`'Unix Makefiles'`     |\n|                       |                                                   | | :code:`'CodeBlocks'`         |\n|                       |                                                   | | :code:`'Xcode'`              |\n+-----------------------+---------------------------------------------------+--------------------------------+\n| :code:`parameters`    | Problem parameters                                | | :code:`'vectors'` (default)  |\n|                       |                                                   | | :code:`'matrices'`           |\n+-----------------------+---------------------------------------------------+--------------------------------+\n| :code:`mexname`       | Name of the compiled mex interface                | | :code:`'emosqp'` (default)   |\n|                       |                                                   | | Nonempty string              |\n+-----------------------+---------------------------------------------------+--------------------------------+\n| :code:`force_rewrite` | Rewrite existing directory                        | | :code:`false` (default)      |\n|                       |                                                   | | :code:`true`                 |\n+-----------------------+---------------------------------------------------+--------------------------------+\n| :code:`FLOAT`         | Use :code:`float` type instead of :code:`double`  | | :code:`false` (default)      |\n|                       |                                                   | | :code:`true`                 |\n+-----------------------+---------------------------------------------------+--------------------------------+\n| :code:`LONG`          | Use :code:`long long` type instead of :code:`int` | | :code:`true` (default)       |\n|                       |                                                   | | :code:`false`                |\n+-----------------------+---------------------------------------------------+--------------------------------+\n\nYou can pass the options as field-value pairs, e.g.,\n\n.. code:: matlab\n\n    m.codegen('code', 'parameters', 'matrices', 'mexname', 'emosqp');\n\nIf the :code:`project_type` argument is not passed or is set to :code:`''`,\nthen no build files are generated.\n\n\n\nMex interface\n-------------\nOnce the code is generated the following functions are provided through its mex interface. Each function is called as\n\n.. code:: matlab\n\n    emosqp('function_name');\n\n\nwhere :code:`emosqp` is the name of the mex interface specified in the previous section\n\n.. function:: emosqp('solve')\n   :noindex:\n\n   Solve the problem.\n\n   :returns: multiple variables [x, y, status_val, iter, run_time]\n\n             - **x** (*ndarray*) - Primal solution\n             - **y** (*ndarray*) - Dual solution\n             - **status_val** (*int*) - Status value as in :ref:`status_values`\n             - **iter** (*int*) - Number of iterations\n             - **run_time** (*double*) - Run time\n\n\n.. function:: emosqp('update_lin_cost', q_new)\n   :noindex:\n\n   Update linear cost.\n\n   :param ndarray q_new: New linear cost vector\n\n\n.. function:: emosqp('update_lower_bound', l_new)\n   :noindex:\n\n   Update lower bound in the constraints.\n\n   :param ndarray l_new: New lower bound vector\n\n\n.. function:: emosqp('update_upper_bound', u_new)\n   :noindex:\n\n   Update upper bound in the constraints.\n\n   :param ndarray u_new: New upper bound vector\n\n\n.. function:: emosqp('update_bounds', l_new, u_new)\n   :noindex:\n\n   Update lower and upper bounds in the constraints.\n\n   :param ndarray l_new: New lower bound vector\n   :param ndarray u_new: New upper bound vector\n\n\nIf the code is generated with the option :code:`parameters` set to\n:code:`'matrices'`, then the following functions are also provided\n\n\n.. function:: emosqp('update_P', Px, Px_idx, Px_n)\n   :noindex:\n\n  Update nonzero entries of the quadratic cost matrix (only upper triangular) without changing sparsity structure.\n\n   :param ndarray Px: Values of entries to be updated\n   :param ndarray Px_idx: Indices of entries to be updated. Pass :code:`[]` if\n                         all the indices are to be updated\n   :param int Px_n: Number of entries to be updated. Used only if Px_idx is not\n                   :code:`[]`.\n\n\n.. function:: emosqp('update_A', Ax, Ax_idx, Ax_n)\n   :noindex:\n\n   Update nonzero entries of the constraint matrix.\n\n   :param ndarray Ax: Values of entries to be updated\n   :param ndarray Ax_idx: Indices of entries to be updated. Pass :code:`[]` if\n                         all the indices are to be updated\n   :param int Ax_n: Number of entries to be updated. Used only if Ax_idx is not\n                   :code:`[]`.\n\n\n.. function:: emosqp('update_P_A', Px, Px_idx, Px_n, Ax, Ax_idx, Ax_n)\n   :noindex:\n\n  Update nonzero entries of the quadratic cost and constraint matrices. It considers only the upper-triangular part of P.\n\n   :param ndarray Px: Values of entries to be updated\n   :param ndarray Px_idx: Indices of entries to be updated. Pass :code:`[]` if\n                         all the indices are to be updated\n   :param int Px_n: Number of entries to be updated. Used only if Px_idx is not\n                   :code:`[]`.\n   :param ndarray Ax: Values of entries to be updated\n   :param ndarray Ax_idx: Indices of entries to be updated. Pass :code:`[]` if\n                         all the indices are to be updated\n   :param int Ax_n: Number of entries to be updated. Used only if Ax_idx is not\n                   :code:`[]`.\n\n\nYou can update all the nonzero entries in matrix :math:`A` by running\n\n.. code:: matlab\n\n    emosqp('update_A', Ax_new, [], 0);\n\nSee C/C++ :ref:`C_sublevel_API` for more details on the input arguments.\n"
  },
  {
    "path": "osqp/docs/codegen/python.rst",
    "content": "Python\n======\n\nBefore generating code for a parametric problem, the problem should be first\nspecified in the setup phase. See :ref:`python_setup` for more details.\n\n\nCodegen\n-------\nThe code is generated by running\n\n.. code:: python\n\n    m.codegen(dir_name, **opts)\n\nThe argument :code:`dir_name` is the name of a directory where the generated\ncode is stored.\nAdditional codegen options are shown in the following table\n\n+-------------------------+---------------------------------------------------+--------------------------------+\n| Option                  | Description                                       | Allowed values                 |\n+=========================+===================================================+================================+\n| :code:`project_type`    | Build environment                                 | | :code:`''` (default)         |\n|                         |                                                   | | :code:`'Makefile'`           |\n|                         |                                                   | | :code:`'MinGW Makefiles'`    |\n|                         |                                                   | | :code:`'Unix Makefiles'`     |\n|                         |                                                   | | :code:`'CodeBlocks'`         |\n|                         |                                                   | | :code:`'Xcode'`              |\n+-------------------------+---------------------------------------------------+--------------------------------+\n| :code:`parameters`      | Problem parameters                                | | :code:`'vectors'` (default)  |\n|                         |                                                   | | :code:`'matrices'`           |\n+-------------------------+---------------------------------------------------+--------------------------------+\n| :code:`python_ext_name` | Name of the generated Python module               | | :code:`'emosqp'` (default)   |\n|                         |                                                   | | Nonempty string              |\n+-------------------------+---------------------------------------------------+--------------------------------+\n| :code:`force_rewrite`   | Rewrite existing directory                        | | :code:`False` (default)      |\n|                         |                                                   | | :code:`True`                 |\n+-------------------------+---------------------------------------------------+--------------------------------+\n| :code:`FLOAT`           | Use :code:`float` type instead of :code:`double`  | | :code:`False` (default)      |\n|                         |                                                   | | :code:`True`                 |\n+-------------------------+---------------------------------------------------+--------------------------------+\n| :code:`LONG`            | Use :code:`long long` type instead of :code:`int` | | :code:`True` (default)       |\n|                         |                                                   | | :code:`False`                |\n+-------------------------+---------------------------------------------------+--------------------------------+\n\nThe options are passed using named arguments, e.g.,\n\n.. code:: python\n\n    m.codegen('code', parameters='matrices', python_ext_name='emosqp')\n\nIf the :code:`project_type` argument is not passed or is set to :code:`''`,\nthen no build files are generated.\n\n\n\nExtension module API\n--------------------\nOnce the code is generated, you can import a light python wrapper with\n\n.. code:: python\n\n    import emosqp\n\nwhere :code:`emosqp` is the extension name given in the previous section. The module imports the following functions\n\n.. py:function:: solve()\n   :noindex:\n\n   Solve the problem.\n\n   :returns: tuple (x, y, status_val, iter, run_time)\n\n             - **x** (*ndarray*) - Primal solution\n             - **y** (*ndarray*) - Dual solution\n             - **status_val** (*int*) - Status value as in :ref:`status_values`\n             - **iter** (*int*) - Number of iterations\n             - **run_time** (*double*) - Run time\n\n\n\n\n\n.. py:function:: update_lin_cost(q_new)\n   :noindex:\n\n   Update linear cost.\n\n   :param ndarray q_new: New linear cost vector\n\n\n.. py:function:: update_lower_bound(l_new)\n   :noindex:\n\n   Update lower bound in the constraints.\n\n   :param ndarray l_new: New lower bound vector\n\n\n.. py:function:: update_upper_bound(u_new)\n   :noindex:\n\n   Update upper bound in the constraints.\n\n   :param ndarray u_new: New upper bound vector\n\n\n.. py:function:: update_bounds(l_new, u_new)\n   :noindex:\n\n   Update lower and upper bounds in the constraints.\n\n   :param ndarray l_new: New lower bound vector\n   :param ndarray u_new: New upper bound vector\n\n\nIf the code is generated with the option :code:`parameters` set to\n:code:`'matrices'`, the following functions are also provided\n\n\n.. py:function:: update_P(Px, Px_idx, Px_n)\n  :noindex:\n\n  Update nonzero entries of the quadratic cost matrix (only upper triangular) without changing sparsity structure.\n\n  :param ndarray Px: Values of entries to be updated\n  :param ndarray Px_idx: Indices of entries to be updated. Pass :code:`None` if\n                         all the indices are to be updated\n  :param int Px_n: Number of entries to be updated. Used only if Px_idx is not\n                   :code:`None`.\n\n\n.. py:function:: update_A(Ax, Ax_idx, Ax_n)\n  :noindex:\n  \n  Update nonzero entries of the constraint matrix.\n\n  :param ndarray Ax: Values of entries to be updated\n  :param ndarray Ax_idx: Indices of entries to be updated. Pass :code:`None` if\n                         all the indices are to be updated\n  :param int Ax_n: Number of entries to be updated. Used only if Ax_idx is not\n                   :code:`None`.\n\n\n.. py:function:: update_P_A(Px, Px_idx, Px_n, Ax, Ax_idx, Ax_n)\n  :noindex:\n\n  Update nonzero entries of the quadratic cost and constraint matrices. It considers only the upper-triangular part of P.\n\n  :param ndarray Px: Values of entries to be updated\n  :param ndarray Px_idx: Indices of entries to be updated. Pass :code:`None` if\n                         all the indices are to be updated\n  :param int Px_n: Number of entries to be updated. Used only if Px_idx is not\n                   :code:`None`.\n  :param ndarray Ax: Values of entries to be updated\n  :param ndarray Ax_idx: Indices of entries to be updated. Pass :code:`None` if\n                         all the indices are to be updated\n  :param int Ax_n: Number of entries to be updated. Used only if Ax_idx is not\n                   :code:`None`.\n\n\nYou can update all the nonzero entries in matrix :math:`A` by running\n\n.. code:: python\n\n    emosqp.update_A(Ax_new, None, 0);\n\nSee C/C++ :ref:`C_sublevel_API` for more details on the input arguments.\n"
  },
  {
    "path": "osqp/docs/conf.py",
    "content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#\n# OSQP documentation build configuration file, created by\n# sphinx-quickstart on Sat Feb 18 15:49:00 2017.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n\nimport sphinx_rtd_theme\nimport os\nimport subprocess\n# import sys\n# sys.path.insert(0, os.path.abspath('.'))\n\n# Set version for bumpversion\n__version__ = \"0.6.0\"\n\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#\n# needs_sphinx = '1.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = ['sphinx.ext.todo', 'sphinx.ext.mathjax', 'breathe']\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n#\n# source_suffix = ['.rst', '.md']\nsource_suffix = '.rst'\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# General information about the project.\nproject = 'OSQP'\ncopyright = '2019, Bartolomeo Stellato, Goran Banjac'\nauthor = 'Bartolomeo Stellato, Goran Banjac'\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n\nversion = '.'.join(__version__.split('.')[:4])\n\n# The full version, including alpha/beta/rc tags.\nrelease = __version__\n\n# The language for content autogenerated by Sphinx. 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List of tuples\n# (source start file, target name, title,\n#  author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n    (master_doc, 'OSQP.tex', 'OSQP Documentation',\n     'Bartolomeo Stellato, Goran Banjac', 'manual'),\n]\n\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [\n    (master_doc, 'OSQP', 'OSQP Documentation',\n     [author], 1)\n]\n\n# -- Options for breathe ---------------------------------------\n\n# Generate doxygen documentation\nsubprocess.call('doxygen doxygen.conf', shell=True)\n\nbreathe_projects = {\"osqp\": \"doxygen_out/xml/\"}\nbreathe_default_project = \"osqp\"\n\n\n\n# -- Options for Texinfo output -------------------------------------------\n\n# Grouping the document tree into Texinfo files. 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    "path": "osqp/docs/contributing/index.rst",
    "content": "Contributing\n=============\n\nOSQP is an open-source project open to any academic or commercial applications.\nContributions are welcome as `GitHub pull requests <https://help.github.com/articles/creating-a-pull-request/>`_ in any part of the project such as\n\n* algorithm developments\n* interfaces to other languages\n* compatibility with new architectures\n* linear system solvers interfaces\n\n\n.. _interfacing_new_linear_system_solvers :\n\nInterfacing new linear system solvers\n-------------------------------------\nOSQP is designed to be easily interfaced to new linear system solvers via dynamic library loading.\nTo add a linear system solver interface you need to edit the :code:`lin_sys/` directory subfolder :code:`direct/` or :code:`indirect/` depending on the type of solver.\nCreate a subdirectory with your solver name with four files:\n\n* Dynamic library loading: :code:`mysolver_loader.c` and :code:`mysolver_loader.h`.\n* Linear system solution: :code:`mysolver.c` and :code:`mysolver.h`.\n\nWe suggest you to have a look at the `MKL Pardiso solver interface <https://github.com/oxfordcontrol/osqp/tree/master/lin_sys/direct/pardiso>`_ for more details.\n\nDynamic library loading\n^^^^^^^^^^^^^^^^^^^^^^^\nIn this part define the methods to load the shared library and obtain the functions required to solve the linear system.\nThe main functions to be exported are :code:`lh_load_mysolver(const char* libname)` and :code:`lh_unload_mysolver()`.\nIn addition, the file :code:`mysolver_loader.c` must define static function pointers to the shared library functions to be loaded.\n\nLinear system solution\n^^^^^^^^^^^^^^^^^^^^^^\nIn this part we define the core of the interface: **linear system solver object**.\nThe main functions are the external method\n\n* :code:`init_linsys_solver_mysolver`: create the instance and perform the setup\n\nand the internal methods of the object\n\n* :code:`free_linsys_solver_mysolver`: free the instance\n* :code:`solve_linsys_mysolver`: solve the linear system\n* :code:`update_matrices`: update problem matrices\n* :code:`update_rho_vec`: update :math:`\\rho` as a diagonal vector.\n\nAfter the initializations these functions are assigned to the internal pointers so that, for an instance :code:`s` they can be called as :code:`s->free`, :code:`s->solve`, :code:`s->update_matrices` and :code:`s->update_rho_vec`.\n\nThe linear system solver object is defined in :code:`mysolver.h` as follows\n\n.. code:: c\n\n        typedef struct mysolver mysolver_solver;\n\n        struct mysolver {\n            // Methods\n            enum linsys_solver_type type; // Linear system solver defined in constants.h\n\n            c_int (*solve)(struct mysolver * self, c_float * b);\n            void (*free)(struct mysolver * self);\n            c_int (*update_matrices)(struct mysolver * self, const csc *P, const csc *A);\n            c_int (*update_rho_vec)(struct mysolver * self, const c_float * rho_vec);\n\n            // Attributes\n            c_int nthreads; // Number of threads used (required!)\n\n            // Internal attributes of the solver\n            ...\n\n            // Internal attributes required for matrix updates\n            c_int * Pdiag_idx, Pdiag_n;  ///< index and number of diagonal elements in P\n            c_int * PtoKKT, * AtoKKT;    ///< Index of elements from P and A to KKT matrix\n            c_int * rhotoKKT;            ///< Index of rho places in KKT matrix\n            ...\n\n        };\n\n        // Initialize mysolver solver\n        c_int init_linsys_solver_mysolver(mysolver_solver ** s, const csc * P, const csc * A, c_float sigma, c_float * rho_vec, c_int polish);\n\n        // Solve linear system and store result in b\n        c_int solve_linsys_mysolver(mysolver_solver * s, c_float * b);\n\n         // Update linear system solver matrices\n        c_int update_linsys_solver_matrices_mysolver(mysolver_solver * s, const csc *P, const csc *A);\n\n        // Update rho_vec parameter in linear system solver structure\n        c_int update_linsys_solver_rho_vec_mysolver(mysolver_solver * s, const c_float * rho_vec);\n\n        // Free linear system solver\n        void free_linsys_solver_mysolver(mysolver_solver * s);\n\n\nThe function details are coded in the :code:`mysolver.c` file.\n"
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If set\n# to NO, structs, classes, and unions are shown on a separate page (for HTML and\n# Man pages) or section (for LaTeX and RTF).\n# The default value is: NO.\n\nINLINE_SIMPLE_STRUCTS  = NO\n\n# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or\n# enum is documented as struct, union, or enum with the name of the typedef. So\n# typedef struct TypeS {} TypeT, will appear in the documentation as a struct\n# with name TypeT. When disabled the typedef will appear as a member of a file,\n# namespace, or class. And the struct will be named TypeS. This can typically be\n# useful for C code in case the coding convention dictates that all compound\n# types are typedef'ed and only the typedef is referenced, never the tag name.\n# The default value is: NO.\n\nTYPEDEF_HIDES_STRUCT   = NO\n\n# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This\n# cache is used to resolve symbols given their name and scope. Since this can be\n# an expensive process and often the same symbol appears multiple times in the\n# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small\n# doxygen will become slower. If the cache is too large, memory is wasted. The\n# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range\n# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536\n# symbols. At the end of a run doxygen will report the cache usage and suggest\n# the optimal cache size from a speed point of view.\n# Minimum value: 0, maximum value: 9, default value: 0.\n\nLOOKUP_CACHE_SIZE      = 0\n\n#---------------------------------------------------------------------------\n# Build related configuration options\n#---------------------------------------------------------------------------\n\n# If the EXTRACT_ALL tag is set to YES, doxygen will assume all entities in\n# documentation are documented, even if no documentation was available. Private\n# class members and static file members will be hidden unless the\n# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES.\n# Note: This will also disable the warnings about undocumented members that are\n# normally produced when WARNINGS is set to YES.\n# The default value is: NO.\n\nEXTRACT_ALL            = YES\n\n# If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will\n# be included in the documentation.\n# The default value is: NO.\n\nEXTRACT_PRIVATE        = NO\n\n# If the EXTRACT_PACKAGE tag is set to YES, all members with package or internal\n# scope will be included in the documentation.\n# The default value is: NO.\n\nEXTRACT_PACKAGE        = NO\n\n# If the EXTRACT_STATIC tag is set to YES, all static members of a file will be\n# included in the documentation.\n# The default value is: NO.\n\nEXTRACT_STATIC         = NO\n\n# If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined\n# locally in source files will be included in the documentation. If set to NO,\n# only classes defined in header files are included. Does not have any effect\n# for Java sources.\n# The default value is: YES.\n\nEXTRACT_LOCAL_CLASSES  = YES\n\n# This flag is only useful for Objective-C code. If set to YES, local methods,\n# which are defined in the implementation section but not in the interface are\n# included in the documentation. If set to NO, only methods in the interface are\n# included.\n# The default value is: NO.\n\nEXTRACT_LOCAL_METHODS  = NO\n\n# If this flag is set to YES, the members of anonymous namespaces will be\n# extracted and appear in the documentation as a namespace called\n# 'anonymous_namespace{file}', where file will be replaced with the base name of\n# the file that contains the anonymous namespace. By default anonymous namespace\n# are hidden.\n# The default value is: NO.\n\nEXTRACT_ANON_NSPACES   = NO\n\n# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all\n# undocumented members inside documented classes or files. If set to NO these\n# members will be included in the various overviews, but no documentation\n# section is generated. This option has no effect if EXTRACT_ALL is enabled.\n# The default value is: NO.\n\nHIDE_UNDOC_MEMBERS     = NO\n\n# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all\n# undocumented classes that are normally visible in the class hierarchy. If set\n# to NO, these classes will be included in the various overviews. This option\n# has no effect if EXTRACT_ALL is enabled.\n# The default value is: NO.\n\nHIDE_UNDOC_CLASSES     = NO\n\n# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend\n# (class|struct|union) declarations. If set to NO, these declarations will be\n# included in the documentation.\n# The default value is: NO.\n\nHIDE_FRIEND_COMPOUNDS  = NO\n\n# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any\n# documentation blocks found inside the body of a function. If set to NO, these\n# blocks will be appended to the function's detailed documentation block.\n# The default value is: NO.\n\nHIDE_IN_BODY_DOCS      = NO\n\n# The INTERNAL_DOCS tag determines if documentation that is typed after a\n# \\internal command is included. If the tag is set to NO then the documentation\n# will be excluded. Set it to YES to include the internal documentation.\n# The default value is: NO.\n\nINTERNAL_DOCS          = NO\n\n# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file\n# names in lower-case letters. If set to YES, upper-case letters are also\n# allowed. This is useful if you have classes or files whose names only differ\n# in case and if your file system supports case sensitive file names. Windows\n# and Mac users are advised to set this option to NO.\n# The default value is: system dependent.\n\nCASE_SENSE_NAMES       = NO\n\n# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with\n# their full class and namespace scopes in the documentation. If set to YES, the\n# scope will be hidden.\n# The default value is: NO.\n\nHIDE_SCOPE_NAMES       = NO\n\n# If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will\n# append additional text to a page's title, such as Class Reference. If set to\n# YES the compound reference will be hidden.\n# The default value is: NO.\n\nHIDE_COMPOUND_REFERENCE= NO\n\n# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of\n# the files that are included by a file in the documentation of that file.\n# The default value is: YES.\n\nSHOW_INCLUDE_FILES     = YES\n\n# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each\n# grouped member an include statement to the documentation, telling the reader\n# which file to include in order to use the member.\n# The default value is: NO.\n\nSHOW_GROUPED_MEMB_INC  = NO\n\n# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include\n# files with double quotes in the documentation rather than with sharp brackets.\n# The default value is: NO.\n\nFORCE_LOCAL_INCLUDES   = NO\n\n# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the\n# documentation for inline members.\n# The default value is: YES.\n\nINLINE_INFO            = YES\n\n# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the\n# (detailed) documentation of file and class members alphabetically by member\n# name. If set to NO, the members will appear in declaration order.\n# The default value is: YES.\n\nSORT_MEMBER_DOCS       = YES\n\n# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief\n# descriptions of file, namespace and class members alphabetically by member\n# name. If set to NO, the members will appear in declaration order. Note that\n# this will also influence the order of the classes in the class list.\n# The default value is: NO.\n\nSORT_BRIEF_DOCS        = NO\n\n# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the\n# (brief and detailed) documentation of class members so that constructors and\n# destructors are listed first. If set to NO the constructors will appear in the\n# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS.\n# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief\n# member documentation.\n# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting\n# detailed member documentation.\n# The default value is: NO.\n\nSORT_MEMBERS_CTORS_1ST = NO\n\n# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy\n# of group names into alphabetical order. If set to NO the group names will\n# appear in their defined order.\n# The default value is: NO.\n\nSORT_GROUP_NAMES       = NO\n\n# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by\n# fully-qualified names, including namespaces. If set to NO, the class list will\n# be sorted only by class name, not including the namespace part.\n# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES.\n# Note: This option applies only to the class list, not to the alphabetical\n# list.\n# The default value is: NO.\n\nSORT_BY_SCOPE_NAME     = NO\n\n# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper\n# type resolution of all parameters of a function it will reject a match between\n# the prototype and the implementation of a member function even if there is\n# only one candidate or it is obvious which candidate to choose by doing a\n# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still\n# accept a match between prototype and implementation in such cases.\n# The default value is: NO.\n\nSTRICT_PROTO_MATCHING  = NO\n\n# The GENERATE_TODOLIST tag can be used to enable (YES) or disable (NO) the todo\n# list. This list is created by putting \\todo commands in the documentation.\n# The default value is: YES.\n\nGENERATE_TODOLIST      = YES\n\n# The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test\n# list. This list is created by putting \\test commands in the documentation.\n# The default value is: YES.\n\nGENERATE_TESTLIST      = YES\n\n# The GENERATE_BUGLIST tag can be used to enable (YES) or disable (NO) the bug\n# list. This list is created by putting \\bug commands in the documentation.\n# The default value is: YES.\n\nGENERATE_BUGLIST       = YES\n\n# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or disable (NO)\n# the deprecated list. This list is created by putting \\deprecated commands in\n# the documentation.\n# The default value is: YES.\n\nGENERATE_DEPRECATEDLIST= YES\n\n# The ENABLED_SECTIONS tag can be used to enable conditional documentation\n# sections, marked by \\if <section_label> ... \\endif and \\cond <section_label>\n# ... \\endcond blocks.\n\nENABLED_SECTIONS       =\n\n# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the\n# initial value of a variable or macro / define can have for it to appear in the\n# documentation. If the initializer consists of more lines than specified here\n# it will be hidden. Use a value of 0 to hide initializers completely. The\n# appearance of the value of individual variables and macros / defines can be\n# controlled using \\showinitializer or \\hideinitializer command in the\n# documentation regardless of this setting.\n# Minimum value: 0, maximum value: 10000, default value: 30.\n\nMAX_INITIALIZER_LINES  = 30\n\n# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at\n# the bottom of the documentation of classes and structs. If set to YES, the\n# list will mention the files that were used to generate the documentation.\n# The default value is: YES.\n\nSHOW_USED_FILES        = YES\n\n# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This\n# will remove the Files entry from the Quick Index and from the Folder Tree View\n# (if specified).\n# The default value is: YES.\n\nSHOW_FILES             = YES\n\n# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces\n# page. This will remove the Namespaces entry from the Quick Index and from the\n# Folder Tree View (if specified).\n# The default value is: YES.\n\nSHOW_NAMESPACES        = YES\n\n# The FILE_VERSION_FILTER tag can be used to specify a program or script that\n# doxygen should invoke to get the current version for each file (typically from\n# the version control system). Doxygen will invoke the program by executing (via\n# popen()) the command command input-file, where command is the value of the\n# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided\n# by doxygen. Whatever the program writes to standard output is used as the file\n# version. For an example see the documentation.\n\nFILE_VERSION_FILTER    =\n\n# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed\n# by doxygen. The layout file controls the global structure of the generated\n# output files in an output format independent way. To create the layout file\n# that represents doxygen's defaults, run doxygen with the -l option. You can\n# optionally specify a file name after the option, if omitted DoxygenLayout.xml\n# will be used as the name of the layout file.\n#\n# Note that if you run doxygen from a directory containing a file called\n# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE\n# tag is left empty.\n\nLAYOUT_FILE            =\n\n# The CITE_BIB_FILES tag can be used to specify one or more bib files containing\n# the reference definitions. This must be a list of .bib files. The .bib\n# extension is automatically appended if omitted. This requires the bibtex tool\n# to be installed. See also http://en.wikipedia.org/wiki/BibTeX for more info.\n# For LaTeX the style of the bibliography can be controlled using\n# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the\n# search path. See also \\cite for info how to create references.\n\nCITE_BIB_FILES         =\n\n#---------------------------------------------------------------------------\n# Configuration options related to warning and progress messages\n#---------------------------------------------------------------------------\n\n# The QUIET tag can be used to turn on/off the messages that are generated to\n# standard output by doxygen. If QUIET is set to YES this implies that the\n# messages are off.\n# The default value is: NO.\n\nQUIET                  = NO\n\n# The WARNINGS tag can be used to turn on/off the warning messages that are\n# generated to standard error (stderr) by doxygen. If WARNINGS is set to YES\n# this implies that the warnings are on.\n#\n# Tip: Turn warnings on while writing the documentation.\n# The default value is: YES.\n\nWARNINGS               = YES\n\n# If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate\n# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag\n# will automatically be disabled.\n# The default value is: YES.\n\nWARN_IF_UNDOCUMENTED   = YES\n\n# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for\n# potential errors in the documentation, such as not documenting some parameters\n# in a documented function, or documenting parameters that don't exist or using\n# markup commands wrongly.\n# The default value is: YES.\n\nWARN_IF_DOC_ERROR      = YES\n\n# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that\n# are documented, but have no documentation for their parameters or return\n# value. If set to NO, doxygen will only warn about wrong or incomplete\n# parameter documentation, but not about the absence of documentation.\n# The default value is: NO.\n\nWARN_NO_PARAMDOC       = NO\n\n# If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when\n# a warning is encountered.\n# The default value is: NO.\n\nWARN_AS_ERROR          = NO\n\n# The WARN_FORMAT tag determines the format of the warning messages that doxygen\n# can produce. The string should contain the $file, $line, and $text tags, which\n# will be replaced by the file and line number from which the warning originated\n# and the warning text. Optionally the format may contain $version, which will\n# be replaced by the version of the file (if it could be obtained via\n# FILE_VERSION_FILTER)\n# The default value is: $file:$line: $text.\n\nWARN_FORMAT            = \"$file:$line: $text\"\n\n# The WARN_LOGFILE tag can be used to specify a file to which warning and error\n# messages should be written. If left blank the output is written to standard\n# error (stderr).\n\nWARN_LOGFILE           =\n\n#---------------------------------------------------------------------------\n# Configuration options related to the input files\n#---------------------------------------------------------------------------\n\n# The INPUT tag is used to specify the files and/or directories that contain\n# documented source files. You may enter file names like myfile.cpp or\n# directories like /usr/src/myproject. Separate the files or directories with\n# spaces. See also FILE_PATTERNS and EXTENSION_MAPPING\n# Note: If this tag is empty the current directory is searched.\n\nINPUT                  = ./../include ./../src\n\n# This tag can be used to specify the character encoding of the source files\n# that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses\n# libiconv (or the iconv built into libc) for the transcoding. See the libiconv\n# documentation (see: http://www.gnu.org/software/libiconv) for the list of\n# possible encodings.\n# The default value is: UTF-8.\n\nINPUT_ENCODING         = UTF-8\n\n# If the value of the INPUT tag contains directories, you can use the\n# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and\n# *.h) to filter out the source-files in the directories.\n#\n# Note that for custom extensions or not directly supported extensions you also\n# need to set EXTENSION_MAPPING for the extension otherwise the files are not\n# read by doxygen.\n#\n# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp,\n# *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h,\n# *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc,\n# *.m, *.markdown, *.md, *.mm, *.dox, *.py, *.pyw, *.f90, *.f95, *.f03, *.f08,\n# *.f, *.for, *.tcl, *.vhd, *.vhdl, *.ucf and *.qsf.\n\nFILE_PATTERNS          = *.c \\\n                         *.cc \\\n                         *.cxx \\\n                         *.cpp \\\n                         *.c++ \\\n                         *.java \\\n                         *.ii \\\n                         *.ixx \\\n                         *.ipp \\\n                         *.i++ \\\n                         *.inl \\\n                         *.idl \\\n                         *.ddl \\\n                         *.odl \\\n                         *.h \\\n                         *.hh \\\n                         *.hxx \\\n                         *.hpp \\\n                         *.h++ \\\n                         *.cs \\\n                         *.d \\\n                         *.php \\\n                         *.php4 \\\n                         *.php5 \\\n                         *.phtml \\\n                         *.inc \\\n                         *.m \\\n                         *.markdown \\\n                         *.md \\\n                         *.mm \\\n                         *.dox \\\n                         *.py \\\n                         *.pyw \\\n                         *.f90 \\\n                         *.f95 \\\n                         *.f03 \\\n                         *.f08 \\\n                         *.f \\\n                         *.for \\\n                         *.tcl \\\n                         *.vhd \\\n                         *.vhdl \\\n                         *.ucf \\\n                         *.qsf\n\n# The RECURSIVE tag can be used to specify whether or not subdirectories should\n# be searched for input files as well.\n# The default value is: NO.\n\nRECURSIVE              = NO\n\n# The EXCLUDE tag can be used to specify files and/or directories that should be\n# excluded from the INPUT source files. This way you can easily exclude a\n# subdirectory from a directory tree whose root is specified with the INPUT tag.\n#\n# Note that relative paths are relative to the directory from which doxygen is\n# run.\n\nEXCLUDE                =\n\n# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or\n# directories that are symbolic links (a Unix file system feature) are excluded\n# from the input.\n# The default value is: NO.\n\nEXCLUDE_SYMLINKS       = NO\n\n# If the value of the INPUT tag contains directories, you can use the\n# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude\n# certain files from those directories.\n#\n# Note that the wildcards are matched against the file with absolute path, so to\n# exclude all test directories for example use the pattern */test/*\n\nEXCLUDE_PATTERNS       =\n\n# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names\n# (namespaces, classes, functions, etc.) that should be excluded from the\n# output. The symbol name can be a fully qualified name, a word, or if the\n# wildcard * is used, a substring. Examples: ANamespace, AClass,\n# AClass::ANamespace, ANamespace::*Test\n#\n# Note that the wildcards are matched against the file with absolute path, so to\n# exclude all test directories use the pattern */test/*\n\nEXCLUDE_SYMBOLS        =\n\n# The EXAMPLE_PATH tag can be used to specify one or more files or directories\n# that contain example code fragments that are included (see the \\include\n# command).\n\nEXAMPLE_PATH           =\n\n# If the value of the EXAMPLE_PATH tag contains directories, you can use the\n# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and\n# *.h) to filter out the source-files in the directories. If left blank all\n# files are included.\n\nEXAMPLE_PATTERNS       = *\n\n# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be\n# searched for input files to be used with the \\include or \\dontinclude commands\n# irrespective of the value of the RECURSIVE tag.\n# The default value is: NO.\n\nEXAMPLE_RECURSIVE      = NO\n\n# The IMAGE_PATH tag can be used to specify one or more files or directories\n# that contain images that are to be included in the documentation (see the\n# \\image command).\n\nIMAGE_PATH             =\n\n# The INPUT_FILTER tag can be used to specify a program that doxygen should\n# invoke to filter for each input file. Doxygen will invoke the filter program\n# by executing (via popen()) the command:\n#\n# <filter> <input-file>\n#\n# where <filter> is the value of the INPUT_FILTER tag, and <input-file> is the\n# name of an input file. Doxygen will then use the output that the filter\n# program writes to standard output. If FILTER_PATTERNS is specified, this tag\n# will be ignored.\n#\n# Note that the filter must not add or remove lines; it is applied before the\n# code is scanned, but not when the output code is generated. If lines are added\n# or removed, the anchors will not be placed correctly.\n#\n# Note that for custom extensions or not directly supported extensions you also\n# need to set EXTENSION_MAPPING for the extension otherwise the files are not\n# properly processed by doxygen.\n\nINPUT_FILTER           =\n\n# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern\n# basis. Doxygen will compare the file name with each pattern and apply the\n# filter if there is a match. The filters are a list of the form: pattern=filter\n# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how\n# filters are used. If the FILTER_PATTERNS tag is empty or if none of the\n# patterns match the file name, INPUT_FILTER is applied.\n#\n# Note that for custom extensions or not directly supported extensions you also\n# need to set EXTENSION_MAPPING for the extension otherwise the files are not\n# properly processed by doxygen.\n\nFILTER_PATTERNS        =\n\n# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using\n# INPUT_FILTER) will also be used to filter the input files that are used for\n# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES).\n# The default value is: NO.\n\nFILTER_SOURCE_FILES    = NO\n\n# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file\n# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and\n# it is also possible to disable source filtering for a specific pattern using\n# *.ext= (so without naming a filter).\n# This tag requires that the tag FILTER_SOURCE_FILES is set to YES.\n\nFILTER_SOURCE_PATTERNS =\n\n# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that\n# is part of the input, its contents will be placed on the main page\n# (index.html). This can be useful if you have a project on for instance GitHub\n# and want to reuse the introduction page also for the doxygen output.\n\nUSE_MDFILE_AS_MAINPAGE =\n\n#---------------------------------------------------------------------------\n# Configuration options related to source browsing\n#---------------------------------------------------------------------------\n\n# If the SOURCE_BROWSER tag is set to YES then a list of source files will be\n# generated. Documented entities will be cross-referenced with these sources.\n#\n# Note: To get rid of all source code in the generated output, make sure that\n# also VERBATIM_HEADERS is set to NO.\n# The default value is: NO.\n\nSOURCE_BROWSER         = NO\n\n# Setting the INLINE_SOURCES tag to YES will include the body of functions,\n# classes and enums directly into the documentation.\n# The default value is: NO.\n\nINLINE_SOURCES         = NO\n\n# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any\n# special comment blocks from generated source code fragments. Normal C, C++ and\n# Fortran comments will always remain visible.\n# The default value is: YES.\n\nSTRIP_CODE_COMMENTS    = YES\n\n# If the REFERENCED_BY_RELATION tag is set to YES then for each documented\n# function all documented functions referencing it will be listed.\n# The default value is: NO.\n\nREFERENCED_BY_RELATION = NO\n\n# If the REFERENCES_RELATION tag is set to YES then for each documented function\n# all documented entities called/used by that function will be listed.\n# The default value is: NO.\n\nREFERENCES_RELATION    = NO\n\n# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set\n# to YES then the hyperlinks from functions in REFERENCES_RELATION and\n# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will\n# link to the documentation.\n# The default value is: YES.\n\nREFERENCES_LINK_SOURCE = YES\n\n# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the\n# source code will show a tooltip with additional information such as prototype,\n# brief description and links to the definition and documentation. Since this\n# will make the HTML file larger and loading of large files a bit slower, you\n# can opt to disable this feature.\n# The default value is: YES.\n# This tag requires that the tag SOURCE_BROWSER is set to YES.\n\nSOURCE_TOOLTIPS        = YES\n\n# If the USE_HTAGS tag is set to YES then the references to source code will\n# point to the HTML generated by the htags(1) tool instead of doxygen built-in\n# source browser. The htags tool is part of GNU's global source tagging system\n# (see http://www.gnu.org/software/global/global.html). You will need version\n# 4.8.6 or higher.\n#\n# To use it do the following:\n# - Install the latest version of global\n# - Enable SOURCE_BROWSER and USE_HTAGS in the config file\n# - Make sure the INPUT points to the root of the source tree\n# - Run doxygen as normal\n#\n# Doxygen will invoke htags (and that will in turn invoke gtags), so these\n# tools must be available from the command line (i.e. in the search path).\n#\n# The result: instead of the source browser generated by doxygen, the links to\n# source code will now point to the output of htags.\n# The default value is: NO.\n# This tag requires that the tag SOURCE_BROWSER is set to YES.\n\nUSE_HTAGS              = NO\n\n# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a\n# verbatim copy of the header file for each class for which an include is\n# specified. Set to NO to disable this.\n# See also: Section \\class.\n# The default value is: YES.\n\nVERBATIM_HEADERS       = YES\n\n#---------------------------------------------------------------------------\n# Configuration options related to the alphabetical class index\n#---------------------------------------------------------------------------\n\n# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all\n# compounds will be generated. Enable this if the project contains a lot of\n# classes, structs, unions or interfaces.\n# The default value is: YES.\n\nALPHABETICAL_INDEX     = YES\n\n# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in\n# which the alphabetical index list will be split.\n# Minimum value: 1, maximum value: 20, default value: 5.\n# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.\n\nCOLS_IN_ALPHA_INDEX    = 5\n\n# In case all classes in a project start with a common prefix, all classes will\n# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag\n# can be used to specify a prefix (or a list of prefixes) that should be ignored\n# while generating the index headers.\n# This tag requires that the tag ALPHABETICAL_INDEX is set to YES.\n\nIGNORE_PREFIX          =\n\n#---------------------------------------------------------------------------\n# Configuration options related to the HTML output\n#---------------------------------------------------------------------------\n\n# If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output\n# The default value is: YES.\n\nGENERATE_HTML          = YES\n\n# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a\n# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of\n# it.\n# The default directory is: html.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_OUTPUT            = html\n\n# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each\n# generated HTML page (for example: .htm, .php, .asp).\n# The default value is: .html.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_FILE_EXTENSION    = .html\n\n# The HTML_HEADER tag can be used to specify a user-defined HTML header file for\n# each generated HTML page. If the tag is left blank doxygen will generate a\n# standard header.\n#\n# To get valid HTML the header file that includes any scripts and style sheets\n# that doxygen needs, which is dependent on the configuration options used (e.g.\n# the setting GENERATE_TREEVIEW). It is highly recommended to start with a\n# default header using\n# doxygen -w html new_header.html new_footer.html new_stylesheet.css\n# YourConfigFile\n# and then modify the file new_header.html. See also section \"Doxygen usage\"\n# for information on how to generate the default header that doxygen normally\n# uses.\n# Note: The header is subject to change so you typically have to regenerate the\n# default header when upgrading to a newer version of doxygen. For a description\n# of the possible markers and block names see the documentation.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_HEADER            =\n\n# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each\n# generated HTML page. If the tag is left blank doxygen will generate a standard\n# footer. See HTML_HEADER for more information on how to generate a default\n# footer and what special commands can be used inside the footer. See also\n# section \"Doxygen usage\" for information on how to generate the default footer\n# that doxygen normally uses.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_FOOTER            =\n\n# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style\n# sheet that is used by each HTML page. It can be used to fine-tune the look of\n# the HTML output. If left blank doxygen will generate a default style sheet.\n# See also section \"Doxygen usage\" for information on how to generate the style\n# sheet that doxygen normally uses.\n# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as\n# it is more robust and this tag (HTML_STYLESHEET) will in the future become\n# obsolete.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_STYLESHEET        =\n\n# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined\n# cascading style sheets that are included after the standard style sheets\n# created by doxygen. Using this option one can overrule certain style aspects.\n# This is preferred over using HTML_STYLESHEET since it does not replace the\n# standard style sheet and is therefore more robust against future updates.\n# Doxygen will copy the style sheet files to the output directory.\n# Note: The order of the extra style sheet files is of importance (e.g. the last\n# style sheet in the list overrules the setting of the previous ones in the\n# list). For an example see the documentation.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_EXTRA_STYLESHEET  =\n\n# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or\n# other source files which should be copied to the HTML output directory. Note\n# that these files will be copied to the base HTML output directory. Use the\n# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these\n# files. In the HTML_STYLESHEET file, use the file name only. Also note that the\n# files will be copied as-is; there are no commands or markers available.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_EXTRA_FILES       =\n\n# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen\n# will adjust the colors in the style sheet and background images according to\n# this color. Hue is specified as an angle on a colorwheel, see\n# http://en.wikipedia.org/wiki/Hue for more information. For instance the value\n# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300\n# purple, and 360 is red again.\n# Minimum value: 0, maximum value: 359, default value: 220.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_COLORSTYLE_HUE    = 220\n\n# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors\n# in the HTML output. For a value of 0 the output will use grayscales only. A\n# value of 255 will produce the most vivid colors.\n# Minimum value: 0, maximum value: 255, default value: 100.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_COLORSTYLE_SAT    = 100\n\n# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the\n# luminance component of the colors in the HTML output. Values below 100\n# gradually make the output lighter, whereas values above 100 make the output\n# darker. The value divided by 100 is the actual gamma applied, so 80 represents\n# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not\n# change the gamma.\n# Minimum value: 40, maximum value: 240, default value: 80.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_COLORSTYLE_GAMMA  = 80\n\n# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML\n# page will contain the date and time when the page was generated. Setting this\n# to YES can help to show when doxygen was last run and thus if the\n# documentation is up to date.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_TIMESTAMP         = NO\n\n# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML\n# documentation will contain sections that can be hidden and shown after the\n# page has loaded.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_DYNAMIC_SECTIONS  = NO\n\n# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries\n# shown in the various tree structured indices initially; the user can expand\n# and collapse entries dynamically later on. Doxygen will expand the tree to\n# such a level that at most the specified number of entries are visible (unless\n# a fully collapsed tree already exceeds this amount). So setting the number of\n# entries 1 will produce a full collapsed tree by default. 0 is a special value\n# representing an infinite number of entries and will result in a full expanded\n# tree by default.\n# Minimum value: 0, maximum value: 9999, default value: 100.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nHTML_INDEX_NUM_ENTRIES = 100\n\n# If the GENERATE_DOCSET tag is set to YES, additional index files will be\n# generated that can be used as input for Apple's Xcode 3 integrated development\n# environment (see: http://developer.apple.com/tools/xcode/), introduced with\n# OSX 10.5 (Leopard). To create a documentation set, doxygen will generate a\n# Makefile in the HTML output directory. Running make will produce the docset in\n# that directory and running make install will install the docset in\n# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at\n# startup. See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html\n# for more information.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nGENERATE_DOCSET        = NO\n\n# This tag determines the name of the docset feed. A documentation feed provides\n# an umbrella under which multiple documentation sets from a single provider\n# (such as a company or product suite) can be grouped.\n# The default value is: Doxygen generated docs.\n# This tag requires that the tag GENERATE_DOCSET is set to YES.\n\nDOCSET_FEEDNAME        = \"Doxygen generated docs\"\n\n# This tag specifies a string that should uniquely identify the documentation\n# set bundle. This should be a reverse domain-name style string, e.g.\n# com.mycompany.MyDocSet. Doxygen will append .docset to the name.\n# The default value is: org.doxygen.Project.\n# This tag requires that the tag GENERATE_DOCSET is set to YES.\n\nDOCSET_BUNDLE_ID       = org.doxygen.Project\n\n# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify\n# the documentation publisher. This should be a reverse domain-name style\n# string, e.g. com.mycompany.MyDocSet.documentation.\n# The default value is: org.doxygen.Publisher.\n# This tag requires that the tag GENERATE_DOCSET is set to YES.\n\nDOCSET_PUBLISHER_ID    = org.doxygen.Publisher\n\n# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher.\n# The default value is: Publisher.\n# This tag requires that the tag GENERATE_DOCSET is set to YES.\n\nDOCSET_PUBLISHER_NAME  = Publisher\n\n# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three\n# additional HTML index files: index.hhp, index.hhc, and index.hhk. The\n# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop\n# (see: http://www.microsoft.com/en-us/download/details.aspx?id=21138) on\n# Windows.\n#\n# The HTML Help Workshop contains a compiler that can convert all HTML output\n# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML\n# files are now used as the Windows 98 help format, and will replace the old\n# Windows help format (.hlp) on all Windows platforms in the future. Compressed\n# HTML files also contain an index, a table of contents, and you can search for\n# words in the documentation. The HTML workshop also contains a viewer for\n# compressed HTML files.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nGENERATE_HTMLHELP      = NO\n\n# The CHM_FILE tag can be used to specify the file name of the resulting .chm\n# file. You can add a path in front of the file if the result should not be\n# written to the html output directory.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nCHM_FILE               =\n\n# The HHC_LOCATION tag can be used to specify the location (absolute path\n# including file name) of the HTML help compiler (hhc.exe). If non-empty,\n# doxygen will try to run the HTML help compiler on the generated index.hhp.\n# The file has to be specified with full path.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nHHC_LOCATION           =\n\n# The GENERATE_CHI flag controls if a separate .chi index file is generated\n# (YES) or that it should be included in the master .chm file (NO).\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nGENERATE_CHI           = NO\n\n# The CHM_INDEX_ENCODING is used to encode HtmlHelp index (hhk), content (hhc)\n# and project file content.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nCHM_INDEX_ENCODING     =\n\n# The BINARY_TOC flag controls whether a binary table of contents is generated\n# (YES) or a normal table of contents (NO) in the .chm file. Furthermore it\n# enables the Previous and Next buttons.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nBINARY_TOC             = NO\n\n# The TOC_EXPAND flag can be set to YES to add extra items for group members to\n# the table of contents of the HTML help documentation and to the tree view.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTMLHELP is set to YES.\n\nTOC_EXPAND             = NO\n\n# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and\n# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that\n# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help\n# (.qch) of the generated HTML documentation.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nGENERATE_QHP           = NO\n\n# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify\n# the file name of the resulting .qch file. The path specified is relative to\n# the HTML output folder.\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQCH_FILE               =\n\n# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help\n# Project output. For more information please see Qt Help Project / Namespace\n# (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#namespace).\n# The default value is: org.doxygen.Project.\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHP_NAMESPACE          = org.doxygen.Project\n\n# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt\n# Help Project output. For more information please see Qt Help Project / Virtual\n# Folders (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#virtual-\n# folders).\n# The default value is: doc.\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHP_VIRTUAL_FOLDER     = doc\n\n# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom\n# filter to add. For more information please see Qt Help Project / Custom\n# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-\n# filters).\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHP_CUST_FILTER_NAME   =\n\n# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the\n# custom filter to add. For more information please see Qt Help Project / Custom\n# Filters (see: http://qt-project.org/doc/qt-4.8/qthelpproject.html#custom-\n# filters).\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHP_CUST_FILTER_ATTRS  =\n\n# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this\n# project's filter section matches. Qt Help Project / Filter Attributes (see:\n# http://qt-project.org/doc/qt-4.8/qthelpproject.html#filter-attributes).\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHP_SECT_FILTER_ATTRS  =\n\n# The QHG_LOCATION tag can be used to specify the location of Qt's\n# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the\n# generated .qhp file.\n# This tag requires that the tag GENERATE_QHP is set to YES.\n\nQHG_LOCATION           =\n\n# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be\n# generated, together with the HTML files, they form an Eclipse help plugin. To\n# install this plugin and make it available under the help contents menu in\n# Eclipse, the contents of the directory containing the HTML and XML files needs\n# to be copied into the plugins directory of eclipse. The name of the directory\n# within the plugins directory should be the same as the ECLIPSE_DOC_ID value.\n# After copying Eclipse needs to be restarted before the help appears.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nGENERATE_ECLIPSEHELP   = NO\n\n# A unique identifier for the Eclipse help plugin. When installing the plugin\n# the directory name containing the HTML and XML files should also have this\n# name. Each documentation set should have its own identifier.\n# The default value is: org.doxygen.Project.\n# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES.\n\nECLIPSE_DOC_ID         = org.doxygen.Project\n\n# If you want full control over the layout of the generated HTML pages it might\n# be necessary to disable the index and replace it with your own. The\n# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top\n# of each HTML page. A value of NO enables the index and the value YES disables\n# it. Since the tabs in the index contain the same information as the navigation\n# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nDISABLE_INDEX          = NO\n\n# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index\n# structure should be generated to display hierarchical information. If the tag\n# value is set to YES, a side panel will be generated containing a tree-like\n# index structure (just like the one that is generated for HTML Help). For this\n# to work a browser that supports JavaScript, DHTML, CSS and frames is required\n# (i.e. any modern browser). Windows users are probably better off using the\n# HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can\n# further fine-tune the look of the index. As an example, the default style\n# sheet generated by doxygen has an example that shows how to put an image at\n# the root of the tree instead of the PROJECT_NAME. Since the tree basically has\n# the same information as the tab index, you could consider setting\n# DISABLE_INDEX to YES when enabling this option.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nGENERATE_TREEVIEW      = NO\n\n# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that\n# doxygen will group on one line in the generated HTML documentation.\n#\n# Note that a value of 0 will completely suppress the enum values from appearing\n# in the overview section.\n# Minimum value: 0, maximum value: 20, default value: 4.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nENUM_VALUES_PER_LINE   = 4\n\n# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used\n# to set the initial width (in pixels) of the frame in which the tree is shown.\n# Minimum value: 0, maximum value: 1500, default value: 250.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nTREEVIEW_WIDTH         = 250\n\n# If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to\n# external symbols imported via tag files in a separate window.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nEXT_LINKS_IN_WINDOW    = NO\n\n# Use this tag to change the font size of LaTeX formulas included as images in\n# the HTML documentation. When you change the font size after a successful\n# doxygen run you need to manually remove any form_*.png images from the HTML\n# output directory to force them to be regenerated.\n# Minimum value: 8, maximum value: 50, default value: 10.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nFORMULA_FONTSIZE       = 10\n\n# Use the FORMULA_TRANPARENT tag to determine whether or not the images\n# generated for formulas are transparent PNGs. Transparent PNGs are not\n# supported properly for IE 6.0, but are supported on all modern browsers.\n#\n# Note that when changing this option you need to delete any form_*.png files in\n# the HTML output directory before the changes have effect.\n# The default value is: YES.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nFORMULA_TRANSPARENT    = YES\n\n# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see\n# http://www.mathjax.org) which uses client side Javascript for the rendering\n# instead of using pre-rendered bitmaps. Use this if you do not have LaTeX\n# installed or if you want to formulas look prettier in the HTML output. When\n# enabled you may also need to install MathJax separately and configure the path\n# to it using the MATHJAX_RELPATH option.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nUSE_MATHJAX            = YES\n\n# When MathJax is enabled you can set the default output format to be used for\n# the MathJax output. See the MathJax site (see:\n# http://docs.mathjax.org/en/latest/output.html) for more details.\n# Possible values are: HTML-CSS (which is slower, but has the best\n# compatibility), NativeMML (i.e. MathML) and SVG.\n# The default value is: HTML-CSS.\n# This tag requires that the tag USE_MATHJAX is set to YES.\n\nMATHJAX_FORMAT         = HTML-CSS\n\n# When MathJax is enabled you need to specify the location relative to the HTML\n# output directory using the MATHJAX_RELPATH option. The destination directory\n# should contain the MathJax.js script. For instance, if the mathjax directory\n# is located at the same level as the HTML output directory, then\n# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax\n# Content Delivery Network so you can quickly see the result without installing\n# MathJax. However, it is strongly recommended to install a local copy of\n# MathJax from http://www.mathjax.org before deployment.\n# The default value is: http://cdn.mathjax.org/mathjax/latest.\n# This tag requires that the tag USE_MATHJAX is set to YES.\n\nMATHJAX_RELPATH        = http://cdn.mathjax.org/mathjax/latest\n\n# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax\n# extension names that should be enabled during MathJax rendering. For example\n# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols\n# This tag requires that the tag USE_MATHJAX is set to YES.\n\nMATHJAX_EXTENSIONS     =\n\n# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces\n# of code that will be used on startup of the MathJax code. See the MathJax site\n# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an\n# example see the documentation.\n# This tag requires that the tag USE_MATHJAX is set to YES.\n\nMATHJAX_CODEFILE       =\n\n# When the SEARCHENGINE tag is enabled doxygen will generate a search box for\n# the HTML output. The underlying search engine uses javascript and DHTML and\n# should work on any modern browser. Note that when using HTML help\n# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET)\n# there is already a search function so this one should typically be disabled.\n# For large projects the javascript based search engine can be slow, then\n# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to\n# search using the keyboard; to jump to the search box use <access key> + S\n# (what the <access key> is depends on the OS and browser, but it is typically\n# <CTRL>, <ALT>/<option>, or both). Inside the search box use the <cursor down\n# key> to jump into the search results window, the results can be navigated\n# using the <cursor keys>. Press <Enter> to select an item or <escape> to cancel\n# the search. The filter options can be selected when the cursor is inside the\n# search box by pressing <Shift>+<cursor down>. Also here use the <cursor keys>\n# to select a filter and <Enter> or <escape> to activate or cancel the filter\n# option.\n# The default value is: YES.\n# This tag requires that the tag GENERATE_HTML is set to YES.\n\nSEARCHENGINE           = YES\n\n# When the SERVER_BASED_SEARCH tag is enabled the search engine will be\n# implemented using a web server instead of a web client using Javascript. There\n# are two flavors of web server based searching depending on the EXTERNAL_SEARCH\n# setting. When disabled, doxygen will generate a PHP script for searching and\n# an index file used by the script. When EXTERNAL_SEARCH is enabled the indexing\n# and searching needs to be provided by external tools. See the section\n# \"External Indexing and Searching\" for details.\n# The default value is: NO.\n# This tag requires that the tag SEARCHENGINE is set to YES.\n\nSERVER_BASED_SEARCH    = NO\n\n# When EXTERNAL_SEARCH tag is enabled doxygen will no longer generate the PHP\n# script for searching. Instead the search results are written to an XML file\n# which needs to be processed by an external indexer. Doxygen will invoke an\n# external search engine pointed to by the SEARCHENGINE_URL option to obtain the\n# search results.\n#\n# Doxygen ships with an example indexer (doxyindexer) and search engine\n# (doxysearch.cgi) which are based on the open source search engine library\n# Xapian (see: http://xapian.org/).\n#\n# See the section \"External Indexing and Searching\" for details.\n# The default value is: NO.\n# This tag requires that the tag SEARCHENGINE is set to YES.\n\nEXTERNAL_SEARCH        = NO\n\n# The SEARCHENGINE_URL should point to a search engine hosted by a web server\n# which will return the search results when EXTERNAL_SEARCH is enabled.\n#\n# Doxygen ships with an example indexer (doxyindexer) and search engine\n# (doxysearch.cgi) which are based on the open source search engine library\n# Xapian (see: http://xapian.org/). See the section \"External Indexing and\n# Searching\" for details.\n# This tag requires that the tag SEARCHENGINE is set to YES.\n\nSEARCHENGINE_URL       =\n\n# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the unindexed\n# search data is written to a file for indexing by an external tool. With the\n# SEARCHDATA_FILE tag the name of this file can be specified.\n# The default file is: searchdata.xml.\n# This tag requires that the tag SEARCHENGINE is set to YES.\n\nSEARCHDATA_FILE        = searchdata.xml\n\n# When SERVER_BASED_SEARCH and EXTERNAL_SEARCH are both enabled the\n# EXTERNAL_SEARCH_ID tag can be used as an identifier for the project. This is\n# useful in combination with EXTRA_SEARCH_MAPPINGS to search through multiple\n# projects and redirect the results back to the right project.\n# This tag requires that the tag SEARCHENGINE is set to YES.\n\nEXTERNAL_SEARCH_ID     =\n\n# The EXTRA_SEARCH_MAPPINGS tag can be used to enable searching through doxygen\n# projects other than the one defined by this configuration file, but that are\n# all added to the same external search index. Each project needs to have a\n# unique id set via EXTERNAL_SEARCH_ID. The search mapping then maps the id of\n# to a relative location where the documentation can be found. The format is:\n# EXTRA_SEARCH_MAPPINGS = tagname1=loc1 tagname2=loc2 ...\n# This tag requires that the tag SEARCHENGINE is set to YES.\n\nEXTRA_SEARCH_MAPPINGS  =\n\n#---------------------------------------------------------------------------\n# Configuration options related to the LaTeX output\n#---------------------------------------------------------------------------\n\n# If the GENERATE_LATEX tag is set to YES, doxygen will generate LaTeX output.\n# The default value is: YES.\n\nGENERATE_LATEX         = NO\n\n# The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. If a\n# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of\n# it.\n# The default directory is: latex.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_OUTPUT           = latex\n\n# The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be\n# invoked.\n#\n# Note that when enabling USE_PDFLATEX this option is only used for generating\n# bitmaps for formulas in the HTML output, but not in the Makefile that is\n# written to the output directory.\n# The default file is: latex.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_CMD_NAME         = latex\n\n# The MAKEINDEX_CMD_NAME tag can be used to specify the command name to generate\n# index for LaTeX.\n# The default file is: makeindex.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nMAKEINDEX_CMD_NAME     = makeindex\n\n# If the COMPACT_LATEX tag is set to YES, doxygen generates more compact LaTeX\n# documents. This may be useful for small projects and may help to save some\n# trees in general.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nCOMPACT_LATEX          = NO\n\n# The PAPER_TYPE tag can be used to set the paper type that is used by the\n# printer.\n# Possible values are: a4 (210 x 297 mm), letter (8.5 x 11 inches), legal (8.5 x\n# 14 inches) and executive (7.25 x 10.5 inches).\n# The default value is: a4.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nPAPER_TYPE             = a4\n\n# The EXTRA_PACKAGES tag can be used to specify one or more LaTeX package names\n# that should be included in the LaTeX output. The package can be specified just\n# by its name or with the correct syntax as to be used with the LaTeX\n# \\usepackage command. To get the times font for instance you can specify :\n# EXTRA_PACKAGES=times or EXTRA_PACKAGES={times}\n# To use the option intlimits with the amsmath package you can specify:\n# EXTRA_PACKAGES=[intlimits]{amsmath}\n# If left blank no extra packages will be included.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nEXTRA_PACKAGES         =\n\n# The LATEX_HEADER tag can be used to specify a personal LaTeX header for the\n# generated LaTeX document. The header should contain everything until the first\n# chapter. If it is left blank doxygen will generate a standard header. See\n# section \"Doxygen usage\" for information on how to let doxygen write the\n# default header to a separate file.\n#\n# Note: Only use a user-defined header if you know what you are doing! The\n# following commands have a special meaning inside the header: $title,\n# $datetime, $date, $doxygenversion, $projectname, $projectnumber,\n# $projectbrief, $projectlogo. Doxygen will replace $title with the empty\n# string, for the replacement values of the other commands the user is referred\n# to HTML_HEADER.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_HEADER           =\n\n# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for the\n# generated LaTeX document. The footer should contain everything after the last\n# chapter. If it is left blank doxygen will generate a standard footer. See\n# LATEX_HEADER for more information on how to generate a default footer and what\n# special commands can be used inside the footer.\n#\n# Note: Only use a user-defined footer if you know what you are doing!\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_FOOTER           =\n\n# The LATEX_EXTRA_STYLESHEET tag can be used to specify additional user-defined\n# LaTeX style sheets that are included after the standard style sheets created\n# by doxygen. Using this option one can overrule certain style aspects. Doxygen\n# will copy the style sheet files to the output directory.\n# Note: The order of the extra style sheet files is of importance (e.g. the last\n# style sheet in the list overrules the setting of the previous ones in the\n# list).\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_EXTRA_STYLESHEET =\n\n# The LATEX_EXTRA_FILES tag can be used to specify one or more extra images or\n# other source files which should be copied to the LATEX_OUTPUT output\n# directory. Note that the files will be copied as-is; there are no commands or\n# markers available.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_EXTRA_FILES      =\n\n# If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated is\n# prepared for conversion to PDF (using ps2pdf or pdflatex). The PDF file will\n# contain links (just like the HTML output) instead of page references. This\n# makes the output suitable for online browsing using a PDF viewer.\n# The default value is: YES.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nPDF_HYPERLINKS         = YES\n\n# If the USE_PDFLATEX tag is set to YES, doxygen will use pdflatex to generate\n# the PDF file directly from the LaTeX files. Set this option to YES, to get a\n# higher quality PDF documentation.\n# The default value is: YES.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nUSE_PDFLATEX           = YES\n\n# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \\batchmode\n# command to the generated LaTeX files. This will instruct LaTeX to keep running\n# if errors occur, instead of asking the user for help. This option is also used\n# when generating formulas in HTML.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_BATCHMODE        = NO\n\n# If the LATEX_HIDE_INDICES tag is set to YES then doxygen will not include the\n# index chapters (such as File Index, Compound Index, etc.) in the output.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_HIDE_INDICES     = NO\n\n# If the LATEX_SOURCE_CODE tag is set to YES then doxygen will include source\n# code with syntax highlighting in the LaTeX output.\n#\n# Note that which sources are shown also depends on other settings such as\n# SOURCE_BROWSER.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_SOURCE_CODE      = NO\n\n# The LATEX_BIB_STYLE tag can be used to specify the style to use for the\n# bibliography, e.g. plainnat, or ieeetr. See\n# http://en.wikipedia.org/wiki/BibTeX and \\cite for more info.\n# The default value is: plain.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_BIB_STYLE        = plain\n\n# If the LATEX_TIMESTAMP tag is set to YES then the footer of each generated\n# page will contain the date and time when the page was generated. Setting this\n# to NO can help when comparing the output of multiple runs.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_LATEX is set to YES.\n\nLATEX_TIMESTAMP        = NO\n\n#---------------------------------------------------------------------------\n# Configuration options related to the RTF output\n#---------------------------------------------------------------------------\n\n# If the GENERATE_RTF tag is set to YES, doxygen will generate RTF output. The\n# RTF output is optimized for Word 97 and may not look too pretty with other RTF\n# readers/editors.\n# The default value is: NO.\n\nGENERATE_RTF           = NO\n\n# The RTF_OUTPUT tag is used to specify where the RTF docs will be put. If a\n# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of\n# it.\n# The default directory is: rtf.\n# This tag requires that the tag GENERATE_RTF is set to YES.\n\nRTF_OUTPUT             = rtf\n\n# If the COMPACT_RTF tag is set to YES, doxygen generates more compact RTF\n# documents. This may be useful for small projects and may help to save some\n# trees in general.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_RTF is set to YES.\n\nCOMPACT_RTF            = NO\n\n# If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated will\n# contain hyperlink fields. The RTF file will contain links (just like the HTML\n# output) instead of page references. This makes the output suitable for online\n# browsing using Word or some other Word compatible readers that support those\n# fields.\n#\n# Note: WordPad (write) and others do not support links.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_RTF is set to YES.\n\nRTF_HYPERLINKS         = NO\n\n# Load stylesheet definitions from file. Syntax is similar to doxygen's config\n# file, i.e. a series of assignments. You only have to provide replacements,\n# missing definitions are set to their default value.\n#\n# See also section \"Doxygen usage\" for information on how to generate the\n# default style sheet that doxygen normally uses.\n# This tag requires that the tag GENERATE_RTF is set to YES.\n\nRTF_STYLESHEET_FILE    =\n\n# Set optional variables used in the generation of an RTF document. Syntax is\n# similar to doxygen's config file. A template extensions file can be generated\n# using doxygen -e rtf extensionFile.\n# This tag requires that the tag GENERATE_RTF is set to YES.\n\nRTF_EXTENSIONS_FILE    =\n\n# If the RTF_SOURCE_CODE tag is set to YES then doxygen will include source code\n# with syntax highlighting in the RTF output.\n#\n# Note that which sources are shown also depends on other settings such as\n# SOURCE_BROWSER.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_RTF is set to YES.\n\nRTF_SOURCE_CODE        = NO\n\n#---------------------------------------------------------------------------\n# Configuration options related to the man page output\n#---------------------------------------------------------------------------\n\n# If the GENERATE_MAN tag is set to YES, doxygen will generate man pages for\n# classes and files.\n# The default value is: NO.\n\nGENERATE_MAN           = NO\n\n# The MAN_OUTPUT tag is used to specify where the man pages will be put. If a\n# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of\n# it. A directory man3 will be created inside the directory specified by\n# MAN_OUTPUT.\n# The default directory is: man.\n# This tag requires that the tag GENERATE_MAN is set to YES.\n\nMAN_OUTPUT             = man\n\n# The MAN_EXTENSION tag determines the extension that is added to the generated\n# man pages. In case the manual section does not start with a number, the number\n# 3 is prepended. The dot (.) at the beginning of the MAN_EXTENSION tag is\n# optional.\n# The default value is: .3.\n# This tag requires that the tag GENERATE_MAN is set to YES.\n\nMAN_EXTENSION          = .3\n\n# The MAN_SUBDIR tag determines the name of the directory created within\n# MAN_OUTPUT in which the man pages are placed. If defaults to man followed by\n# MAN_EXTENSION with the initial . removed.\n# This tag requires that the tag GENERATE_MAN is set to YES.\n\nMAN_SUBDIR             =\n\n# If the MAN_LINKS tag is set to YES and doxygen generates man output, then it\n# will generate one additional man file for each entity documented in the real\n# man page(s). These additional files only source the real man page, but without\n# them the man command would be unable to find the correct page.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_MAN is set to YES.\n\nMAN_LINKS              = NO\n\n#---------------------------------------------------------------------------\n# Configuration options related to the XML output\n#---------------------------------------------------------------------------\n\n# If the GENERATE_XML tag is set to YES, doxygen will generate an XML file that\n# captures the structure of the code including all documentation.\n# The default value is: NO.\n\nGENERATE_XML           = YES\n\n# The XML_OUTPUT tag is used to specify where the XML pages will be put. If a\n# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of\n# it.\n# The default directory is: xml.\n# This tag requires that the tag GENERATE_XML is set to YES.\n\nXML_OUTPUT             = xml\n\n# If the XML_PROGRAMLISTING tag is set to YES, doxygen will dump the program\n# listings (including syntax highlighting and cross-referencing information) to\n# the XML output. Note that enabling this will significantly increase the size\n# of the XML output.\n# The default value is: YES.\n# This tag requires that the tag GENERATE_XML is set to YES.\n\nXML_PROGRAMLISTING     = YES\n\n#---------------------------------------------------------------------------\n# Configuration options related to the DOCBOOK output\n#---------------------------------------------------------------------------\n\n# If the GENERATE_DOCBOOK tag is set to YES, doxygen will generate Docbook files\n# that can be used to generate PDF.\n# The default value is: NO.\n\nGENERATE_DOCBOOK       = NO\n\n# The DOCBOOK_OUTPUT tag is used to specify where the Docbook pages will be put.\n# If a relative path is entered the value of OUTPUT_DIRECTORY will be put in\n# front of it.\n# The default directory is: docbook.\n# This tag requires that the tag GENERATE_DOCBOOK is set to YES.\n\nDOCBOOK_OUTPUT         = docbook\n\n# If the DOCBOOK_PROGRAMLISTING tag is set to YES, doxygen will include the\n# program listings (including syntax highlighting and cross-referencing\n# information) to the DOCBOOK output. Note that enabling this will significantly\n# increase the size of the DOCBOOK output.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_DOCBOOK is set to YES.\n\nDOCBOOK_PROGRAMLISTING = NO\n\n#---------------------------------------------------------------------------\n# Configuration options for the AutoGen Definitions output\n#---------------------------------------------------------------------------\n\n# If the GENERATE_AUTOGEN_DEF tag is set to YES, doxygen will generate an\n# AutoGen Definitions (see http://autogen.sf.net) file that captures the\n# structure of the code including all documentation. Note that this feature is\n# still experimental and incomplete at the moment.\n# The default value is: NO.\n\nGENERATE_AUTOGEN_DEF   = NO\n\n#---------------------------------------------------------------------------\n# Configuration options related to the Perl module output\n#---------------------------------------------------------------------------\n\n# If the GENERATE_PERLMOD tag is set to YES, doxygen will generate a Perl module\n# file that captures the structure of the code including all documentation.\n#\n# Note that this feature is still experimental and incomplete at the moment.\n# The default value is: NO.\n\nGENERATE_PERLMOD       = NO\n\n# If the PERLMOD_LATEX tag is set to YES, doxygen will generate the necessary\n# Makefile rules, Perl scripts and LaTeX code to be able to generate PDF and DVI\n# output from the Perl module output.\n# The default value is: NO.\n# This tag requires that the tag GENERATE_PERLMOD is set to YES.\n\nPERLMOD_LATEX          = NO\n\n# If the PERLMOD_PRETTY tag is set to YES, the Perl module output will be nicely\n# formatted so it can be parsed by a human reader. This is useful if you want to\n# understand what is going on. On the other hand, if this tag is set to NO, the\n# size of the Perl module output will be much smaller and Perl will parse it\n# just the same.\n# The default value is: YES.\n# This tag requires that the tag GENERATE_PERLMOD is set to YES.\n\nPERLMOD_PRETTY         = YES\n\n# The names of the make variables in the generated doxyrules.make file are\n# prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. This is useful\n# so different doxyrules.make files included by the same Makefile don't\n# overwrite each other's variables.\n# This tag requires that the tag GENERATE_PERLMOD is set to YES.\n\nPERLMOD_MAKEVAR_PREFIX =\n\n#---------------------------------------------------------------------------\n# Configuration options related to the preprocessor\n#---------------------------------------------------------------------------\n\n# If the ENABLE_PREPROCESSING tag is set to YES, doxygen will evaluate all\n# C-preprocessor directives found in the sources and include files.\n# The default value is: YES.\n\nENABLE_PREPROCESSING   = NO\n\n# If the MACRO_EXPANSION tag is set to YES, doxygen will expand all macro names\n# in the source code. If set to NO, only conditional compilation will be\n# performed. Macro expansion can be done in a controlled way by setting\n# EXPAND_ONLY_PREDEF to YES.\n# The default value is: NO.\n# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.\n\nMACRO_EXPANSION        = NO\n\n# If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES then\n# the macro expansion is limited to the macros specified with the PREDEFINED and\n# EXPAND_AS_DEFINED tags.\n# The default value is: NO.\n# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.\n\nEXPAND_ONLY_PREDEF     = NO\n\n# If the SEARCH_INCLUDES tag is set to YES, the include files in the\n# INCLUDE_PATH will be searched if a #include is found.\n# The default value is: YES.\n# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.\n\nSEARCH_INCLUDES        = YES\n\n# The INCLUDE_PATH tag can be used to specify one or more directories that\n# contain include files that are not input files but should be processed by the\n# preprocessor.\n# This tag requires that the tag SEARCH_INCLUDES is set to YES.\n\nINCLUDE_PATH           =\n\n# You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard\n# patterns (like *.h and *.hpp) to filter out the header-files in the\n# directories. If left blank, the patterns specified with FILE_PATTERNS will be\n# used.\n# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.\n\nINCLUDE_FILE_PATTERNS  =\n\n# The PREDEFINED tag can be used to specify one or more macro names that are\n# defined before the preprocessor is started (similar to the -D option of e.g.\n# gcc). The argument of the tag is a list of macros of the form: name or\n# name=definition (no spaces). If the definition and the \"=\" are omitted, \"=1\"\n# is assumed. To prevent a macro definition from being undefined via #undef or\n# recursively expanded use the := operator instead of the = operator.\n# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.\n\nPREDEFINED             =\n\n# If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then this\n# tag can be used to specify a list of macro names that should be expanded. The\n# macro definition that is found in the sources will be used. Use the PREDEFINED\n# tag if you want to use a different macro definition that overrules the\n# definition found in the source code.\n# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.\n\nEXPAND_AS_DEFINED      =\n\n# If the SKIP_FUNCTION_MACROS tag is set to YES then doxygen's preprocessor will\n# remove all references to function-like macros that are alone on a line, have\n# an all uppercase name, and do not end with a semicolon. Such function macros\n# are typically used for boiler-plate code, and will confuse the parser if not\n# removed.\n# The default value is: YES.\n# This tag requires that the tag ENABLE_PREPROCESSING is set to YES.\n\nSKIP_FUNCTION_MACROS   = YES\n\n#---------------------------------------------------------------------------\n# Configuration options related to external references\n#---------------------------------------------------------------------------\n\n# The TAGFILES tag can be used to specify one or more tag files. For each tag\n# file the location of the external documentation should be added. The format of\n# a tag file without this location is as follows:\n# TAGFILES = file1 file2 ...\n# Adding location for the tag files is done as follows:\n# TAGFILES = file1=loc1 \"file2 = loc2\" ...\n# where loc1 and loc2 can be relative or absolute paths or URLs. See the\n# section \"Linking to external documentation\" for more information about the use\n# of tag files.\n# Note: Each tag file must have a unique name (where the name does NOT include\n# the path). If a tag file is not located in the directory in which doxygen is\n# run, you must also specify the path to the tagfile here.\n\nTAGFILES               =\n\n# When a file name is specified after GENERATE_TAGFILE, doxygen will create a\n# tag file that is based on the input files it reads. See section \"Linking to\n# external documentation\" for more information about the usage of tag files.\n\nGENERATE_TAGFILE       =\n\n# If the ALLEXTERNALS tag is set to YES, all external class will be listed in\n# the class index. If set to NO, only the inherited external classes will be\n# listed.\n# The default value is: NO.\n\nALLEXTERNALS           = NO\n\n# If the EXTERNAL_GROUPS tag is set to YES, all external groups will be listed\n# in the modules index. If set to NO, only the current project's groups will be\n# listed.\n# The default value is: YES.\n\nEXTERNAL_GROUPS        = YES\n\n# If the EXTERNAL_PAGES tag is set to YES, all external pages will be listed in\n# the related pages index. If set to NO, only the current project's pages will\n# be listed.\n# The default value is: YES.\n\nEXTERNAL_PAGES         = YES\n\n# The PERL_PATH should be the absolute path and name of the perl script\n# interpreter (i.e. the result of 'which perl').\n# The default file (with absolute path) is: /usr/bin/perl.\n\nPERL_PATH              = /usr/bin/perl\n\n#---------------------------------------------------------------------------\n# Configuration options related to the dot tool\n#---------------------------------------------------------------------------\n\n# If the CLASS_DIAGRAMS tag is set to YES, doxygen will generate a class diagram\n# (in HTML and LaTeX) for classes with base or super classes. Setting the tag to\n# NO turns the diagrams off. Note that this option also works with HAVE_DOT\n# disabled, but it is recommended to install and use dot, since it yields more\n# powerful graphs.\n# The default value is: YES.\n\nCLASS_DIAGRAMS         = YES\n\n# You can define message sequence charts within doxygen comments using the \\msc\n# command. Doxygen will then run the mscgen tool (see:\n# http://www.mcternan.me.uk/mscgen/)) to produce the chart and insert it in the\n# documentation. The MSCGEN_PATH tag allows you to specify the directory where\n# the mscgen tool resides. If left empty the tool is assumed to be found in the\n# default search path.\n\nMSCGEN_PATH            =\n\n# You can include diagrams made with dia in doxygen documentation. Doxygen will\n# then run dia to produce the diagram and insert it in the documentation. The\n# DIA_PATH tag allows you to specify the directory where the dia binary resides.\n# If left empty dia is assumed to be found in the default search path.\n\nDIA_PATH               =\n\n# If set to YES the inheritance and collaboration graphs will hide inheritance\n# and usage relations if the target is undocumented or is not a class.\n# The default value is: YES.\n\nHIDE_UNDOC_RELATIONS   = YES\n\n# If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is\n# available from the path. This tool is part of Graphviz (see:\n# http://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent\n# Bell Labs. The other options in this section have no effect if this option is\n# set to NO\n# The default value is: NO.\n\nHAVE_DOT               = NO\n\n# The DOT_NUM_THREADS specifies the number of dot invocations doxygen is allowed\n# to run in parallel. When set to 0 doxygen will base this on the number of\n# processors available in the system. You can set it explicitly to a value\n# larger than 0 to get control over the balance between CPU load and processing\n# speed.\n# Minimum value: 0, maximum value: 32, default value: 0.\n# This tag requires that the tag HAVE_DOT is set to YES.\n\nDOT_NUM_THREADS        = 0\n\n# When you want a differently looking font in the dot files that doxygen\n# generates you can specify the font name using DOT_FONTNAME. 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  },
  {
    "path": "osqp/docs/examples/huber.rst",
    "content": "Huber fitting\n=============\n\n*Huber fitting* or the *robust least-squares problem* performs linear regression under the assumption that there are outliers in the data.\nThe fitting problem is written as\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{minimize} & \\sum_{i=1}^{m} \\phi_{\\rm hub}(a_i^T x - b_i),\n  \\end{array}\n\nwith the Huber penalty function :math:`\\phi_{\\rm hub}:\\mathbf{R}\\to\\mathbf{R}` defined as\n\n.. math::\n  \\phi_{\\rm hub}(u) =\n  \\begin{cases}\n      u^2         & |u| \\le 1 \\\\\n      (2|u| - 1)  & |u| > 1\n  \\end{cases}\n\nThe problem has the following equivalent form (see `here <http://doi.org/10.1109/34.877518>`_ for more details)\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{minimize}   & u^T u + 2\\,\\boldsymbol{1}^T v \\\\\n    \\mbox{subject to} & -v \\le Ax - b - u \\le v\n  \\end{array}\n\n\n\nPython\n------\n\n.. code:: python\n\n    import osqp\n    import numpy as np\n    import scipy as sp\n    from scipy import sparse\n\n    # Generate problem data\n    sp.random.seed(1)\n    n = 10\n    m = 100\n    Ad = sparse.random(m, n, density=0.5, format='csc')\n    x_true = np.random.randn(n) / np.sqrt(n)\n    ind95 = (np.random.rand(m) < 0.95).astype(float)\n    b = Ad.dot(x_true) + np.multiply(0.5*np.random.randn(m), ind95) \\\n        + np.multiply(10.*np.random.rand(m), 1. - ind95)\n\n    # OSQP data\n    Im = sparse.eye(m)\n    P = sparse.block_diag([sparse.csc_matrix((n, n)), 2*Im,\n                           sparse.csc_matrix((m, m))],\n                           format='csc')\n    q = np.append(np.zeros(m+n), 2*np.ones(m))\n    A = sparse.vstack([\n            sparse.hstack([Ad, -Im, Im]),\n            sparse.hstack([Ad, -Im, -Im])], format='csc')\n    l = np.hstack([b, -np.inf*np.ones(m)])\n    u = np.hstack([np.inf*np.ones(m), b])\n\n    # Create an OSQP object\n    prob = osqp.OSQP()\n\n    # Setup workspace\n    prob.setup(P, q, A, l, u)\n\n    # Solve problem\n    res = prob.solve()\n\n\n\nMatlab\n------\n\n.. code:: matlab\n\n    % Generate problem data\n    rng(1)\n    n = 10;\n    m = 100;\n    Ad = sprandn(m, n, 0.5);\n    x_true = randn(n, 1) / sqrt(n);\n    ind95 = rand(m, 1) > 0.95;\n    b = Ad*x_true + 10*rand(m, 1).*ind95 + 0.5*randn(m, 1).*(1-ind95);\n\n    % OSQP data\n    Im = speye(m);\n    P = blkdiag(sparse(n, n), 2*Im, sparse(m, m));\n    q = [zeros(m + n, 1); 2*ones(m, 1)];\n    A = [Ad, -Im, Im;\n         Ad, -Im, -Im];\n    l = [b; -inf*ones(m, 1)];\n    u = [inf*ones(m, 1); b];\n\n    % Create an OSQP object\n    prob = osqp;\n\n    % Setup workspace\n    prob.setup(P, q, A, l, u);\n\n    % Solve problem\n    res = prob.solve();\n\n\n\nCVXPY\n-----\n\n.. code:: python\n\n    from cvxpy import *\n    import numpy as np\n    import scipy as sp\n    from scipy import sparse\n\n    # Generate problem data\n    sp.random.seed(1)\n    n = 10\n    m = 100\n    A = sparse.random(m, n, density=0.5, format='csc')\n    x_true = np.random.randn(n) / np.sqrt(n)\n    ind95 = (np.random.rand(m) < 0.95).astype(float)\n    b = A.dot(x_true) + np.multiply(0.5*np.random.randn(m), ind95) \\\n        + np.multiply(10.*np.random.rand(m), 1. - ind95)\n\n    # Define problem\n    x = Variable(n)\n    objective = sum(huber(A*x - b))\n\n    # Solve with OSQP\n    Problem(Minimize(objective)).solve(solver=OSQP)\n\n\n\nYALMIP\n------\n\n.. code:: matlab\n\n    % Generate problem data\n    rng(1)\n    n = 10;\n    m = 100;\n    A = sprandn(m, n, 0.5);\n    x_true = randn(n, 1) / sqrt(n);\n    ind95 = rand(m, 1) > 0.95;\n    b = A*x_true + 10*rand(m, 1).*ind95 + 0.5*randn(m, 1).*(1-ind95);\n\n    % Define problem\n    x = sdpvar(n, 1);\n    objective = huber(A*x - b);\n\n    % Solve with OSQP\n    options = sdpsettings('solver', 'osqp');\n    optimize([], objective, options);\n"
  },
  {
    "path": "osqp/docs/examples/index.rst",
    "content": "Examples\n========\n\n\nDemo\n----\n\n.. toctree::\n   :maxdepth: 1\n\n   setup-and-solve.rst\n   update-vectors.rst\n   update-matrices.rst\n\n\nApplications\n------------\n\n.. toctree::\n   :maxdepth: 1\n\n   huber.rst\n   lasso.rst\n   least-squares.rst\n   mpc.rst\n   portfolio.rst\n   svm.rst\n"
  },
  {
    "path": "osqp/docs/examples/lasso.rst",
    "content": "Lasso\n=====\n\n\nLasso is a well known technique for sparse linear regression.\nIt is obtained by adding an :math:`\\ell_1` regularization term in the objective,\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{minimize} & \\frac{1}{2} \\| Ax - b \\|_2^2 + \\gamma \\| x \\|_1\n  \\end{array}\n\n\nwhere :math:`x \\in \\mathbf{R}^{n}` is the vector of parameters, :math:`A \\in \\mathbf{R}^{m \\times n}` is the data matrix, and :math:`\\gamma > 0` is the weighting parameter.\nThe problem has the following equivalent form,\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{minimize}   & \\frac{1}{2} y^T y + \\gamma \\boldsymbol{1}^T t \\\\\n    \\mbox{subject to} & y = Ax - b \\\\\n                      & -t \\le x \\le t\n  \\end{array}\n\n\nIn order to get a good trade-off between sparsity of the solution and quality of the linear fit, we solve the problem for varying weighting parameter :math:`\\gamma`.\nSince :math:`\\gamma` enters only in the linear part of the objective function, we can reuse the matrix factorization and enable warm starting to reduce the computation time.\n\n\n\nPython\n------\n\n.. code:: python\n\n    import osqp\n    import numpy as np\n    import scipy as sp\n    from scipy import sparse\n\n    # Generate problem data\n    sp.random.seed(1)\n    n = 10\n    m = 1000\n    Ad = sparse.random(m, n, density=0.5)\n    x_true = np.multiply((np.random.rand(n) > 0.8).astype(float),\n                         np.random.randn(n)) / np.sqrt(n)\n    b = Ad.dot(x_true) + 0.5*np.random.randn(m)\n    gammas = np.linspace(1, 10, 11)\n\n    # Auxiliary data\n    In = sparse.eye(n)\n    Im = sparse.eye(m)\n    On = sparse.csc_matrix((n, n))\n    Onm = sparse.csc_matrix((n, m))\n\n    # OSQP data\n    P = sparse.block_diag([On, sparse.eye(m), On], format='csc')\n    q = np.zeros(2*n + m)\n    A = sparse.vstack([sparse.hstack([Ad, -Im, Onm.T]),\n                       sparse.hstack([In, Onm, -In]),\n                       sparse.hstack([In, Onm, In])], format='csc')\n    l = np.hstack([b, -np.inf * np.ones(n), np.zeros(n)])\n    u = np.hstack([b, np.zeros(n), np.inf * np.ones(n)])\n\n    # Create an OSQP object\n    prob = osqp.OSQP()\n\n    # Setup workspace\n    prob.setup(P, q, A, l, u, warm_start=True)\n\n    # Solve problem for different values of gamma parameter\n    for gamma in gammas:\n        # Update linear cost\n        q_new = np.hstack([np.zeros(n+m), gamma*np.ones(n)])\n        prob.update(q=q_new)\n\n        # Solve\n        res = prob.solve()\n\n\nMatlab\n------\n\n.. code:: matlab\n\n    % Generate problem data\n    rng(1)\n    n = 10;\n    m = 1000;\n    Ad = sprandn(m, n, 0.5);\n    x_true = (randn(n, 1) > 0.8) .* randn(n, 1) / sqrt(n);\n    b = Ad * x_true + 0.5 * randn(m, 1);\n    gammas = linspace(1, 10, 11);\n\n    % OSQP data\n    P = blkdiag(sparse(n, n), speye(m), sparse(n, n));\n    q = zeros(2*n+m, 1);\n    A = [Ad, -speye(m), sparse(m,n);\n        speye(n), sparse(n, m), -speye(n);\n        speye(n), sparse(n, m), speye(n);];\n    l = [b; -inf*ones(n, 1); zeros(n, 1)];\n    u = [b; zeros(n, 1); inf*ones(n, 1)];\n\n    % Create an OSQP object\n    prob = osqp;\n\n    % Setup workspace\n    prob.setup(P, q, A, l, u, 'warm_start', true);\n\n    % Solve problem for different values of gamma parameter\n    for i = 1 : length(gammas)\n        % Update linear cost\n        gamma = gammas(i);\n        q_new = [zeros(n+m,1); gamma*ones(n,1)];\n        prob.update('q', q_new);\n\n        % Solve\n        res = prob.solve();\n    end\n\n\n\nCVXPY\n-----\n\n.. code:: python\n\n    from cvxpy import *\n    import numpy as np\n    import scipy as sp\n    from scipy import sparse\n\n    # Generate problem data\n    sp.random.seed(1)\n    n = 10\n    m = 1000\n    A = sparse.random(m, n, density=0.5)\n    x_true = np.multiply((np.random.rand(n) > 0.8).astype(float),\n                         np.random.randn(n)) / np.sqrt(n)\n    b = A.dot(x_true) + 0.5*np.random.randn(m)\n    gammas = np.linspace(1, 10, 11)\n\n    # Define problem\n    x = Variable(n)\n    gamma = Parameter(nonneg=True)\n    objective = 0.5*sum_squares(A*x - b) + gamma*norm1(x)\n    prob = Problem(Minimize(objective))\n\n    # Solve problem for different values of gamma parameter\n    for gamma_val in gammas:\n        gamma.value = gamma_val\n        prob.solve(solver=OSQP, warm_start=True)\n\n\nYALMIP\n------\n\n.. code:: matlab\n\n    % Generate problem data\n    rng(1)\n    n = 10;\n    m = 1000;\n    A = sprandn(m, n, 0.5);\n    x_true = (randn(n, 1) > 0.8) .* randn(n, 1) / sqrt(n);\n    b = A * x_true + 0.5 * randn(m, 1);\n    gammas = linspace(1, 10, 11);\n\n    % Define problem\n    x = sdpvar(n, 1);\n    gamma = sdpvar;\n    objective = 0.5*norm(A*x - b)^2 + gamma*norm(x,1);\n\n    % Solve with OSQP\n    options = sdpsettings('solver', 'osqp');\n    x_opt = optimizer([], objective, options, gamma, x);\n\n    % Solve problem for different values of gamma parameter\n    for i = 1 : length(gammas)\n        x_opt(gammas(i));\n    end\n"
  },
  {
    "path": "osqp/docs/examples/least-squares.rst",
    "content": "Least-squares\n=============\n\nConsider the following constrained least-squares problem\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{minimize} & \\frac{1}{2} \\|Ax - b\\|_2^2 \\\\\n    \\mbox{subject to} & 0 \\leq x \\leq 1\n  \\end{array}\n\nThe problem has the following equivalent form\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{minimize} & \\frac{1}{2} y^T y \\\\\n    \\mbox{subject to} & y = A x - b \\\\\n                      & 0 \\le x \\le 1\n  \\end{array}\n\n\n\nPython\n------\n\n.. code:: python\n\n    import osqp\n    import numpy as np\n    import scipy as sp\n    from scipy import sparse\n\n    # Generate problem data\n    sp.random.seed(1)\n    m = 30\n    n = 20\n    Ad = sparse.random(m, n, density=0.7, format='csc')\n    b = np.random.randn(m)\n\n    # OSQP data\n    P = sparse.block_diag([sparse.csc_matrix((n, n)), sparse.eye(m)], format='csc')\n    q = np.zeros(n+m)\n    A = sparse.vstack([\n            sparse.hstack([Ad, -sparse.eye(m)]),\n            sparse.hstack([sparse.eye(n), sparse.csc_matrix((n, m))])], format='csc')\n    l = np.hstack([b, np.zeros(n)])\n    u = np.hstack([b, np.ones(n)])\n\n    # Create an OSQP object\n    prob = osqp.OSQP()\n\n    # Setup workspace\n    prob.setup(P, q, A, l, u)\n\n    # Solve problem\n    res = prob.solve()\n\n\n\nMatlab\n------\n\n.. code:: matlab\n\n   % Generate problem data\n   rng(1)\n   m = 30;\n   n = 20;\n   Ad = sprandn(m, n, 0.7);\n   b = randn(m, 1);\n\n   % OSQP data\n   P = blkdiag(sparse(n, n), speye(m));\n   q = zeros(n+m, 1);\n   A = [Ad, -speye(m);\n        speye(n), sparse(n, m)];\n   l = [b; zeros(n, 1)];\n   u = [b; ones(n, 1)];\n\n   % Create an OSQP object\n   prob = osqp;\n\n   % Setup workspace\n   prob.setup(P, q, A, l, u);\n\n   % Solve problem\n   res = prob.solve();\n\n\n\nCVXPY\n-----\n\n.. code:: python\n\n    from cvxpy import *\n    import numpy as np\n    import scipy as sp\n    from scipy import sparse\n\n    # Generate problem data\n    sp.random.seed(1)\n    m = 30\n    n = 20\n    A = sparse.random(m, n, density=0.7, format='csc')\n    b = np.random.randn(m)\n\n    # Define problem\n    x = Variable(n)\n    objective = 0.5*sum_squares(A*x - b)\n    constraints = [x >= 0, x <= 1]\n\n    # Solve with OSQP\n    Problem(Minimize(objective), constraints).solve(solver=OSQP)\n\n\n\nYALMIP\n------\n\n.. code:: matlab\n\n   % Generate data\n   rng(1)\n   m = 30;\n   n = 20;\n   A = sprandn(m, n, 0.7);\n   b = randn(m, 1);\n\n   % Define problem\n   x = sdpvar(n, 1);\n   objective = 0.5*norm(A*x - b)^2;\n   constraints = [ 0 <= x <= 1];\n\n   % Solve with OSQP\n   options = sdpsettings('solver','osqp');\n   optimize(constraints, objective, options);\n\n"
  },
  {
    "path": "osqp/docs/examples/mpc.rst",
    "content": "Model predictive control (MPC)\n==============================\n\nWe consider the problem of controlling a linear time-invariant dinamical system to some reference state :math:`x_r \\in \\mathbf{R}^{n_x}`.\nTo achieve this we use *constrained linear-quadratic MPC*, which solves at each time step the following finite-horizon optimal control problem\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{minimize}   & (x_N-x_r)^T Q_N (x_N-x_r) + \\sum_{k=0}^{N-1} (x_k-x_r)^T Q (x_k-x_r) + u_k^T R u_k \\\\\n    \\mbox{subject to} & x_{k+1} = A x_k + B u_k \\\\\n                      & x_{\\rm min} \\le x_k  \\le x_{\\rm max} \\\\\n                      & u_{\\rm min} \\le u_k  \\le u_{\\rm max} \\\\\n                      & x_0 = \\bar{x}\n  \\end{array}\n\nThe states :math:`x_k \\in \\mathbf{R}^{n_x}` and the inputs :math:`u_k \\in \\mathbf{R}^{n_u}` are constrained to be between some lower and upper bounds.\nThe problem is solved repeatedly for varying initial state :math:`\\bar{x} \\in \\mathbf{R}^{n_x}`.\n\n\nPython\n------\n\n.. code:: python\n\n    import osqp\n    import numpy as np\n    import scipy as sp\n    from scipy import sparse\n\n    # Discrete time model of a quadcopter\n    Ad = sparse.csc_matrix([\n      [1.,      0.,     0., 0., 0., 0., 0.1,     0.,     0.,  0.,     0.,     0.    ],\n      [0.,      1.,     0., 0., 0., 0., 0.,      0.1,    0.,  0.,     0.,     0.    ],\n      [0.,      0.,     1., 0., 0., 0., 0.,      0.,     0.1, 0.,     0.,     0.    ],\n      [0.0488,  0.,     0., 1., 0., 0., 0.0016,  0.,     0.,  0.0992, 0.,     0.    ],\n      [0.,     -0.0488, 0., 0., 1., 0., 0.,     -0.0016, 0.,  0.,     0.0992, 0.    ],\n      [0.,      0.,     0., 0., 0., 1., 0.,      0.,     0.,  0.,     0.,     0.0992],\n      [0.,      0.,     0., 0., 0., 0., 1.,      0.,     0.,  0.,     0.,     0.    ],\n      [0.,      0.,     0., 0., 0., 0., 0.,      1.,     0.,  0.,     0.,     0.    ],\n      [0.,      0.,     0., 0., 0., 0., 0.,      0.,     1.,  0.,     0.,     0.    ],\n      [0.9734,  0.,     0., 0., 0., 0., 0.0488,  0.,     0.,  0.9846, 0.,     0.    ],\n      [0.,     -0.9734, 0., 0., 0., 0., 0.,     -0.0488, 0.,  0.,     0.9846, 0.    ],\n      [0.,      0.,     0., 0., 0., 0., 0.,      0.,     0.,  0.,     0.,     0.9846]\n    ])\n    Bd = sparse.csc_matrix([\n      [0.,      -0.0726,  0.,     0.0726],\n      [-0.0726,  0.,      0.0726, 0.    ],\n      [-0.0152,  0.0152, -0.0152, 0.0152],\n      [-0.,     -0.0006, -0.,     0.0006],\n      [0.0006,   0.,     -0.0006, 0.0000],\n      [0.0106,   0.0106,  0.0106, 0.0106],\n      [0,       -1.4512,  0.,     1.4512],\n      [-1.4512,  0.,      1.4512, 0.    ],\n      [-0.3049,  0.3049, -0.3049, 0.3049],\n      [-0.,     -0.0236,  0.,     0.0236],\n      [0.0236,   0.,     -0.0236, 0.    ],\n      [0.2107,   0.2107,  0.2107, 0.2107]])\n    [nx, nu] = Bd.shape\n\n    # Constraints\n    u0 = 10.5916\n    umin = np.array([9.6, 9.6, 9.6, 9.6]) - u0\n    umax = np.array([13., 13., 13., 13.]) - u0\n    xmin = np.array([-np.pi/6,-np.pi/6,-np.inf,-np.inf,-np.inf,-1.,\n                     -np.inf,-np.inf,-np.inf,-np.inf,-np.inf,-np.inf])\n    xmax = np.array([ np.pi/6, np.pi/6, np.inf, np.inf, np.inf, np.inf,\n                      np.inf, np.inf, np.inf, np.inf, np.inf, np.inf])\n\n    # Objective function\n    Q = sparse.diags([0., 0., 10., 10., 10., 10., 0., 0., 0., 5., 5., 5.])\n    QN = Q\n    R = 0.1*sparse.eye(4)\n\n    # Initial and reference states\n    x0 = np.zeros(12)\n    xr = np.array([0.,0.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.])\n\n    # Prediction horizon\n    N = 10\n\n    # Cast MPC problem to a QP: x = (x(0),x(1),...,x(N),u(0),...,u(N-1))\n    # - quadratic objective\n    P = sparse.block_diag([sparse.kron(sparse.eye(N), Q), QN,\n                           sparse.kron(sparse.eye(N), R)], format='csc')\n    # - linear objective\n    q = np.hstack([np.kron(np.ones(N), -Q.dot(xr)), -QN.dot(xr),\n                   np.zeros(N*nu)])\n    # - linear dynamics\n    Ax = sparse.kron(sparse.eye(N+1),-sparse.eye(nx)) + sparse.kron(sparse.eye(N+1, k=-1), Ad)\n    Bu = sparse.kron(sparse.vstack([sparse.csc_matrix((1, N)), sparse.eye(N)]), Bd)\n    Aeq = sparse.hstack([Ax, Bu])\n    leq = np.hstack([-x0, np.zeros(N*nx)])\n    ueq = leq\n    # - input and state constraints\n    Aineq = sparse.eye((N+1)*nx + N*nu)\n    lineq = np.hstack([np.kron(np.ones(N+1), xmin), np.kron(np.ones(N), umin)])\n    uineq = np.hstack([np.kron(np.ones(N+1), xmax), np.kron(np.ones(N), umax)])\n    # - OSQP constraints\n    A = sparse.vstack([Aeq, Aineq], format='csc')\n    l = np.hstack([leq, lineq])\n    u = np.hstack([ueq, uineq])\n\n    # Create an OSQP object\n    prob = osqp.OSQP()\n\n    # Setup workspace\n    prob.setup(P, q, A, l, u, warm_start=True)\n\n    # Simulate in closed loop\n    nsim = 15\n    for i in range(nsim):\n        # Solve\n        res = prob.solve()\n\n        # Check solver status\n        if res.info.status != 'solved':\n            raise ValueError('OSQP did not solve the problem!')\n\n        # Apply first control input to the plant\n        ctrl = res.x[-N*nu:-(N-1)*nu]\n        x0 = Ad.dot(x0) + Bd.dot(ctrl)\n\n        # Update initial state\n        l[:nx] = -x0\n        u[:nx] = -x0\n        prob.update(l=l, u=u)\n\n\n\nMatlab\n------\n\n.. code:: matlab\n\n    % Discrete time model of a quadcopter\n    Ad = [1       0       0   0   0   0   0.1     0       0    0       0       0;\n          0       1       0   0   0   0   0       0.1     0    0       0       0;\n          0       0       1   0   0   0   0       0       0.1  0       0       0;\n          0.0488  0       0   1   0   0   0.0016  0       0    0.0992  0       0;\n          0      -0.0488  0   0   1   0   0      -0.0016  0    0       0.0992  0;\n          0       0       0   0   0   1   0       0       0    0       0       0.0992;\n          0       0       0   0   0   0   1       0       0    0       0       0;\n          0       0       0   0   0   0   0       1       0    0       0       0;\n          0       0       0   0   0   0   0       0       1    0       0       0;\n          0.9734  0       0   0   0   0   0.0488  0       0    0.9846  0       0;\n          0      -0.9734  0   0   0   0   0      -0.0488  0    0       0.9846  0;\n          0       0       0   0   0   0   0       0       0    0       0       0.9846];\n    Bd = [0      -0.0726  0       0.0726;\n         -0.0726  0       0.0726  0;\n         -0.0152  0.0152 -0.0152  0.0152;\n          0      -0.0006 -0.0000  0.0006;\n          0.0006  0      -0.0006  0;\n          0.0106  0.0106  0.0106  0.0106;\n          0      -1.4512  0       1.4512;\n         -1.4512  0       1.4512  0;\n         -0.3049  0.3049 -0.3049  0.3049;\n          0      -0.0236  0       0.0236;\n          0.0236  0      -0.0236  0;\n          0.2107  0.2107  0.2107  0.2107];\n    [nx, nu] = size(Bd);\n\n    % Constraints\n    u0 = 10.5916;\n    umin = [9.6; 9.6; 9.6; 9.6] - u0;\n    umax = [13; 13; 13; 13] - u0;\n    xmin = [-pi/6; -pi/6; -Inf; -Inf; -Inf; -1; -Inf(6,1)];\n    xmax = [ pi/6;  pi/6;  Inf;  Inf;  Inf; Inf; Inf(6,1)];\n\n    % Objective function\n    Q = diag([0 0 10 10 10 10 0 0 0 5 5 5]);\n    QN = Q;\n    R = 0.1*eye(4);\n\n    % Initial and reference states\n    x0 = zeros(12,1);\n    xr = [0; 0; 1; 0; 0; 0; 0; 0; 0; 0; 0; 0];\n\n    % Prediction horizon\n    N = 10;\n\n    % Cast MPC problem to a QP: x = (x(0),x(1),...,x(N),u(0),...,u(N-1))\n    % - quadratic objective\n    P = blkdiag( kron(speye(N), Q), QN, kron(speye(N), R) );\n    % - linear objective\n    q = [repmat(-Q*xr, N, 1); -QN*xr; zeros(N*nu, 1)];\n    % - linear dynamics\n    Ax = kron(speye(N+1), -speye(nx)) + kron(sparse(diag(ones(N, 1), -1)), Ad);\n    Bu = kron([sparse(1, N); speye(N)], Bd);\n    Aeq = [Ax, Bu];\n    leq = [-x0; zeros(N*nx, 1)];\n    ueq = leq;\n    % - input and state constraints\n    Aineq = speye((N+1)*nx + N*nu);\n    lineq = [repmat(xmin, N+1, 1); repmat(umin, N, 1)];\n    uineq = [repmat(xmax, N+1, 1); repmat(umax, N, 1)];\n    % - OSQP constraints\n    A = [Aeq; Aineq];\n    l = [leq; lineq];\n    u = [ueq; uineq];\n\n    % Create an OSQP object\n    prob = osqp;\n\n    % Setup workspace\n    prob.setup(P, q, A, l, u, 'warm_start', true);\n\n    % Simulate in closed loop\n    nsim = 15;\n    for i = 1 : nsim\n        % Solve\n        res = prob.solve();\n\n        % Check solver status\n        if ~strcmp(res.info.status, 'solved')\n            error('OSQP did not solve the problem!')\n        end\n\n        % Apply first control input to the plant\n        ctrl = res.x((N+1)*nx+1:(N+1)*nx+nu);\n        x0 = Ad*x0 + Bd*ctrl;\n\n        % Update initial state\n        l(1:nx) = -x0;\n        u(1:nx) = -x0;\n        prob.update('l', l, 'u', u);\n    end\n\n\n\nCVXPY\n-----\n\n.. code:: python\n\n    from cvxpy import *\n    import numpy as np\n    import scipy as sp\n    from scipy import sparse\n\n    # Discrete time model of a quadcopter\n    Ad = sparse.csc_matrix([\n      [1.,      0.,     0., 0., 0., 0., 0.1,     0.,     0.,  0.,     0.,     0.    ],\n      [0.,      1.,     0., 0., 0., 0., 0.,      0.1,    0.,  0.,     0.,     0.    ],\n      [0.,      0.,     1., 0., 0., 0., 0.,      0.,     0.1, 0.,     0.,     0.    ],\n      [0.0488,  0.,     0., 1., 0., 0., 0.0016,  0.,     0.,  0.0992, 0.,     0.    ],\n      [0.,     -0.0488, 0., 0., 1., 0., 0.,     -0.0016, 0.,  0.,     0.0992, 0.    ],\n      [0.,      0.,     0., 0., 0., 1., 0.,      0.,     0.,  0.,     0.,     0.0992],\n      [0.,      0.,     0., 0., 0., 0., 1.,      0.,     0.,  0.,     0.,     0.    ],\n      [0.,      0.,     0., 0., 0., 0., 0.,      1.,     0.,  0.,     0.,     0.    ],\n      [0.,      0.,     0., 0., 0., 0., 0.,      0.,     1.,  0.,     0.,     0.    ],\n      [0.9734,  0.,     0., 0., 0., 0., 0.0488,  0.,     0.,  0.9846, 0.,     0.    ],\n      [0.,     -0.9734, 0., 0., 0., 0., 0.,     -0.0488, 0.,  0.,     0.9846, 0.    ],\n      [0.,      0.,     0., 0., 0., 0., 0.,      0.,     0.,  0.,     0.,     0.9846]\n    ])\n    Bd = sparse.csc_matrix([\n      [0.,      -0.0726,  0.,     0.0726],\n      [-0.0726,  0.,      0.0726, 0.    ],\n      [-0.0152,  0.0152, -0.0152, 0.0152],\n      [-0.,     -0.0006, -0.,     0.0006],\n      [0.0006,   0.,     -0.0006, 0.0000],\n      [0.0106,   0.0106,  0.0106, 0.0106],\n      [0,       -1.4512,  0.,     1.4512],\n      [-1.4512,  0.,      1.4512, 0.    ],\n      [-0.3049,  0.3049, -0.3049, 0.3049],\n      [-0.,     -0.0236,  0.,     0.0236],\n      [0.0236,   0.,     -0.0236, 0.    ],\n      [0.2107,   0.2107,  0.2107, 0.2107]])\n    [nx, nu] = Bd.shape\n\n    # Constraints\n    u0 = 10.5916\n    umin = np.array([9.6, 9.6, 9.6, 9.6]) - u0\n    umax = np.array([13., 13., 13., 13.]) - u0\n    xmin = np.array([-np.pi/6,-np.pi/6,-np.inf,-np.inf,-np.inf,-1.,\n                     -np.inf,-np.inf,-np.inf,-np.inf,-np.inf,-np.inf])\n    xmax = np.array([ np.pi/6, np.pi/6, np.inf, np.inf, np.inf, np.inf,\n                      np.inf, np.inf, np.inf, np.inf, np.inf, np.inf])\n\n    # Objective function\n    Q = sparse.diags([0., 0., 10., 10., 10., 10., 0., 0., 0., 5., 5., 5.])\n    QN = Q\n    R = 0.1*sparse.eye(4)\n\n    # Initial and reference states\n    x0 = np.zeros(12)\n    xr = np.array([0.,0.,1.,0.,0.,0.,0.,0.,0.,0.,0.,0.])\n\n    # Prediction horizon\n    N = 10\n\n    # Define problem\n    u = Variable((nu, N))\n    x = Variable((nx, N+1))\n    x_init = Parameter(nx)\n    objective = 0\n    constraints = [x[:,0] == x_init]\n    for k in range(N):\n        objective += quad_form(x[:,k] - xr, Q) + quad_form(u[:,k], R)\n        constraints += [x[:,k+1] == Ad*x[:,k] + Bd*u[:,k]]\n        constraints += [xmin <= x[:,k], x[:,k] <= xmax]\n        constraints += [umin <= u[:,k], u[:,k] <= umax]\n    objective += quad_form(x[:,N] - xr, QN)\n    prob = Problem(Minimize(objective), constraints)\n\n    # Simulate in closed loop\n    nsim = 15\n    for i in range(nsim):\n        x_init.value = x0\n        prob.solve(solver=OSQP, warm_start=True)\n        x0 = Ad.dot(x0) + Bd.dot(u[:,0].value)\n\n\n\nYALMIP\n------\n\n.. code:: matlab\n\n    % Discrete time model of a quadcopter\n    Ad = [1       0       0   0   0   0   0.1     0       0    0       0       0;\n          0       1       0   0   0   0   0       0.1     0    0       0       0;\n          0       0       1   0   0   0   0       0       0.1  0       0       0;\n          0.0488  0       0   1   0   0   0.0016  0       0    0.0992  0       0;\n          0      -0.0488  0   0   1   0   0      -0.0016  0    0       0.0992  0;\n          0       0       0   0   0   1   0       0       0    0       0       0.0992;\n          0       0       0   0   0   0   1       0       0    0       0       0;\n          0       0       0   0   0   0   0       1       0    0       0       0;\n          0       0       0   0   0   0   0       0       1    0       0       0;\n          0.9734  0       0   0   0   0   0.0488  0       0    0.9846  0       0;\n          0      -0.9734  0   0   0   0   0      -0.0488  0    0       0.9846  0;\n          0       0       0   0   0   0   0       0       0    0       0       0.9846];\n    Bd = [0      -0.0726  0       0.0726;\n         -0.0726  0       0.0726  0;\n         -0.0152  0.0152 -0.0152  0.0152;\n          0      -0.0006 -0.0000  0.0006;\n          0.0006  0      -0.0006  0;\n          0.0106  0.0106  0.0106  0.0106;\n          0      -1.4512  0       1.4512;\n         -1.4512  0       1.4512  0;\n         -0.3049  0.3049 -0.3049  0.3049;\n          0      -0.0236  0       0.0236;\n          0.0236  0      -0.0236  0;\n          0.2107  0.2107  0.2107  0.2107];\n    [nx, nu] = size(Bd);\n\n    % Constraints\n    u0 = 10.5916;\n    umin = [9.6; 9.6; 9.6; 9.6] - u0;\n    umax = [13; 13; 13; 13] - u0;\n    xmin = [-pi/6; -pi/6; -Inf; -Inf; -Inf; -1; -Inf(6,1)];\n    xmax = [ pi/6;  pi/6;  Inf;  Inf;  Inf; Inf; Inf(6,1)];\n\n    % Objective function\n    Q = diag([0 0 10 10 10 10 0 0 0 5 5 5]);\n    QN = Q;\n    R = 0.1*eye(4);\n\n    % Initial and reference states\n    x0 = zeros(12,1);\n    xr = [0; 0; 1; 0; 0; 0; 0; 0; 0; 0; 0; 0];\n\n    % Prediction horizon\n    N = 10;\n\n    % Define problem\n    u = sdpvar(repmat(nu,1,N), repmat(1,1,N));\n    x = sdpvar(repmat(nx,1,N+1), repmat(1,1,N+1));\n    constraints = [xmin <= x{1} <= xmax];\n    objective = 0;\n    for k = 1 : N\n        objective = objective + (x{k}-xr)'*Q*(x{k}-xr) + u{k}'*R*u{k};\n        constraints = [constraints, x{k+1} == Ad*x{k} + Bd*u{k}];\n        constraints = [constraints, umin <= u{k}<= umax, xmin <= x{k+1} <= xmax];\n    end\n    objective = objective + (x{N+1}-xr)'*QN*(x{N+1}-xr);\n    options = sdpsettings('solver', 'osqp');\n    controller = optimizer(constraints, objective, options, x{1}, [u{:}]);\n\n    % Simulate in closed loop\n    nsim = 15;\n    for i = 1 : nsim\n        U = controller{x0};\n        x0 = Ad*x0 + Bd*U(:,1);\n    end\n"
  },
  {
    "path": "osqp/docs/examples/portfolio.rst",
    "content": "Portfolio optimization\n======================\n\n\nPortfolio optimization seeks to allocate assets in a way that maximizes the risk adjusted return,\n\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{maximize} & \\mu^T x - \\gamma \\left( x^T \\Sigma x \\right) \\\\\n    \\mbox{subject to} & \\boldsymbol{1}^T x = 1 \\\\\n                      & x \\ge 0\n  \\end{array}\n\n\nwhere :math:`x \\in \\mathbf{R}^{n}` represents the portfolio, :math:`\\mu \\in \\mathbf{R}^{n}` the vector of expected returns, :math:`\\gamma > 0` the risk aversion parameter, and :math:`\\Sigma \\in \\mathbf{S}^{n}_{+}` the risk model covariance matrix.\nThe risk model is usually assumed to be the sum of a diagonal and a rank :math:`k < n` matrix,\n\n\n.. math::\n  \\Sigma = F F^T + D,\n\n\nwhere :math:`F \\in \\mathbf{R}^{n \\times k}` is the factor loading matrix and :math:`D \\in \\mathbf{S}^{n}_{+}` is a diagonal matrix describing the asset-specific risk.\nThe resulting problem has the following equivalent form,\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{minimize} & \\frac{1}{2} x^T D x + \\frac{1}{2} y^T y - \\frac{1}{2\\gamma}\\mu^T x \\\\\n    \\mbox{subject to} & y = F^T x \\\\\n                      & \\boldsymbol{1}^T x = 1 \\\\\n                      & x \\ge 0\n  \\end{array}\n\n\n\nPython\n------\n\n.. code:: python\n\n    import osqp\n    import numpy as np\n    import scipy as sp\n    from scipy import sparse\n\n    # Generate problem data\n    sp.random.seed(1)\n    n = 100\n    k = 10\n    F = sparse.random(n, k, density=0.7, format='csc')\n    D = sparse.diags(np.random.rand(n) * np.sqrt(k), format='csc')\n    mu = np.random.randn(n)\n    gamma = 1\n\n    # OSQP data\n    P = sparse.block_diag([D, sparse.eye(k)], format='csc')\n    q = np.hstack([-mu / (2*gamma), np.zeros(k)])\n    A = sparse.vstack([\n            sparse.hstack([F.T, -sparse.eye(k)]),\n            sparse.hstack([sparse.csc_matrix(np.ones((1, n))), sparse.csc_matrix((1, k))]),\n            sparse.hstack((sparse.eye(n), sparse.csc_matrix((n, k))))\n        ], format='csc')\n    l = np.hstack([np.zeros(k), 1., np.zeros(n)])\n    u = np.hstack([np.zeros(k), 1., np.ones(n)])\n\n    # Create an OSQP object\n    prob = osqp.OSQP()\n\n    # Setup workspace\n    prob.setup(P, q, A, l, u)\n\n    # Solve problem\n    res = prob.solve()\n\n\n\nMatlab\n------\n\n.. code:: matlab\n\n    % Generate problem data\n    rng(1)\n    n = 100;\n    k = 10;\n    F = sprandn(n, k, 0.7);\n    D = sparse(diag( sqrt(k)*rand(n,1) ));\n    mu = randn(n, 1);\n    gamma = 1;\n\n    % OSQP data\n    P = blkdiag(D, speye(k));\n    q = [-mu/(2*gamma); zeros(k, 1)];\n    A = [F', -speye(k);\n         ones(1, n), zeros(1, k);\n         speye(n), sparse(n, k)];\n    l = [zeros(k, 1); 1; zeros(n, 1)];\n    u = [zeros(k, 1); 1; ones(n, 1)];\n\n    % Create an OSQP object\n    prob = osqp;\n\n    % Setup workspace\n    prob.setup(P, q, A, l, u);\n\n    % Solve problem\n    res = prob.solve();\n\n\n\nCVXPY\n-----\n\n.. code:: python\n\n    from cvxpy import *\n    import numpy as np\n    import scipy as sp\n    from scipy import sparse\n\n    # Generate problem data\n    sp.random.seed(1)\n    n = 100\n    k = 10\n    F = sparse.random(n, k, density=0.7, format='csc')\n    D = sparse.diags(np.random.rand(n) * np.sqrt(k), format='csc')\n    mu = np.random.randn(n)\n    gamma = 1\n    Sigma = F*F.T + D\n\n    # Define problem\n    x = Variable(n)\n    objective = mu.T*x - gamma*quad_form(x, Sigma)\n    constraints = [sum(x) == 1, x >= 0]\n\n    # Solve with OSQP\n    Problem(Maximize(objective), constraints).solve(solver=OSQP)\n\n\n\nYALMIP\n------\n\n.. code:: matlab\n\n    % Generate problem data\n    rng(1)\n    n = 100;\n    k = 10;\n    F = sprandn(n, k, 0.7);\n    D = sparse(diag( sqrt(k)*rand(n,1) ));\n    mu = randn(n, 1);\n    gamma = 1;\n    Sigma = F*F' + D;\n\n    % Define problem\n    x = sdpvar(n, 1);\n    objective = gamma * (x'*Sigma*x) - mu'*x;\n    constraints = [sum(x) == 1, x >= 0];\n\n    % Solve with OSQP\n    options = sdpsettings('solver', 'osqp');\n    optimize(constraints, objective, options);\n\n"
  },
  {
    "path": "osqp/docs/examples/setup-and-solve.rst",
    "content": "Setup and solve\n===============\n\n\nConsider the following QP\n\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{minimize} & \\frac{1}{2} x^T \\begin{bmatrix}4 & 1\\\\ 1 & 2 \\end{bmatrix} x + \\begin{bmatrix}1 \\\\ 1\\end{bmatrix}^T x \\\\\n    \\mbox{subject to} & \\begin{bmatrix}1 \\\\ 0 \\\\ 0\\end{bmatrix} \\leq \\begin{bmatrix} 1 & 1\\\\ 1 & 0\\\\ 0 & 1\\end{bmatrix} x \\leq  \\begin{bmatrix}1 \\\\ 0.7 \\\\ 0.7\\end{bmatrix}\n  \\end{array}\n\n\n\nWe show below how to solve the problem in Python, Matlab, Julia and C.\n\n\n\nPython\n------\n\n.. code:: python\n\n    import osqp\n    import numpy as np\n    from scipy import sparse\n\n    # Define problem data\n    P = sparse.csc_matrix([[4, 1], [1, 2]])\n    q = np.array([1, 1])\n    A = sparse.csc_matrix([[1, 1], [1, 0], [0, 1]])\n    l = np.array([1, 0, 0])\n    u = np.array([1, 0.7, 0.7])\n\n    # Create an OSQP object\n    prob = osqp.OSQP()\n\n    # Setup workspace and change alpha parameter\n    prob.setup(P, q, A, l, u, alpha=1.0)\n\n    # Solve problem\n    res = prob.solve()\n\n\n\nMatlab\n------\n\n.. code:: matlab\n\n    % Define problem data\n    P = sparse([4, 1; 1, 2]);\n    q = [1; 1];\n    A = sparse([1, 1; 1, 0; 0, 1]);\n    l = [1; 0; 0];\n    u = [1; 0.7; 0.7];\n\n    % Create an OSQP object\n    prob = osqp;\n\n    % Setup workspace and change alpha parameter\n    prob.setup(P, q, A, l, u, 'alpha', 1);\n\n    % Solve problem\n    res = prob.solve();\n\n\n\nJulia\n------\n\n.. code:: julia\n\n    using OSQP\n    using Compat.SparseArrays\n\n    # Define problem data\n    P = sparse([4. 1.; 1. 2.])\n    q = [1.; 1.]\n    A = sparse([1. 1.; 1. 0.; 0. 1.])\n    l = [1.; 0.; 0.]\n    u = [1.; 0.7; 0.7]\n\n    # Crate OSQP object\n    prob = OSQP.Model()\n\n    # Setup workspace and change alpha parameter\n    OSQP.setup!(prob; P=P, q=q, A=A, l=l, u=u, alpha=1)\n\n    # Solve problem\n    results = OSQP.solve!(prob)\n\n\n\nR\n-\n\n.. code:: r\n\n    library(osqp)\n    library(Matrix)\n\n    # Define problem data\n    P <- Matrix(c(4., 1.,\n                  1., 2.), 2, 2, sparse = TRUE)\n    q <- c(1., 1.)\n    A <- Matrix(c(1., 1., 0.,\n                  1., 0., 1.), 3, 2, sparse = TRUE)\n    l <- c(1., 0., 0.)\n    u <- c(1., 0.7, 0.7)\n\n    # Change alpha parameter and setup workspace\n    settings <- osqpSettings(alpha = 1.0)\n    model <- osqp(P, q, A, l, u, settings)\n\n    # Solve problem\n    res <- model$Solve()\n\n\n\nC\n-\n\n.. code:: c\n\n    #include \"osqp.h\"\n\n    int main(int argc, char **argv) {\n        // Load problem data\n        c_float P_x[3] = {4.0, 1.0, 2.0, };\n        c_int P_nnz = 3;\n        c_int P_i[3] = {0, 0, 1, };\n        c_int P_p[3] = {0, 1, 3, };\n        c_float q[2] = {1.0, 1.0, };\n        c_float A_x[4] = {1.0, 1.0, 1.0, 1.0, };\n        c_int A_nnz = 4;\n        c_int A_i[4] = {0, 1, 0, 2, };\n        c_int A_p[3] = {0, 2, 4, };\n        c_float l[3] = {1.0, 0.0, 0.0, };\n        c_float u[3] = {1.0, 0.7, 0.7, };\n        c_int n = 2;\n        c_int m = 3;\n\n        // Exitflag\n        c_int exitflag = 0;\n\n        // Workspace structures\n        OSQPWorkspace *work;\n        OSQPSettings  *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n        OSQPData      *data     = (OSQPData *)c_malloc(sizeof(OSQPData));\n\n        // Populate data\n        if (data) {\n            data->n = n;\n            data->m = m;\n            data->P = csc_matrix(data->n, data->n, P_nnz, P_x, P_i, P_p);\n            data->q = q;\n            data->A = csc_matrix(data->m, data->n, A_nnz, A_x, A_i, A_p);\n            data->l = l;\n            data->u = u;\n        }\n\n        // Define solver settings as default\n        if (settings) {\n            osqp_set_default_settings(settings);\n            settings->alpha = 1.0; // Change alpha parameter\n        }\n\n        // Setup workspace\n        exitflag = osqp_setup(&work, data, settings);\n\n        // Solve Problem\n        osqp_solve(work);\n\n        // Cleanup\n        if (data) {\n            if (data->A) c_free(data->A);\n            if (data->P) c_free(data->P);\n            c_free(data);\n        }\n        if (settings) c_free(settings);\n\n        return exitflag;\n    };\n"
  },
  {
    "path": "osqp/docs/examples/svm.rst",
    "content": "Support vector machine (SVM)\n============================\n\n*Support vector machine* seeks an affine function that approximately classifies the two sets of points.\nThe problem can be stated as\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{minimize} & \\frac{1}{2} x^T x + \\gamma \\sum_{i=1}^{m} \\max(0, b_i a_i^T x + 1),\n  \\end{array}\n\nwhere :math:`b_i \\in \\{ -1, +1 \\}` is a set label, and :math:`a_i` is a vector of features for the :math:`i`-th point.\nThe problem has the following equivalent form\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{minimize}   & \\frac{1}{2} x^T x + \\gamma \\boldsymbol{1}^T t \\\\\n    \\mbox{subject to} & t \\ge {\\rm diag}(b) Ax + 1 \\\\\n                      & t \\ge 0,\n  \\end{array}\n\nwhere :math:`{\\rm diag}(b)` denotes the diagonal matrix with elements of :math:`b` on its diagonal.\n\n\n\nPython\n------\n\n.. code:: python\n\n    import osqp\n    import numpy as np\n    import scipy as sp\n    from scipy import sparse\n\n    # Generate problem data\n    sp.random.seed(1)\n    n = 10\n    m = 1000\n    N = int(m / 2)\n    gamma = 1.0\n    b = np.hstack([np.ones(N), -np.ones(N)])\n    A_upp = sparse.random(N, n, density=0.5)\n    A_low = sparse.random(N, n, density=0.5)\n    Ad = sparse.vstack([\n            A_upp / np.sqrt(n) + (A_upp != 0.).astype(float) / n,\n            A_low / np.sqrt(n) - (A_low != 0.).astype(float) / n\n         ], format='csc')\n\n    # OSQP data\n    Im = sparse.eye(m)\n    P = sparse.block_diag([sparse.eye(n), sparse.csc_matrix((m, m))], format='csc')\n    q = np.hstack([np.zeros(n), gamma*np.ones(m)])\n    A = sparse.vstack([\n            sparse.hstack([sparse.diags(b).dot(Ad), -Im]),\n            sparse.hstack([sparse.csc_matrix((m, n)), Im])\n        ], format='csc')\n    l = np.hstack([-np.inf*np.ones(m), np.zeros(m)])\n    u = np.hstack([-np.ones(m), np.inf*np.ones(m)])\n\n    # Create an OSQP object\n    prob = osqp.OSQP()\n\n    # Setup workspace\n    prob.setup(P, q, A, l, u)\n\n    # Solve problem\n    res = prob.solve()\n\n\nMatlab\n------\n\n.. code:: matlab\n\n    % Generate problem data\n    rng(1)\n    n = 10;\n    m = 1000;\n    N = ceil(m/2);\n    gamma = 1;\n    A_upp = sprandn(N, n, 0.5);\n    A_low = sprandn(N, n, 0.5);\n    Ad = [A_upp / sqrt(n) + (A_upp ~= 0) / n;\n          A_low / sqrt(n) - (A_low ~= 0) / n];\n    b = [ones(N, 1); -ones(N,1)];\n\n    % OSQP data\n    P = blkdiag(speye(n), sparse(m, m));\n    q = [zeros(n,1); gamma*ones(m,1)];\n    A = [diag(b)*Ad, -speye(m);\n         sparse(m, n), speye(m)];\n    l = [-inf*ones(m, 1); zeros(m, 1)];\n    u = [-ones(m, 1); inf*ones(m, 1)];\n\n    % Create an OSQP object\n    prob = osqp;\n\n    % Setup workspace\n    prob.setup(P, q, A, l, u);\n\n    % Solve problem\n    res = prob.solve();\n\n\n\nCVXPY\n-----\n\n.. code:: python\n\n    from cvxpy import *\n    import numpy as np\n    import scipy as sp\n    from scipy import sparse\n\n    # Generate problem data\n    sp.random.seed(1)\n    n = 10\n    m = 1000\n    N = int(m / 2)\n    gamma = 1.0\n    b = np.hstack([np.ones(N), -np.ones(N)])\n    A_upp = sparse.random(N, n, density=0.5)\n    A_low = sparse.random(N, n, density=0.5)\n    A = sparse.vstack([\n            A_upp / np.sqrt(n) + (A_upp != 0.).astype(float) / n,\n            A_low / np.sqrt(n) - (A_low != 0.).astype(float) / n\n        ], format='csc')\n\n    # Define problem\n    x = Variable(n)\n    objective = 0.5*sum_squares(x) + gamma*sum(pos(diag(b)*A*x + 1))\n\n    # Solve with OSQP\n    Problem(Minimize(objective)).solve(solver=OSQP)\n    \n\n\n\nYALMIP\n------\n\n.. code:: matlab\n\n    % Generate problem data\n    rng(1)\n    n = 10;\n    m = 1000;\n    N = ceil(m/2);\n    gamma = 1;\n    A_upp = sprandn(N, n, 0.5);\n    A_low = sprandn(N, n, 0.5);\n    A = [A_upp / sqrt(n) + (A_upp ~= 0) / n;\n         A_low / sqrt(n) - (A_low ~= 0) / n];\n    b = [ones(N, 1); -ones(N,1)];\n\n    % Define problem\n    x = sdpvar(n, 1);\n    objective = 0.5*norm(x)^2 + gamma*sum(max(diag(b)*A*x + 1, 0));\n\n    % Solve with OSQP\n    options = sdpsettings('solver','osqp');\n    optimize([],objective,options);\n\n"
  },
  {
    "path": "osqp/docs/examples/update-matrices.rst",
    "content": "Update matrices\n===============\n\n\nConsider the following QP\n\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{minimize} & \\frac{1}{2} x^T \\begin{bmatrix}4 & 1\\\\ 1 & 2 \\end{bmatrix} x + \\begin{bmatrix}1 \\\\ 1\\end{bmatrix}^T x \\\\\n    \\mbox{subject to} & \\begin{bmatrix}1 \\\\ 0 \\\\ 0\\end{bmatrix} \\leq \\begin{bmatrix} 1 & 1\\\\ 1 & 0\\\\ 0 & 1\\end{bmatrix} x \\leq \\begin{bmatrix}1 \\\\ 0.7 \\\\ 0.7\\end{bmatrix}\n  \\end{array}\n\n\n\nWe show below how to setup and solve the problem.\nThen we update the matrices :math:`P` and :math:`A` and solve the updated problem\n\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{minimize} & \\frac{1}{2} x^T \\begin{bmatrix}5 & 1.5\\\\ 1.5 & 1 \\end{bmatrix} x + \\begin{bmatrix}1 \\\\ 1\\end{bmatrix}^T x \\\\\n    \\mbox{subject to} & \\begin{bmatrix}1 \\\\ 0 \\\\ 0\\end{bmatrix} \\leq \\begin{bmatrix} 1.2 & 1.1\\\\ 1.5 & 0\\\\ 0 & 0.8\\end{bmatrix} x \\leq \\begin{bmatrix}1 \\\\ 0.7 \\\\ 0.7\\end{bmatrix}\n  \\end{array}\n  \n\n\nPython\n------\n\n.. code:: python\n\n    import osqp\n    import numpy as np\n    from scipy import sparse\n\n    # Define problem data\n    P = sparse.csc_matrix([[4, 1], [1, 2]])\n    q = np.array([1, 1])\n    A = sparse.csc_matrix([[1, 1], [1, 0], [0, 1]])\n    l = np.array([1, 0, 0])\n    u = np.array([1, 0.7, 0.7])\n\n    # Create an OSQP object\n    prob = osqp.OSQP()\n\n    # Setup workspace\n    prob.setup(P, q, A, l, u)\n\n    # Solve problem\n    res = prob.solve()\n\n    # Update problem\n    # NB: Update only upper triangular part of P\n    P_new = sparse.csc_matrix([[5, 1.5], [1.5, 1]])\n    A_new = sparse.csc_matrix([[1.2, 1.1], [1.5, 0], [0, 0.8]])\n    prob.update(Px=sparse.triu(P_new).data, Ax=A_new.data)\n\n    # Solve updated problem\n    res = prob.solve()\n\n\n\nMatlab\n------\n\n.. code:: matlab\n\n    % Define problem data\n    P = sparse([4, 1; 1, 2]);\n    q = [1; 1];\n    A = sparse([1, 1; 1, 0; 0, 1]);\n    l = [1; 0; 0];\n    u = [1; 0.7; 0.7];\n\n    % Create an OSQP object\n    prob = osqp;\n\n    % Setup workspace\n    prob.setup(P, q, A, l, u);\n\n    % Solve problem\n    res = prob.solve();\n\n    % Update problem\n    % NB: Update only upper triangular part of P\n    P_new = sparse([5, 1.5; 1.5, 1]);\n    A_new = sparse([1.2, 1.1; 1.5, 0; 0, 0.8]);\n    prob.update('Px', nonzeros(triu(P_new)), 'Ax', nonzeros(A_new));\n\n    % Solve updated problem\n    res = prob.solve();\n\n\n\nJulia\n------\n\n.. code:: julia\n\n    using OSQP\n    using Compat.SparseArrays, Compat.LinearAlgebra\n\n    # Define problem data\n    P = sparse([4. 1.; 1. 2.])\n    q = [1.; 1.]\n    A = sparse([1. 1.; 1. 0.; 0. 1.])\n    l = [1.; 0.; 0.]\n    u = [1.; 0.7; 0.7]\n\n    # Crate OSQP object\n    prob = OSQP.Model()\n\n    # Setup workspace\n    OSQP.setup!(prob; P=P, q=q, A=A, l=l, u=u)\n\n    # Solve problem\n    results = OSQP.solve!(prob)\n\n    # Update problem\n    # NB: Update only upper triangular part of P\n    P_new = sparse([5. 1.5; 1.5 1.])\n    A_new = sparse([1.2 1.1; 1.5 0.; 0. 0.8])\n    OSQP.update!(prob, Px=triu(P_new).nzval, Ax=A_new.nzval)\n\n    # Solve updated problem\n    results = OSQP.solve!(prob)\n\n\n\nR\n-\n\n.. code:: r\n\n    library(osqp)\n    library(Matrix)\n\n    # Define problem data\n    P <- Matrix(c(4., 1.,\n                  1., 2.), 2, 2, sparse = TRUE)\n    q <- c(1., 1.)\n    A <- Matrix(c(1., 1., 0.,\n                  1., 0., 1.), 3, 2, sparse = TRUE)\n    l <- c(1., 0., 0.)\n    u <- c(1., 0.7, 0.7)\n\n    # Setup workspace\n    model <- osqp(P, q, A, l, u)\n\n    # Solve problem\n    res <- model$Solve()\n\n    # Update problem\n    # NB: Update only upper triangular part of P\n    P_new <- Matrix(c(5., 1.5,\n                      1.5, 1.), 2, 2, sparse = TRUE)\n    A_new <- Matrix(c(1.2, 1.5, 0.,\n                      1.1, 0., 0.8), 3, 2, sparse = TRUE)\n    model$Update(Px = P_new@x, Ax = A_new@x)\n\n    # Solve updated problem\n    res <- model$Solve()\n\n\n\nC\n-\n\n.. code:: c\n\n    #include \"osqp.h\"\n\n    int main(int argc, char **argv) {\n        // Load problem data\n        c_float P_x[3] = {4.0, 1.0, 2.0, };\n        c_float P_x_new[3] = {5.0, 1.5, 1.0, };\n        c_int P_nnz = 3;\n        c_int P_i[3] = {0, 0, 1, };\n        c_int P_p[3] = {0, 1, 3, };\n        c_float q[2] = {1.0, 1.0, };\n        c_float q_new[2] = {2.0, 3.0, };\n        c_float A_x[4] = {1.0, 1.0, 1.0, 1.0, };\n        c_float A_x_new[4] = {1.2, 1.5, 1.1, 0.8, };\n        c_int A_nnz = 4;\n        c_int A_i[4] = {0, 1, 0, 2, };\n        c_int A_p[3] = {0, 2, 4, };\n        c_float l[3] = {1.0, 0.0, 0.0, };\n        c_float l_new[3] = {2.0, -1.0, -1.0, };\n        c_float u[3] = {1.0, 0.7, 0.7, };\n        c_float u_new[3] = {2.0, 2.5, 2.5, };\n        c_int n = 2;\n        c_int m = 3;\n\n        // Exitflag\n        c_int exitflag = 0;\n\n        // Workspace structures\n        OSQPWorkspace *work;\n        OSQPSettings  *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n        OSQPData      *data     = (OSQPData *)c_malloc(sizeof(OSQPData));\n\n        // Populate data\n        if (data) {\n            data = (OSQPData *)c_malloc(sizeof(OSQPData));\n            data->n = n;\n            data->m = m;\n            data->P = csc_matrix(data->n, data->n, P_nnz, P_x, P_i, P_p);\n            data->q = q;\n            data->A = csc_matrix(data->m, data->n, A_nnz, A_x, A_i, A_p);\n            data->l = l;\n            data->u = u;\n        }\n\n        // Define Solver settings as default\n        if (settings) osqp_set_default_settings(settings);\n\n        // Setup workspace\n        exitflag = osqp_setup(&work, data, settings);\n\n        // Solve problem\n        osqp_solve(work);\n\n        // Update problem\n        // NB: Update only upper triangular part of P\n        osqp_update_P(work, P_x_new, OSQP_NULL, 3);\n        osqp_update_A(work, A_x_new, OSQP_NULL, 4);\n\n        // Solve updated problem\n        osqp_solve(work);\n\n        // Cleanup\n        if (data) {\n            if (data->A) c_free(data->A);\n            if (data->P) c_free(data->P);\n            c_free(data);\n        }\n        if (settings) c_free(settings);\n\n        return exitflag;\n    };\n"
  },
  {
    "path": "osqp/docs/examples/update-vectors.rst",
    "content": "Update vectors\n==============\n\n\nConsider the following QP\n\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{minimize} & \\frac{1}{2} x^T \\begin{bmatrix}4 & 1\\\\ 1 & 2 \\end{bmatrix} x + \\begin{bmatrix}1 \\\\ 1\\end{bmatrix}^T x \\\\\n    \\mbox{subject to} & \\begin{bmatrix}1 \\\\ 0 \\\\ 0\\end{bmatrix} \\leq \\begin{bmatrix} 1 & 1\\\\ 1 & 0\\\\ 0 & 1\\end{bmatrix} x \\leq \\begin{bmatrix}1 \\\\ 0.7 \\\\ 0.7\\end{bmatrix}\n  \\end{array}\n\n\n\nWe show below how to setup and solve the problem.\nThen we update the vectors :math:`q`, :math:`l`, and :math:`u` and solve the updated problem\n\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{minimize} & \\frac{1}{2} x^T \\begin{bmatrix}4 & 1\\\\ 1 & 2 \\end{bmatrix} x + \\begin{bmatrix}2 \\\\ 3\\end{bmatrix}^T x \\\\\n    \\mbox{subject to} & \\begin{bmatrix}2 \\\\ -1 \\\\ -1\\end{bmatrix} \\leq \\begin{bmatrix} 1 & 1\\\\ 1 & 0\\\\ 0 & 1\\end{bmatrix} x \\leq \\begin{bmatrix}2 \\\\ 2.5 \\\\ 2.5\\end{bmatrix}\n  \\end{array}\n  \n\n\nPython\n------\n\n.. code:: python\n\n    import osqp\n    import numpy as np\n    from scipy import sparse\n\n    # Define problem data\n    P = sparse.csc_matrix([[4, 1], [1, 2]])\n    q = np.array([1, 1])\n    A = sparse.csc_matrix([[1, 1], [1, 0], [0, 1]])\n    l = np.array([1, 0, 0])\n    u = np.array([1, 0.7, 0.7])\n\n    # Create an OSQP object\n    prob = osqp.OSQP()\n\n    # Setup workspace\n    prob.setup(P, q, A, l, u)\n\n    # Solve problem\n    res = prob.solve()\n\n    # Update problem\n    q_new = np.array([2, 3])\n    l_new = np.array([2, -1, -1])\n    u_new = np.array([2, 2.5, 2.5])\n    prob.update(q=q_new, l=l_new, u=u_new)\n\n    # Solve updated problem\n    res = prob.solve()\n\n\n\nMatlab\n------\n\n.. code:: matlab\n\n    % Define problem data\n    P = sparse([4, 1; 1, 2]);\n    q = [1; 1];\n    A = sparse([1, 1; 1, 0; 0, 1]);\n    l = [1; 0; 0];\n    u = [1; 0.7; 0.7];\n\n    % Create an OSQP object\n    prob = osqp;\n\n    % Setup workspace\n    prob.setup(P, q, A, l, u);\n\n    % Solve problem\n    res = prob.solve();\n\n    % Update problem\n    q_new = [2; 3];\n    l_new = [2; -1; -1];\n    u_new = [2; 2.5; 2.5];\n    prob.update('q', q_new, 'l', l_new, 'u', u_new);\n\n    % Solve updated problem\n    res = prob.solve();\n\n\n\nJulia\n------\n\n.. code:: julia\n\n    using OSQP\n    using Compat.SparseArrays\n\n    # Define problem data\n    P = sparse([4. 1.; 1. 2.])\n    q = [1.; 1.]\n    A = sparse([1. 1.; 1. 0.; 0. 1.])\n    l = [1.; 0.; 0.]\n    u = [1.; 0.7; 0.7]\n\n    # Crate OSQP object\n    prob = OSQP.Model()\n\n    # Setup workspace\n    OSQP.setup!(prob; P=P, q=q, A=A, l=l, u=u)\n\n    # Solve problem\n    results = OSQP.solve!(prob)\n\n    # Update problem\n    q_new = [2.; 3.]\n    l_new = [2.; -1.; -1.]\n    u_new = [2.; 2.5; 2.5]\n    OSQP.update!(prob, q=q_new, l=l_new, u=u_new)\n\n    # Solve updated problem\n    results = OSQP.solve!(prob)\n\n\n\nR\n-\n\n.. code:: r\n\n    library(osqp)\n    library(Matrix)\n\n    # Define problem data\n    P <- Matrix(c(4., 1.,\n                  1., 2.), 2, 2, sparse = TRUE)\n    q <- c(1., 1.)\n    A <- Matrix(c(1., 1., 0.,\n                  1., 0., 1.), 3, 2, sparse = TRUE)\n    l <- c(1., 0., 0.)\n    u <- c(1., 0.7, 0.7)\n\n    # Setup workspace\n    model <- osqp(P, q, A, l, u)\n\n    # Solve problem\n    res <- model$Solve()\n\n    # Update problem\n    q_new <- c(2., 3.)\n    l_new <- c(2., -1., -1.)\n    u_new <- c(2., 2.5, 2.5)\n    model$Update(q = q_new, l = l_new, u = u_new)\n\n    # Solve updated problem\n    res <- model$Solve()\n\n\n\nC\n-\n\n.. code:: c\n\n    #include \"osqp.h\"\n\n    int main(int argc, char **argv) {\n        // Load problem data\n        c_float P_x[3] = {4.0, 1.0, 2.0, };\n        c_int P_nnz = 3;\n        c_int P_i[3] = {0, 0, 1, };\n        c_int P_p[3] = {0, 1, 3, };\n        c_float q[2] = {1.0, 1.0, };\n        c_float q_new[2] = {2.0, 3.0, };\n        c_float A_x[4] = {1.0, 1.0, 1.0, 1.0, };\n        c_int A_nnz = 4;\n        c_int A_i[4] = {0, 1, 0, 2, };\n        c_int A_p[3] = {0, 2, 4, };\n        c_float l[3] = {1.0, 0.0, 0.0, };\n        c_float l_new[3] = {2.0, -1.0, -1.0, };\n        c_float u[3] = {1.0, 0.7, 0.7, };\n        c_float u_new[3] = {2.0, 2.5, 2.5, };\n        c_int n = 2;\n        c_int m = 3;\n\n        // Exitflag\n        c_int exitflag = 0;\n\n        // Workspace structures\n        OSQPWorkspace *work;\n        OSQPSettings  *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n        OSQPData      *data     = (OSQPData *)c_malloc(sizeof(OSQPData));\n\n        // Populate data\n        if (data) {\n            data->n = n;\n            data->m = m;\n            data->P = csc_matrix(data->n, data->n, P_nnz, P_x, P_i, P_p);\n            data->q = q;\n            data->A = csc_matrix(data->m, data->n, A_nnz, A_x, A_i, A_p);\n            data->l = l;\n            data->u = u;\n        }\n\n        // Define solver settings as default\n        if (settings) osqp_set_default_settings(settings);\n\n        // Setup workspace\n        exitflag = osqp_setup(&work, data, settings);\n\n        // Solve problem\n        osqp_solve(work);\n\n        // Update problem\n        osqp_update_lin_cost(work, q_new);\n        osqp_update_bounds(work, l_new, u_new);\n\n        // Solve updated problem\n        osqp_solve(work);\n\n        // Cleanup\n        if (data) {\n            if (data->A) c_free(data->A);\n            if (data->P) c_free(data->P);\n            c_free(data);\n        }\n        if (settings) c_free(settings);\n\n        return exitflag;\n    };\n"
  },
  {
    "path": "osqp/docs/get_started/CC++.rst",
    "content": ".. _install_osqp_libs:\n\nCC++\n=====\n\nBinaries\n--------\n\nPrecompiled platform-dependent shared and static libraries are available on `Bintray <https://bintray.com/bstellato/generic/OSQP/0.6.0>`_.\nWe here assume that the user uncompressed each archive to :code:`OSQP_FOLDER`.\n\nEach archive contains static :code:`OSQP_FOLDER/lib/libosqp.a` and shared :code:`OSQP_FOLDER/lib/libosqp.ext` libraries to be used to interface OSQP to any C/C++ software.\nThe extension :code:`.ext` is platform dependent and is :code:`.so` for Linux, :code:`.dylib` for Mac and :code:`.dll` for Windows.\nThe required include files can be found in :code:`OSQP_FOLDER/include`.\n\nSimply compile with the linker option with :code:`-LOSQP_FOLDER/lib` and :code:`-losqp`.\n\nIf you are interested in development builds, you can find them on `Bintray <https://dl.bintray.com/bstellato/generic/OSQP-dev/>`__.\n\nSources\n-------\n\nThe OSQP libraries can also be compiled from sources. For more details see :ref:`build_from_sources`.\n\n\nIncluding OSQP in CMake projects\n--------------------------------\nIf you compiled OSQP from sources and followed the CMake installation instructions in :ref:`install_the_binaries` section, you can include the package in another CMake project with the following lines depending on whether you need a shared or a static library\n\n.. code::\n\n   # Find OSQP library and headers\n   find_package(osqp REQUIRED)\n\n   # Link the OSQP shared library\n   target_link_libraries(yourTarget PRIVATE osqp::osqp)\n\n   # or...\n\n   # Link the OSQP static library\n   target_link_libraries(yourTarget PRIVATE osqp::osqpstatic)\n\n\n"
  },
  {
    "path": "osqp/docs/get_started/cutest.rst",
    "content": "CUTEst\n=======\n\nTo be able to use OSQP and `CUTEst <https://github.com/ralna/CUTEst/wiki>`_ you need to\n\n* Install `CUTEst <https://github.com/ralna/CUTEst/wiki>`_\n* Compile :ref:`OSQP from sources <build_from_sources>`\n* Set the environment variable :code:`OSQP` to the main OSQP directory containing source code for which the compiled binary libraries lie in :code:`$OSQP/build/out`.\n\nFor more details, see the `README.osqp <https://github.com/ralna/CUTEst/blob/master/src/osqp/README.osqp>`_ file in the CUTEst repository.\n\n\n"
  },
  {
    "path": "osqp/docs/get_started/index.rst",
    "content": "Get started\n===============\n\nTo get started simply choose your language interface and follow the easy installation instructions below:\n\n.. toctree::\n   :maxdepth: 1\n   :glob:\n\n   sources.rst\n   CC++.rst\n   python.rst\n   julia.rst\n   matlab.rst\n   r.rst\n   cutest.rst\n   linear_system_solvers.rst\n"
  },
  {
    "path": "osqp/docs/get_started/julia.rst",
    "content": "Julia\n======\n\nJulia interface can be directly installed from Julia package manager by running\n\n.. code:: julia\n\n   ] add OSQP\n\n\nThe interface code is available on `GitHub <https://github.com/oxfordcontrol/OSQP.jl>`_.\n\n"
  },
  {
    "path": "osqp/docs/get_started/linear_system_solvers.rst",
    "content": ".. _linear_system_solvers_installation :\n\nLinear System Solvers\n======================\n\nThe linear system solver is a core part of the OSQP algorithm.\nDepending on the problem instance, different linear system solvers can greatly speedup or reduce the computation time of OSQP.\nTo set the preferred linear system solver, see :ref:`linear_system_solvers_setting`.\n\nDynamic shared library loading\n------------------------------\nOSQP dynamically loads the shared libraries related to the selected external solver. Thus, there is no need to link it at compile time.\nThe only requirement is that the shared library related to the solver is in the library path of the operating system\n\n+------------------+---------------------------+----------------+\n| Operating system | Path variable             | Extension      |\n+==================+===========================+================+\n| Linux            | :code:`LD_LIBRARY_PATH`   | :code:`.so`    |\n+------------------+---------------------------+----------------+\n| Mac              | :code:`DYLD_LIBRARY_PATH` | :code:`.dylib` |\n+------------------+---------------------------+----------------+\n| Windows          | :code:`PATH`              | :code:`.dll`   |\n+------------------+---------------------------+----------------+\n\n\n\n\n\nQDLDL\n---------------\nOSQP comes with `QDLDL <https://github.com/oxfordcontrol/qdldl>`_ internally installed.\nIt does not require any external shared library.\nQDLDL is a sparse direct solver that works well for most small to medium sized problems.\nHowever, it becomes not really efficient for large scale problems since it is not multi-threaded.\n\n\nMKL Pardiso\n-----------\n`MKL Pardiso <https://software.intel.com/en-us/mkl-developer-reference-fortran-intel-mkl-pardiso-parallel-direct-sparse-solver-interface>`_ is an efficient multi-threaded linear system solver that works well for large scale problems part of the Intel Math Kernel Library.\nIntel offers `free lincenses <https://software.intel.com/en-us/articles/free-mkl>`_ for MKL for most non-commercial applications.\n\nInstall with MKL\n^^^^^^^^^^^^^^^^\nWe can install MKL Pardiso by using the standard `MKL installer <https://software.intel.com/en-us/mkl>`_.\nThe main library to be loaded is called :code:`libmkl_rt`.\nTo add it, together with its dependencies, to your path, just execute the automatic MKL script.\n\n+------------------+------------------------------------------------+\n| Operating system | Script                                         |\n+==================+================================================+\n| Linux            | :code:`source $MKLROOT/bin/mklvars.sh intel64` |\n+------------------+------------------------------------------------+\n| Mac              | :code:`source $MKLROOT/bin/mklvars.sh intel64` |\n+------------------+------------------------------------------------+\n| Windows          | :code:`%MKLROOT%\\mklvars.bat intel64`          |\n+------------------+------------------------------------------------+\n\nwhere :code:`MKLROOT` is the MKL installation directory.\n\nInstall with Anaconda\n^^^^^^^^^^^^^^^^^^^^^\n`Anaconda Python distribution <https://www.anaconda.com/download/>`_ comes with the intel MKL libraries preinstalled including MKL Pardiso.\nTo use this version, the Anaconda libraries folders have to be in your system path.\nAnaconda environments should add them automatically so in most cases you do not have to do anything. If you get an error where OSQP cannot find MKL, you can add the right path by adding the output from the following command to your path variable:\n\n.. code::\n\n   echo \"`ls -rd ${CONDA_ROOT}/pkgs/*/ | grep mkl-2 | head -n 1`lib:`ls -rd ${CONDA_ROOT}/pkgs/*/ | grep intel-openmp- | head -n 1`lib\"\n\n\nwhere :code:`ANACONDA_ROOT` is the root of your anaconda installation.\n\n"
  },
  {
    "path": "osqp/docs/get_started/matlab.rst",
    "content": "Matlab\n======\nOSQP Matlab interface requires Matlab 2015b or newer.\n\n\nBinaries\n--------\n\nPrecompiled platform-dependent Matlab binaries are available on `Bintray <https://bintray.com/bstellato/generic/OSQP>`_.\n\nTo install the interface, just run the following commands:\n\n.. code:: matlab\n\n    websave('install_osqp.m', 'https://dl.bintray.com/bstellato/generic/OSQP/0.6.0/install_osqp.m');\n    install_osqp\n\n\nSources\n-------\n\nYou need to install the following (see :ref:`build_from_sources` for more details):\n\n- A supported 64bit `C/C++ compiler <https://www.mathworks.com/support/compilers.html>`_\n- `CMake <https://cmake.org/>`_\n\n\n\nAfter you install both, check that your compiler is selected by executing\n\n.. code:: matlab\n\n   mex -setup\n\n.. note::\n\n   **Windows**: If Matlab does not find TDM-GCC, you need to set the environment variable :code:`MW_MINGW64_LOC` as follows\n\n   .. code:: matlab\n\n      setenv('MW_MINGW64_LOC', 'C:\\TDM-GCC-64')\n\n\n   where :code:`C:\\TDM-GCC-64` is the installation folder for TDM-GCC.\n\nYou can now build the interface by running inside Matlab\n\n.. code:: matlab\n\n   !git clone --recurse-submodules https://github.com/oxfordcontrol/osqp-matlab\n   cd osqp-matlab\n   make_osqp\n\n\nThen you can add the interface to the search path by executing from the same directory\n\n.. code:: matlab\n\n   addpath(pwd)\n   savepath\n"
  },
  {
    "path": "osqp/docs/get_started/python.rst",
    "content": "Python\n======\n\nPython interface supports Python 2.7 and 3.5 or newer.\n\nPip\n----\n\n.. code:: bash\n\n   pip install osqp\n\n\nAnaconda\n--------\n\n.. code:: bash\n\n   conda install -c conda-forge osqp\n\n\nSources\n---------\nYou need to install the following (see :ref:`build_from_sources` for more details):\n\n- `GCC compiler <https://gcc.gnu.org/>`_\n- `CMake <https://cmake.org/>`_\n\n.. note::\n\n   **Windows**: You need to install **also** the Visual Studio C++ compiler:\n\n   * Python 2: `Visual C++ 9.0 for Python (VC 9.0) <https://www.microsoft.com/en-us/download/details.aspx?id=44266>`_\n\n   * Python 3: `Build Tools for Visual Studio 2017 <https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2017>`_\n\n\nNow you are ready to build OSQP python interface from sources. Run the following in your terminal\n\n.. code:: bash\n\n   git clone --recurse-submodules https://github.com/oxfordcontrol/osqp-python\n   cd osqp-python\n   python setup.py install\n"
  },
  {
    "path": "osqp/docs/get_started/r.rst",
    "content": "R\n======\n\nBinaries\n--------\n\nA pre-compiled version of the OSQP R interface can be installed directly from within R.   Note that this will install the OSQP interface from the current CRAN repository, which may not be the most up-to-date version:\n\n.. code:: r\n\n  install.packages(\"osqp\")\n\n\n\nThe pre-compiled binaries can also be downloaded `directly from the CRAN server\n<https://cran.r-project.org/web/packages/osqp/>`_.\n\nFrom Sources\n------------\n\nIf you would like to use the most recent version of OSQP-R and have access to git on your machine along with a suitable compiler, then you can do the following from within a terminal:\n\n.. code:: r\n\n  git clone --recursive https://github.com/oxfordcontrol/osqp-r.git\n  cd osqp-r\n  R CMD install .\n\nFrom Sources (within R)\n-----------------------\n\nIf you would like to install the latest version directly from with R (e.g.\\ because you do not have ``git`` installed) and have a suitable compiler, then you can do the following from within R:\n\n.. code:: r\n\n  install.packages(\"remotes\")\n  remotes::install_github(\"r-lib/remotes#103\")\n  remotes::install_git(\"git://github.com/OxfordControl/osqp-r\",submodules = TRUE)\n\nNote that the second line above is necessary because the \"remotes\" package in R does not currently support recursive cloning of git submodules.\n"
  },
  {
    "path": "osqp/docs/get_started/sources.rst",
    "content": ".. _build_from_sources:\n\n\nBuild from sources\n==================\n\nInstall GCC and CMake\n----------------------\n\nThe main compilation directives are specified using\n\n- `GCC compiler <https://gcc.gnu.org/>`_ to build the binaries\n- `CMake <https://cmake.org/>`__ to create the Makefiles\n\n\nLinux\n^^^^^\nBoth :code:`gcc` and :code:`cmake` commands are already installed by default.\n\nMac OS\n^^^^^^\n\nInstall Xcode and command line tools\n\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n\n#. Install the latest release of `Xcode <https://developer.apple.com/download/>`_.\n\n#. Install the command line tools by executing from the terminal\n\n    .. code:: bash\n\n        xcode-select --install\n\nInstall CMake via Homebrew\n\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n\n#. Install `Homebrew <https://brew.sh/>`_ and update its packages to the latest version.\n\n#. Install cmake by executing\n\n    .. code:: bash\n\n        brew install cmake\n\n\nWindows\n^^^^^^^\n\n#. Install `TDM-GCC <http://tdm-gcc.tdragon.net/download>`_ 32bit or 64bit depending on your platform.\n\n#. Install the latest binaries of `CMake <https://cmake.org/download/#latest>`__.\n\n\nBuild the binaries\n------------------\n\nRun the following commands from the terminal\n\n#. You first need to get the sources from one of these two options:\n\n    * Download the compressed file `from bintray.com <https://dl.bintray.com/bstellato/generic/OSQP/0.4.1/osqp-0.4.1.tar.gz>`_.\n\n    * Clone the repository\n\n        .. code:: bash\n\n            git clone --recursive https://github.com/oxfordcontrol/osqp\n\n#. Create :code:`build` directory and change directory\n\n        .. code:: bash\n\n            cd osqp\n            mkdir build\n            cd build\n\n#. Create Makefiles\n\n    - In Linux and Mac OS run\n\n        .. code:: bash\n\n            cmake -G \"Unix Makefiles\" ..\n\n    - In Windows run\n\n        .. code:: bash\n\n            cmake -G \"MinGW Makefiles\" ..\n\n\n#. Compile OSQP\n\n    .. code:: bash\n\n       cmake --build .\n\n\nThanks to CMake, it is possible to create projects for a wide variety of IDEs; see `here <https://cmake.org/cmake/help/latest/manual/cmake-generators.7.html>`_ for more details. For example, to create a project for Visual Studio 14 2015, it is just necessary to run\n\n.. code:: bash\n\n   cmake -G \"Visual Studio 14 2015\" ..\n\n\nThe compilation will generate the demo :code:`osqp_demo` and the unittests :code:`osqp_tester` executables. In the case of :code:`Unix` or :code:`MinGW` :code:`Makefiles` option they are located in the :code:`build/out/` directory.  Run them to check that the compilation was correct.\n\n\nOnce the sources are built, the generated static :code:`build/out/libosqp.a` and shared :code:`build/out/libosqp.ext` libraries can be used to interface any C/C++ software to OSQP (see :ref:`install_osqp_libs` installation).\n\n.. _install_the_binaries:\n\nInstall the binaries\n--------------------\n\n\n\nTo install the generated libraries and headers to a system-wide location compatible with `GNU standards <http://www.gnu.org/prep/standards/html_node/Directory-Variables.html>`_ it is just necessary to run\n\n.. code:: bash\n\n   cmake --build . --target install\n\nThis code installs the libraries in :code:`libdir` and the headers into :code:`includedir/osqp`. For mode details see the defaults folders on the `GNU standards <http://www.gnu.org/prep/standards/html_node/Directory-Variables.html>`_ website.\nTo change the installation prefix, in the \"Create Makefiles\" step above, you need to specify the destination folder as :code:`cmake -DCMAKE_INSTALL_PREFIX:PATH=myfolder ..`.\n\n.. note:: This step requires write permissions in the destination\n\t  folders. You might be able to gain access using the\n\t  :code:`sudo` command.\n\nWe provided also an uninstall routine to remove the copied files by running\n\n.. code:: bash\n\n   cmake --build . --target uninstall\n\nNote that this corresponds to running :code:`make install` and :code:`make uninstall` on unix machines.\n"
  },
  {
    "path": "osqp/docs/index.rst",
    "content": "OSQP solver documentation\n==========================\n**Join our** `forum <https://osqp.discourse.group/>`_ **for any\nquestions related to the solver!**\n\nThe OSQP (Operator Splitting Quadratic Program) solver is a numerical\noptimization package for solving convex quadratic programs in the form\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{minimize} & \\frac{1}{2} x^T P x + q^T x \\\\\n    \\mbox{subject to} & l \\leq A x \\leq u\n  \\end{array}\n\nwhere :math:`x` is the optimization variable and\n:math:`P \\in \\mathbf{S}^{n}_{+}` a positive semidefinite matrix.\n\n**Code available on** `GitHub <https://github.com/oxfordcontrol/osqp>`_.\n\n.. rubric:: Citing OSQP\n\nIf you are using OSQP for your work, we encourage you to\n\n* :ref:`Cite the related papers <citing>`\n* Put a star on GitHub |github-star|\n\n\n.. |github-star| image:: https://img.shields.io/github/stars/oxfordcontrol/osqp.svg?style=social&label=Star\n  :target: https://github.com/oxfordcontrol/osqp\n\n\n**We are looking forward to hearing your success stories with OSQP!** Please `share them with us <bartolomeo.stellato@gmail.com>`_.\n\n.. rubric:: Features\n\n\n.. glossary::\n\n    Efficient\n        It uses a custom ADMM-based first-order method requiring only a single matrix factorization in the setup phase. All the other operations are extremely cheap. It also implements custom sparse linear algebra routines exploiting structures in the problem data.\n\n    Robust\n        The algorithm is absolutely division free after the setup and it requires no assumptions on problem data (the problem only needs to be convex). It just works!\n\n    Detects primal / dual infeasible problems\n        When the problem is primal or dual infeasible, OSQP detects it. It is the first available QP solver based on first-order methods able to do so.\n\n    Embeddable\n        It has an easy interface to generate customized embeddable C code with no memory manager required.\n\n    Library-free\n        It requires no external library to run.\n\n    Efficiently warm started\n        It can be easily warm-started and the matrix factorization can be cached to solve parametrized problems extremely efficiently.\n\n    Interfaces\n        It can be interfaced to C, C++, Fortran (soon!), Python, Julia and Matlab.\n\n\n\n.. rubric:: License\n\nOSQP is distributed under the `Apache 2.0 License <https://www.apache.org/licenses/LICENSE-2.0>`_\n\n\n\n.. rubric:: Credits\n\nThe following people have been involved in the development of OSQP:\n\n* `Bartolomeo Stellato <https://stellato.io/>`_ (MIT): main development\n* `Goran Banjac <https://people.ee.ethz.ch/~gbanjac/>`_ (ETH Zürich): main development\n* `Nicholas Moehle <https://www.nicholasmoehle.com/>`_ (Stanford University): methods, maths, and code generation\n* `Paul Goulart <http://users.ox.ac.uk/~engs1373/>`_ (University of Oxford): methods, maths, and Matlab interface\n* `Alberto Bemporad <http://cse.lab.imtlucca.it/~bemporad/>`_ (IMT Lucca): methods and maths\n* `Stephen Boyd <https://web.stanford.edu/~boyd/>`_ (Stanford University): methods and maths\n* `Ian McInerney <mailto:i.mcinerney17@imperial.ac.uk>`_ (Imperial College London): code generation, software development\n\nInterfaces development\n\n* `Nick Gould <http://www.numerical.rl.ac.uk/people/nimg/nimg.html>`_ (Rutherford Appleton Laboratory): Fortran and CUTEst interfaces\n* `Ed Barnard <eabarnard@gmail.com>`_ (University of Oxford): Rust interface\n\n\n.. rubric:: Bug reports and support\n\nPlease report any issues via the `Github issue tracker <https://github.com/oxfordcontrol/osqp/issues>`_. All types of issues are welcome including bug reports, documentation typos, feature requests and so on.\n\n\n.. rubric:: Numerical benchmarks\n\nNumerical benchmarks against other solvers are available `here <https://github.com/oxfordcontrol/osqp_benchmarks>`_.\n\n\n.. toctree::\n   :hidden:\n   :maxdepth: 2\n   :caption: User Documentation\n\n   solver/index\n   get_started/index\n   interfaces/index\n   parsers/index\n   codegen/index\n   examples/index\n   contributing/index\n   citing/index\n"
  },
  {
    "path": "osqp/docs/interfaces/CC++.rst",
    "content": ".. _c_cpp_interface:\n\nC/C++\n=====\n\n\n\n\n.. _C_main_API:\n\nMain solver API\n---------------\n\nThe main C/C++ API is imported from the header :code:`osqp.h` and provides the following functions\n\n\n.. doxygenfunction:: osqp_setup\n\n.. doxygenfunction:: osqp_solve\n\n.. doxygenfunction:: osqp_cleanup\n\n\n.. _C_sublevel_API:\n\nSublevel API\n------------\nSublevel C/C++ API is also imported from the header :code:`osqp.h` and provides the following functions\n\nWarm start\n^^^^^^^^^^\nOSQP automatically warm starts primal and dual variables from the previous QP solution. If you would like to warm start their values manually, you can use\n\n.. doxygenfunction:: osqp_warm_start\n\n.. doxygenfunction:: osqp_warm_start_x\n\n.. doxygenfunction:: osqp_warm_start_y\n\n\n.. _c_cpp_update_data :\n\nUpdate problem data\n^^^^^^^^^^^^^^^^^^^\nProblem data can be updated without executing the setup again using the following functions.\n\n.. doxygenfunction:: osqp_update_lin_cost\n\n.. doxygenfunction:: osqp_update_lower_bound\n\n.. doxygenfunction:: osqp_update_upper_bound\n\n.. doxygenfunction:: osqp_update_bounds\n\n.. doxygenfunction:: osqp_update_P\n\n.. doxygenfunction:: osqp_update_A\n\n.. doxygenfunction:: osqp_update_P_A\n\n\n\n.. _c_cpp_data_types :\n\nData types\n----------\n\nThe most basic used datatypes are\n\n* :code:`c_int`: can be :code:`long` or :code:`int` if the compiler flag :code:`DLONG` is set or not\n* :code:`c_float`: can be a :code:`float` or a :code:`double` if the compiler flag :code:`DFLOAT` is set or not.\n\n\n\nThe relevant structures used in the API are\n\nData\n^^^^\n\n.. doxygenstruct:: OSQPData\n   :members:\n\nThe matrices are defined in `Compressed Sparse Column (CSC) format <https://people.sc.fsu.edu/~jburkardt/data/cc/cc.html>`_ using zero-based indexing.\n\n.. doxygenstruct:: csc\n   :members:\n\nSettings\n^^^^^^^^\n\n.. doxygenstruct:: OSQPSettings\n  :members:\n\nSolution\n^^^^^^^^\n\n.. doxygenstruct:: OSQPSolution\n   :members:\n\nInfo\n^^^^^\n\n.. doxygenstruct:: OSQPInfo\n   :members:\n\nWorkspace\n^^^^^^^^^\n\n.. doxygenstruct:: OSQPWorkspace\n   :members:\n\n\nScaling\n^^^^^^^\n\n.. doxygenstruct:: OSQPScaling\n   :members:\n\nPolish\n^^^^^^\n.. doxygenstruct:: OSQPPolish\n  :members:\n\n\n\n.. TODO: Add sublevel API\n.. TODO: Add using your own linear system solver\n"
  },
  {
    "path": "osqp/docs/interfaces/cutest.rst",
    "content": ".. _cutest_interface:\n\nCUTEst\n======\n\nThe command to solve a problem in SIF format contained in the file\nprobname.SIF is\n\n.. code::\n\n    runcutest -p osqp -D probname.SIF\n\nSee the man page for runcutest for more details or other options.\n\nThe default OSQP settings used in CUTEst appear in the `OSQP.SPC <https://github.com/ralna/CUTEst/blob/master/src/osqp/OSQP.SPC>`_ file. \nOptionally, new parameter values to overwrite the default values can be stored in a file :code:`OSQP.SPC` in the directory where the :code:`runcutest` command is executed.\nThe format of the file :code:`OSQP.SPC` is the parameter name starting in the first column followed by one or more spaces and then the parameter value. \nThe parameter names are case sensitive. \nIf the parameter value is :code:`true` or :code:`false`, then use :code:`1` for true and :code:`0` for :code:`false`.\n\nFor more details see the `README.osqp <https://github.com/ralna/CUTEst/blob/master/src/osqp/README.osqp>`_ file in the CUTEst repository.\n\n"
  },
  {
    "path": "osqp/docs/interfaces/eigen.rst",
    "content": ".. _eigen_interface:\n\nEigen\n======\n\nThe Eigen interface is documented `here <https://robotology.github.io/osqp-eigen/doxygen/doc/html/index.html>`_.\n"
  },
  {
    "path": "osqp/docs/interfaces/fortran.rst",
    "content": ".. _fortran_interface:\n\n\nFortran\n========\n\nThe interface is still experimental.\nYou can find a demo of how it works in the `oxfordcontrol/osqp-fortran <https://github.com/oxfordcontrol/osqp-fortran/blob/master/demo/osqp_demo_fortran.F90>`_ repository.\n"
  },
  {
    "path": "osqp/docs/interfaces/index.rst",
    "content": "Interfaces\n============\n\nOSQP has several interfaces. The information about settings, status values and how to assign different linear system solvers appear in the following links\n\n* :ref:`Solver settings <solver_settings>`\n* :ref:`Linear system solvers <linear_system_solvers_setting>`\n* :ref:`Status values <status_values>`\n\n\n\n.. toctree::\n   :maxdepth: 1\n   :glob:\n   :hidden:\n\n   solver_settings.rst\n   linear_systems_solvers.rst\n   status_values.rst\n\n\nOfficial\n----------\n\n+------------------------------------+----------------------------------------------------------+------------------------------------------------------------------------------------------+\n| Language                           | Maintainers                                              | Repository                                                                               |\n+====================================+==========================================================+==========================================================================================+\n| :ref:`C/C++ <c_cpp_interface>`     | | `Bartolomeo Stellato <bartolomeo.stellato@gmail.com>`_ | `github.com/oxfordcontrol/osqp <https://github.com/oxfordcontrol/osqp>`_                 |\n|                                    | | `Goran Banjac <gbanjac@control.ee.ethz.ch>`_           |                                                                                          |\n|                                    | | `Paul Goulart <paul.goulart@eng.ox.ac.uk>`_            |                                                                                          |\n+------------------------------------+----------------------------------------------------------+------------------------------------------------------------------------------------------+\n| :ref:`Python <python_interface>`   | | `Bartolomeo Stellato <bartolomeo.stellato@gmail.com>`_ | `github.com/oxfordcontrol/osqp-python <https://github.com/oxfordcontrol/osqp-python>`_   |\n|                                    | | `Goran Banjac <gbanjac@control.ee.ethz.ch>`_           |                                                                                          |\n+------------------------------------+----------------------------------------------------------+------------------------------------------------------------------------------------------+\n| :ref:`Matlab <matlab_interface>`   | | `Bartolomeo Stellato <bartolomeo.stellato@gmail.com>`_ | `github.com/oxfordcontrol/osqp-matlab <https://github.com/oxfordcontrol/osqp-matlab>`_   |\n|                                    | | `Goran Banjac <gbanjac@control.ee.ethz.ch>`_           |                                                                                          |\n|                                    | | `Paul Goulart <paul.goulart@eng.ox.ac.uk>`_            |                                                                                          |\n+------------------------------------+----------------------------------------------------------+------------------------------------------------------------------------------------------+\n| :ref:`Julia <julia_interface>`     | | `Twan Koolen <tkoolen@mit.edu>`_                       | `github.com/oxfordcontrol/OSQP.jl <https://github.com/oxfordcontrol/OSQP.jl>`_           |\n|                                    | | `Benoît Legat <benoit.legat@uclouvain.be>`_            |                                                                                          |\n|                                    | | `Bartolomeo Stellato <bartolomeo.stellato@gmail.com>`_ |                                                                                          |\n+------------------------------------+----------------------------------------------------------+------------------------------------------------------------------------------------------+\n| :ref:`R <rlang_interface>`         | | `Bartolomeo Stellato <bartolomeo.stellato@gmail.com>`_ | `github.com/oxfordcontrol/osqp-r <https://github.com/oxfordcontrol/osqp-r>`_             |\n|                                    | | `Paul Goulart <paul.goulart@eng.ox.ac.uk>`_            |                                                                                          |\n+------------------------------------+----------------------------------------------------------+------------------------------------------------------------------------------------------+\n\n\n\n.. toctree::\n   :maxdepth: 1\n   :glob:\n   :hidden:\n\n   CC++.rst\n   python.rst\n   matlab.rst\n   julia.rst\n   rlang.rst\n\n\n\n\n\n\n\n\n\nCommunity Maintained\n--------------------\n\n\n+---------------------------------------------+----------------------------------------------------------+------------------------------------------------------------------------------------------+\n| Language                                    | Maintainers                                              | Repository                                                                               |\n+=============================================+==========================================================+==========================================================================================+\n| :ref:`Eigen <eigen_interface>`              | | `Giulio Romualdi <giulio.romualdi@gmail.com>`_         | `github.com/robotology/osqp-eigen <https://github.com/robotology/osqp-eigen>`_           |\n+---------------------------------------------+----------------------------------------------------------+------------------------------------------------------------------------------------------+\n| :ref:`Rust <rust_interface>`                | | `Ed Barnard <eabarnard@gmail.com>`_                    | `github.com/oxfordcontrol/osqp.rs <https://github.com/oxfordcontrol/osqp.rs>`_           |\n+---------------------------------------------+----------------------------------------------------------+------------------------------------------------------------------------------------------+\n| :ref:`Ruby <ruby_interface>`                | | `Andrew Kane <andrew@chartkick.com>`_                  | `https://github.com/ankane/osqp <https://github.com/ankane/osqp>`_                       |\n+---------------------------------------------+----------------------------------------------------------+------------------------------------------------------------------------------------------+\n| :ref:`Fortran <fortran_interface>`          | | `Nick Gould <nick.gould@stfc.ac.uk>`_                  | `github.com/oxfordcontrol/osqp-fortran <https://github.com/oxfordcontrol/osqp-fortran>`_ |\n|                                             | | `Bartolomeo Stellato <bartolomeo.stellato@gmail.com>`_ |                                                                                          |\n|                                             | | `Paul Goulart <paul.goulart@eng.ox.ac.uk>`_            |                                                                                          |\n+---------------------------------------------+----------------------------------------------------------+------------------------------------------------------------------------------------------+\n| :ref:`Cutest <cutest_interface>`            | | `Nick Gould <nick.gould@stfc.ac.uk>`_                  | `github.com/ralna/CUTEst <https://github.com/ralna/CUTEst/tree/master/src/osqp>`_        |\n+---------------------------------------------+----------------------------------------------------------+------------------------------------------------------------------------------------------+\n\n\n\n.. toctree::\n   :maxdepth: 1\n   :glob:\n   :hidden:\n\n   eigen.rst\n   rust.rst\n   ruby.rst\n   fortran.rst\n   cutest.rst\n\n\n"
  },
  {
    "path": "osqp/docs/interfaces/julia.rst",
    "content": ".. _julia_interface:\n\nJulia\n======\n\nLoad the module\n---------------\nThe OSQP module can be load with\n\n.. code:: julia\n\n    using OSQP\n\n\n.. _julia_setup:\n\nSetup\n-----\n\nThe solver is initialized by creating an OSQP Model\n\n.. code:: julia\n\n    m = OSQP.Model()\n\nThe problem is specified in the setup phase by running\n\n.. code:: julia\n\n    OSQP.setup!(m; P=P, q=q, A=A, l=l, u=u, settings...)\n\n\nThe arguments :code:`q`, :code:`l` and :code:`u` are :code:`Vector{Float64}`. \nThe elements of :code:`l` and :code:`u` can be :math:`\\pm \\infty` ( using :code:`Inf`).\n\nThe arguments :code:`P` and :code:`A` are sparse matrices of type :code:`SparseMatrixCSC`. \nMatrix :code:`P` can be either complete or just the upper triangular\npart. OSQP will make use of only the upper triangular part.\nIf they are sparse matrices are in another format, the interface will attempt to convert them. \nThere is no need to specify all the arguments. \n\nThe argument :code:`settings` specifies the solver settings. \nSettings can also be passed as indipendent keyword arguments such as :code:`max_iter=1000`.\nThe allowed parameters are defined in :ref:`solver_settings`.\n\nSolve\n-----\n\nThe problem can be solved by\n\n.. code:: julia\n\n    results = OSQP.solve!(m)\n\n\nThe output :code:`results` contains the primal solution :code:`x`, the dual solution :code:`y`, certificate of primal infeasibility :code:`prim_inf_cert`, certificate of dual infeasibility :code:`dual_inf_cert` and the :code:`info` object containing the solver statistics defined in the following table\n\n\n+-----------------------+------------------------------------------------+\n| Member                | Description                                    |\n+=======================+================================================+\n| :code:`iter`          | Number of iterations                           |\n+-----------------------+------------------------------------------------+\n| :code:`status`        | Solver status                                  |\n+-----------------------+------------------------------------------------+\n| :code:`status_val`    | Solver status value as in :ref:`status_values` |\n+-----------------------+------------------------------------------------+\n| :code:`status_polish` | Polishing status                               |\n+-----------------------+------------------------------------------------+\n| :code:`obj_val`       | Objective value                                |\n+-----------------------+------------------------------------------------+\n| :code:`pri_res`       | Primal residual                                |\n+-----------------------+------------------------------------------------+\n| :code:`dua_res`       | Dual residual                                  |\n+-----------------------+------------------------------------------------+\n| :code:`setup_time`    | Setup time                                     |\n+-----------------------+------------------------------------------------+\n| :code:`solve_time`    | Solve time                                     |\n+-----------------------+------------------------------------------------+\n| :code:`update_time`   | Update time                                    |\n+-----------------------+------------------------------------------------+\n| :code:`polish_time`   | Polish time                                    |\n+-----------------------+------------------------------------------------+\n| :code:`run_time`      | Total run time: setup/update + solve + polish  |\n+-----------------------+------------------------------------------------+\n| :code:`rho_estimate`  | Optimal rho estimate                           |\n+-----------------------+------------------------------------------------+\n| :code:`rho_updates`   | Number of rho updates                          |\n+-----------------------+------------------------------------------------+\n\nNote that if multiple solves are executed from single setup, then after the\nfirst one :code:`run_time` includes :code:`update_time` + :code:`solve_time`\n+ :code:`polish_time`.\n\n\nUpdate\n------\nPart of problem data and settings can be updated without requiring a new problem setup.\n\nUpdate problem vectors\n^^^^^^^^^^^^^^^^^^^^^^\nVectors :code:`q`, :code:`l` and :code:`u` can be updated with new values :code:`q_new`, :code:`l_new` and :code:`u_new` by just running\n\n.. code:: julia\n\n    OSQP.update!(m; q=q_new, l=l_new, u=u_new)\n\n\nThe user does not have to specify all the keyword arguments.\n\n\nUpdate problem matrices\n^^^^^^^^^^^^^^^^^^^^^^^^\nMatrices :code:`A` and :code:`P` can be updated by changing the value of their elements but not their sparsity pattern. The interface is designed to mimic the :ref:`C/C++ counterpart <c_cpp_update_data>` with the Julia 1-based indexing. Note that the new values of :code:`P` represent only the upper triangular part while :code:`A` is always represented as a full matrix.\n\nYou can update the values of all the elements of :code:`P` by executing\n\n.. code:: julia\n\n    OSQP.update!(m, Px=Px_new)\n\n\nIf you want to update only some elements, you can pass\n\n.. code:: julia\n\n    OSQP.update!(m, Px=Px_new, Px_idx=Px_new_idx)\n\nwhere :code:`Px_new_idx` is the vector of indices of mapping the elements of :code:`Px_new` to the original vector :code:`Px` representing the data of the sparse matrix :code:`P`.\n\nMatrix :code:`A` can be changed in the same way. You can also change both matrices at the same time by running, for example\n\n\n.. code:: julia\n\n    OSQP.update!(m, Px=Px_new, Px_idx=Px_new_idx, Ax=Ax_new, Ax=Ax_new_idx)\n\n\n\n\n\n.. _julia_update_settings:\n\nUpdate settings\n^^^^^^^^^^^^^^^\n\nSettings can be updated by running\n\n.. code:: julia\n\n    OSQP.update_settings!(m; new_settings)\n\n\nwhere :code:`new_settings` are the new settings specified as keyword arguments that can be updated which are marked with an * in :ref:`solver_settings`.\n\n\nWarm start\n----------\n\nOSQP automatically warm starts primal and dual variables from the previous QP solution. If you would like to warm start their values manually, you can use\n\n.. code:: julia\n\n    OSQP.warm_start!(m; x=x0, y=y0)\n\n\nwhere :code:`x0` and :code:`y0` are the new primal and dual variables. \n"
  },
  {
    "path": "osqp/docs/interfaces/linear_systems_solvers.rst",
    "content": ".. _linear_system_solvers_setting :\n\nLinear Systems Solvers\n-----------------------\nThe settings parameter :code:`linsys_solver` defines the solver for the linear system.\nIn C/C++ it corresponds to an integer :code:`c_int` (see :ref:`c_cpp_data_types`) and in the other high level languages to a string.\n\n\n+-----------------+-------------------+--------------------------------+---------------+\n| Solver          | String option     | C/C++ Constant                 | Integer value |\n+=================+===================+================================+===============+\n| QDLDL           | \"qdldl\"           | :code:`QDLDL_SOLVER`           | :code:`0`     |\n+-----------------+-------------------+--------------------------------+---------------+\n| MKL Pardiso     | \"mkl pardiso\"     | :code:`MKL_PARDISO_SOLVER`     | :code:`1`     |\n+-----------------+-------------------+--------------------------------+---------------+\n\n\n\nTo add new linear system solvers see :ref:`interfacing_new_linear_system_solvers`.\n\n\n\n"
  },
  {
    "path": "osqp/docs/interfaces/matlab.rst",
    "content": ".. _matlab_interface:\n\nMatlab\n======\n\n.. _matlab_setup:\n\nSetup\n-----\nThe solver is initialized by creating an OSQP object\n\n.. code:: matlab\n\n    m = osqp;\n\nThe problem is specified in the setup phase by running\n\n.. code:: matlab\n\n    m.setup(P, q, A, l, u, varargin)\n\n\nThe arguments :code:`q`, :code:`l` and :code:`u` are arrays. The elements of :code:`l` and :code:`u` can be :math:`\\pm \\infty` ( using :code:`Inf`). The arguments :code:`P` and :code:`A` are sparse matrices.\nMatrix :code:`P` can be either complete or just the upper triangular\npart. OSQP will make use of only the upper triangular part.\n\nThere is no need to specify all the problem data. They can be omitted by writing :code:`[]`.\n\nThe last argument :code:`varargin` specifies the solver options. You can pass the options in two ways. You can either set the individual parameters as field-value pairs, e.g.,\n\n.. code:: matlab\n\n    m.setup(P, q, A, l, u, 'eps_abs', 1e-04, 'eps_rel', 1e-04);\n\n\nAlternatively, you can create a structure containing all the settings, change some of the fields and then pass it as the last argument\n\n.. code:: matlab\n\n    settings = m.default_settings();\n    settings.eps_abs = 1e-04;\n    settings.eps_rel = 1e-04;\n    m.setup(P, q, A, l, u, settings);\n\nThe allowed settings are defined in :ref:`solver_settings`.\n\n\nSolve\n-----\n\nThe problem can be solved by\n\n.. code:: matlab\n\n   results = m.solve();\n\nThe :code:`results` structure contains the primal solution :code:`x`, the dual solution :code:`y`, certificate of primal infeasibility :code:`prim_inf_cert`, certificate of dual infeasibility :code:`dual_inf_cert` and the :code:`info` structure containing the solver statistics defined in the following table\n\n\n+-----------------------+------------------------------------------------+\n| Member                | Description                                    |\n+=======================+================================================+\n| :code:`iter`          | Number of iterations                           |\n+-----------------------+------------------------------------------------+\n| :code:`status`        | Solver status                                  |\n+-----------------------+------------------------------------------------+\n| :code:`status_val`    | Solver status value as in :ref:`status_values` |\n+-----------------------+------------------------------------------------+\n| :code:`status_polish` | Polishing status                               |\n+-----------------------+------------------------------------------------+\n| :code:`obj_val`       | Objective value                                |\n+-----------------------+------------------------------------------------+\n| :code:`pri_res`       | Primal residual                                |\n+-----------------------+------------------------------------------------+\n| :code:`dua_res`       | Dual residual                                  |\n+-----------------------+------------------------------------------------+\n| :code:`setup_time`    | Setup time                                     |\n+-----------------------+------------------------------------------------+\n| :code:`solve_time`    | Solve time                                     |\n+-----------------------+------------------------------------------------+\n| :code:`update_time`   | Update time                                    |\n+-----------------------+------------------------------------------------+\n| :code:`polish_time`   | Polish time                                    |\n+-----------------------+------------------------------------------------+\n| :code:`run_time`      | Total run time: setup/update + solve + polish  |\n+-----------------------+------------------------------------------------+\n| :code:`rho_estimate`  | Optimal rho estimate                           |\n+-----------------------+------------------------------------------------+\n| :code:`rho_updates`   | Number of rho updates                          |\n+-----------------------+------------------------------------------------+\n\nNote that if multiple solves are executed from single setup, then after the\nfirst one :code:`run_time` includes :code:`update_time` + :code:`solve_time`\n+ :code:`polish_time`.\n\n\nUpdate\n------\nPart of problem data and settings can be updated without requiring a new problem setup.\n\n\n\nUpdate problem vectors\n^^^^^^^^^^^^^^^^^^^^^^^^\n\nVectors :code:`q`, :code:`l` and :code:`u` can be updated with new values :code:`q_new`, :code:`l_new` and :code:`u_new` by just running\n\n.. code:: python\n\n    m.update('q', q_new, 'l', l_new, 'u', u_new);\n\n\nThe user does not have to specify all the arguments.\n\n\nUpdate problem matrices\n^^^^^^^^^^^^^^^^^^^^^^^^\nMatrices :code:`A` and :code:`P` can be updated by changing the value of their elements but not their sparsity pattern. \nThe interface is designed to mimic the :ref:`C/C++ counterpart <c_cpp_update_data>` with the Matlab 1-based indexing. \nNote that the new values of :code:`P` represent only the upper triangular part while :code:`A` is always represented as a full matrix.\n\nYou can update the values of all the elements of :code:`P` by executing\n\n.. code:: matlab\n\n    m.update('Px', Px_new)\n\n\nIf you want to update only some elements, you can pass\n\n.. code:: matlab\n\n    m.update('Px', Px_new, 'Px_idx', Px_new_idx)\n\nwhere :code:`Px_new_idx` is the vector of indices of mapping the elements of :code:`Px_new` to the original vector :code:`Px` representing the data of the sparse matrix :code:`P`.\n\nMatrix :code:`A` can be changed in the same way. You can also change both matrices at the same time by running, for example\n\n\n.. code:: matlab\n\n    m.update('Px', Px_new, 'Px_idx', Px_new_idx, 'Ax' Ax_new, 'Ax', Ax_new_idx)\n\n\nUpdate settings\n^^^^^^^^^^^^^^^\n\nSettings can be updated by running\n\n.. code:: python\n\n    m.update_settings(varargin);\n\n\nwhere :code:`varargin` argument is described in :ref:`matlab_setup`. The allowed settings that can be updated are marked with an * in :ref:`solver_settings`.\n\n\n\n\nWarm start\n----------\nOSQP automatically warm starts primal and dual variables from the previous QP solution. If you would like to warm start their values manually, you can use\n\n.. code:: matlab\n\n    m.warm_start('x', x0, 'y', y0)\n\nwhere :code:`x0` and :code:`y0` are the new primal and dual variables. \n"
  },
  {
    "path": "osqp/docs/interfaces/python.rst",
    "content": ".. _python_interface:\n\nPython\n======\n\nImport\n------\nThe OSQP module can be imported with\n\n.. code:: python\n\n    import osqp\n\n\n.. _python_setup:\n\nSetup\n-----\n\nThe solver is initialized by creating an OSQP object\n\n.. code:: python\n\n    m = osqp.OSQP()\n\nThe problem is specified in the setup phase by running\n\n.. code:: python\n\n    m.setup(P=P, q=q, A=A, l=l, u=u, **settings)\n\n\nThe arguments :code:`q`, :code:`l` and :code:`u` are numpy arrays. The elements of :code:`l` and :code:`u` can be :math:`\\pm \\infty` ( using :code:`numpy.inf`).\n\nThe arguments :code:`P` and :code:`A` are scipy sparse matrices in CSC format. \nMatrix :code:`P` can be either complete or just the upper triangular\npart. OSQP will make use of only the upper triangular part.\nIf they are sparse matrices are in another format, the interface will attempt to convert them. There is no need to specify all the arguments.\n\n\nThe keyword arguments :code:`**settings` specify the solver settings. The allowed parameters are defined in :ref:`solver_settings`.\n\nSolve\n-----\n\nThe problem can be solved by\n\n.. code:: python\n\n   results = m.solve()\n\n\nThe :code:`results` object contains the primal solution :code:`x`, the dual solution :code:`y`, certificate of primal infeasibility :code:`prim_inf_cert`, certificate of dual infeasibility :code:`dual_inf_cert` and the :code:`info` object containing the solver statistics defined in the following table\n\n\n+-----------------------+------------------------------------------------+\n| Member                | Description                                    |\n+=======================+================================================+\n| :code:`iter`          | Number of iterations                           |\n+-----------------------+------------------------------------------------+\n| :code:`status`        | Solver status                                  |\n+-----------------------+------------------------------------------------+\n| :code:`status_val`    | Solver status value as in :ref:`status_values` |\n+-----------------------+------------------------------------------------+\n| :code:`status_polish` | Polishing status                               |\n+-----------------------+------------------------------------------------+\n| :code:`obj_val`       | Objective value                                |\n+-----------------------+------------------------------------------------+\n| :code:`pri_res`       | Primal residual                                |\n+-----------------------+------------------------------------------------+\n| :code:`dua_res`       | Dual residual                                  |\n+-----------------------+------------------------------------------------+\n| :code:`setup_time`    | Setup time                                     |\n+-----------------------+------------------------------------------------+\n| :code:`solve_time`    | Solve time                                     |\n+-----------------------+------------------------------------------------+\n| :code:`update_time`   | Update time                                    |\n+-----------------------+------------------------------------------------+\n| :code:`polish_time`   | Polish time                                    |\n+-----------------------+------------------------------------------------+\n| :code:`run_time`      | Total run time: setup/update + solve + polish  |\n+-----------------------+------------------------------------------------+\n| :code:`rho_estimate`  | Optimal rho estimate                           |\n+-----------------------+------------------------------------------------+\n| :code:`rho_updates`   | Number of rho updates                          |\n+-----------------------+------------------------------------------------+\n\nNote that if multiple solves are executed from single setup, then after the\nfirst one :code:`run_time` includes :code:`update_time` + :code:`solve_time`\n+ :code:`polish_time`.\n\n  \nSolve in just one function (with GIL disabled)\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nWe have a dedicated solve function that performs :code:`setup` and :code:`solve` operations for you. It also disables the GIL in case you\nneed it. Just run it from the main module without creating the object as follows\n\n\n.. code:: python\n\n    results = osqp.solve(P=P, q=q, A=A, l=l, u=u, **settings)\n\n\nUpdate\n------\nPart of problem data and settings can be updated without requiring a new problem setup.\n\nUpdate problem vectors\n^^^^^^^^^^^^^^^^^^^^^^\nVectors :code:`q`, :code:`l` and :code:`u` can be updated with new values :code:`q_new`, :code:`l_new` and :code:`u_new` by just running\n\n.. code:: python\n\n    m.update(q=q_new, l=l_new, u=u_new)\n\n\nThe user does not have to specify all the keyword arguments.\n\n\n.. _python_update_settings:\n\nUpdate problem matrices\n^^^^^^^^^^^^^^^^^^^^^^^^\nMatrices :code:`A` and :code:`P` can be updated by changing the value of their elements but not their sparsity pattern. \nThe interface is designed to mimic the :ref:`C/C++ counterpart <c_cpp_update_data>`. \nNote that the new values of :code:`P` represent only the upper triangular part while :code:`A` is always represented as a full matrix.\n\nYou can update the values of all the elements of :code:`P` by executing\n\n.. code:: python\n\n    m.update(Px=Px_new)\n\n\nIf you want to update only some elements, you can pass\n\n.. code:: python\n\n    m.update(Px=Px_new, Px_idx=Px_new_idx)\n\nwhere :code:`Px_new_idx` is the vector of indices of mapping the elements of :code:`Px_new` to the original vector :code:`Px` representing the data of the sparse matrix :code:`P`.\n\nMatrix :code:`A` can be changed in the same way. You can also change both matrices at the same time by running, for example\n\n\n.. code:: python\n\n    m.update(Px=Px_new, Px_idx=Px_new_idx, Ax=Ax_new, Ax=Ax_new_idx)\n\n\nUpdate settings\n^^^^^^^^^^^^^^^\n\nSettings can be updated by running\n\n.. code:: python\n\n    m.update_settings(**kwargs)\n\n\nwhere :code:`kwargs` are the settings that can be updated which are marked with an * in :ref:`solver_settings`.\n\n\nWarm start\n----------\n\nOSQP automatically warm starts primal and dual variables from the previous QP solution. If you would like to warm start their values manually, you can use\n\n.. code:: python\n\n    m.warm_start(x=x0, y=y0)\n\n\nwhere :code:`x0` and :code:`y0` are the new primal and dual variables. \n"
  },
  {
    "path": "osqp/docs/interfaces/rlang.rst",
    "content": ".. _rlang_interface:\n\n\nR\n==\n\nThe R interface is officially on CRAN and documented `here <https://cran.r-project.org/web/packages/osqp/>`_.\n"
  },
  {
    "path": "osqp/docs/interfaces/ruby.rst",
    "content": ".. _ruby_interface:\n\n\nRuby\n========\n\nThe ruby interface can be installed as a Ruby gem and is documented `here <https://www.rubydoc.info/gems/osqp/>`_.\n"
  },
  {
    "path": "osqp/docs/interfaces/rust.rst",
    "content": ".. _rust_interface:\n\nRust\n======\n\nThe Rust interface is documented `here <https://docs.rs/osqp/>`_.\n"
  },
  {
    "path": "osqp/docs/interfaces/solver_settings.rst",
    "content": ".. _solver_settings :\n\nSolver settings\n---------------\n\nThe solver settings are displayed in the following table. The settings marked with * can be changed without running the setup method again.\n\n.. tabularcolumns:: |p{4.5cm}|p{3.5cm}|p{6.5cm}|L|\n\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| Argument                       | Description                                                 | Allowed values                                               | Default value   |\n+================================+=============================================================+==============================================================+=================+\n| :code:`rho` *                  | ADMM rho step                                               | 0 < :code:`rho`                                              | 0.1             |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`sigma`                  | ADMM sigma step                                             | 0 < :code:`sigma`                                            | 1e-06           |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`max_iter` *             | Maximum number of iterations                                | 0 < :code:`max_iter` (integer)                               | 4000            |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`eps_abs` *              | Absolute tolerance                                          | 0 <= :code:`eps_abs`                                         | 1e-03           |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`eps_rel` *              | Relative tolerance                                          | 0 <= :code:`eps_rel`                                         | 1e-03           |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`eps_prim_inf` *         | Primal infeasibility tolerance                              | 0 <= :code:`eps_prim_inf`                                    | 1e-04           |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`eps_dual_inf` *         | Dual infeasibility tolerance                                | 0 <= :code:`eps_dual_inf`                                    | 1e-04           |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`alpha` *                | ADMM overrelaxation parameter                               | 0 < :code:`alpha` < 2                                        | 1.6             |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`linsys_solver`          | Linear systems solver type                                  | See :ref:`linear_system_solvers_setting`                     | qdldl           |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`delta` *                | Polishing regularization parameter                          | 0 < :code:`delta`                                            | 1e-06           |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`polish` *               | Perform polishing                                           | True/False                                                   | False           |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`polish_refine_iter` *   | Refinement iterations in polish                             | 0 < :code:`polish_refine_iter` (integer)                     | 3               |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`verbose` *              | Print output                                                | True/False                                                   | True            |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`scaled_termination` *   | Scaled termination conditions                               | True/False                                                   | False           |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`check_termination` *    | Check termination interval                                  | 0 (disabled) or 0 < :code:`check_termination` (integer)      | 25              |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`warm_start` *           | Perform warm starting                                       | True/False                                                   | True            |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`scaling`                | Number of scaling iterations                                | 0 (disabled) or 0 < :code:`scaling` (integer)                | 10              |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`adaptive_rho`           | Adaptive rho                                                | True/False                                                   | True            |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`adaptive_rho_interval`  | Adaptive rho interval                                       | 0 (automatic) or 0 < :code:`adaptive_rho_interval` (integer) | 0               |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`adaptive_rho_tolerance` | Tolerance for adapting rho                                  | 1 <= :code:`adaptive_rho_tolerance`                          | 5               |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`adaptive_rho_fraction`  | Adaptive rho interval as fraction of setup time (auto mode) | 0 < :code:`adaptive_rho_fraction`                            | 0.4             |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n| :code:`time_limit` *           | Run time limit in seconds                                   | 0 (disabled) or 0 <= :code:`time_limit`                      | 0               |\n+--------------------------------+-------------------------------------------------------------+--------------------------------------------------------------+-----------------+\n\nThe boolean values :code:`True/False` are defined as :code:`1/0` in the C/C++ interfaces.\n\n\n.. The infinity values correspond to:\n..\n.. +----------+--------------------+\n.. | Language | Value              |\n.. +==========+====================+\n.. | C        | :code:`OSQP_INFTY` |\n.. +----------+--------------------+\n.. | Python   | :code:`numpy.inf`  |\n.. +----------+--------------------+\n.. | Matlab   | :code:`Inf`        |\n.. +----------+--------------------+\n.. | Julia    | :code:`Inf`        |\n.. +----------+--------------------+\n"
  },
  {
    "path": "osqp/docs/interfaces/status_values.rst",
    "content": ".. _status_values :\n\n\n\nStatus values and errors\n========================\n\nStatus values\n-------------\n\nThese are the exit statuses, their respective constants and values returned by the solver as defined in `constants.h <https://github.com/oxfordcontrol/osqp/blob/master/include/constants.h>`_.\nThe *inaccurate* statuses define when the optimality, primal infeasibility or dual infeasibility conditions are satisfied with tolerances 10 times larger than the ones set.\n\n+------------------------------+-----------------------------------+-------+\n| Status                       | Constant                          | Value |\n+==============================+===================================+=======+\n| solved                       | OSQP_SOLVED                       | 1     |\n+------------------------------+-----------------------------------+-------+\n| solved inaccurate            | OSQP_SOLVED_INACCURATE            | 2     |\n+------------------------------+-----------------------------------+-------+\n| maximum iterations reached   | OSQP_MAX_ITER_REACHED             | -2    |\n+------------------------------+-----------------------------------+-------+\n| primal infeasible            | OSQP_PRIMAL_INFEASIBLE            | -3    |\n+------------------------------+-----------------------------------+-------+\n| primal infeasible inaccurate | OSQP_PRIMAL_INFEASIBLE_INACCURATE | 3     |\n+------------------------------+-----------------------------------+-------+\n| dual infeasible              | OSQP_DUAL_INFEASIBLE              | -4    |\n+------------------------------+-----------------------------------+-------+\n| dual infeasible inaccurate   | OSQP_DUAL_INFEASIBLE_INACCURATE   | 4     |\n+------------------------------+-----------------------------------+-------+\n| interrupted by user          | OSQP_SIGINT                       | -5    |\n+------------------------------+-----------------------------------+-------+\n| run time limit reached       | OSQP_TIME_LIMIT_REACHED           | -6    |\n+------------------------------+-----------------------------------+-------+\n| unsolved                     | OSQP_UNSOLVED                     | -10   |\n+------------------------------+-----------------------------------+-------+\n| problem non convex           | OSQP_NON_CVX                      | -7    |\n+------------------------------+-----------------------------------+-------+\n\n.. note::\n\n   We recommend the user to **check the convexity of their problem before\n   passing it to OSQP**! If the user passes a non-convex problem we do not\n   assure the solver will be able to detect it.\n\n   OSQP will try to detect **non-convex** problems by checking if the residuals\n   diverge or if there are any issues in the initial factorization (if a direct\n   method is used). It will detect non-convex problems when one or more of the\n   eigenvalues of :code:`P` are \"clearly\" negative, i.e., when :code:`P + sigma\n   * I` is not positive semidefinite. However, it might fail to detect\n   non-convexity when :code:`P` has slightly negative eigenvalues, i.e., when\n   :code:`P + sigma * I` is positive semidefinite and :code:`P` is not.\n\n\n\n\t \nSolver Errors\n-------------\n\nOSQP can return errors during the setup and solve steps. Here is a table of the meaning\nand their constant values.\n\n\n+------------------------------------------------+-----------------------------------+-------+\n| Errors                                         | Constant                          | Value |\n+================================================+===================================+=======+\n| Data validation                                | OSQP_DATA_VALIDATION_ERROR        | 1     |\n+------------------------------------------------+-----------------------------------+-------+\n| Settings validation                            | OSQP_SETTINGS_VALIDATION_ERROR    | 2     |\n+------------------------------------------------+-----------------------------------+-------+\n| Linear system solver loading                   | OSQP_LINSYS_SOLVER_LOAD_ERROR     | 3     |\n+------------------------------------------------+-----------------------------------+-------+\n| Linear system solver initialization            | OSQP_LINSYS_SOLVER_INIT_ERROR     | 4     |\n+------------------------------------------------+-----------------------------------+-------+\n| Non convex problem                             | OSQP_NONCVX_ERROR                 | 5     |\n+------------------------------------------------+-----------------------------------+-------+\n| Memory allocation                              | OSQP_MEM_ALLOC_ERROR              | 6     |\n+------------------------------------------------+-----------------------------------+-------+\n| Workspace not initialized                      | OSQP_WORKSPACE_NOT_INIT           | 7     |\n+------------------------------------------------+-----------------------------------+-------+\n\n\n\n\n\n"
  },
  {
    "path": "osqp/docs/make.bat",
    "content": "@ECHO OFF\n\npushd %~dp0\n\nREM Command file for Sphinx documentation\n\nif \"%SPHINXBUILD%\" == \"\" (\n\tset SPHINXBUILD=sphinx-build\n)\nset SOURCEDIR=.\nset BUILDDIR=_build\nset SPHINXPROJ=osqp\n\nif \"%1\" == \"\" goto help\n\n%SPHINXBUILD% >NUL 2>NUL\nif errorlevel 9009 (\n\techo.\n\techo.The 'sphinx-build' command was not found. Make sure you have Sphinx\n\techo.installed, then set the SPHINXBUILD environment variable to point\n\techo.to the full path of the 'sphinx-build' executable. Alternatively you\n\techo.may add the Sphinx directory to PATH.\n\techo.\n\techo.If you don't have Sphinx installed, grab it from\n\techo.http://sphinx-doc.org/\n\texit /b 1\n)\n\n%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%\ngoto end\n\n:help\n%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%\n\n:end\npopd\n"
  },
  {
    "path": "osqp/docs/parsers/cvxpy.rst",
    "content": "CVXPY\n=====\n\n`CVXPY <http://www.cvxpy.org/>`_ 1.0 supports the OSQP solver by default. After defining your problem, you can solve it with OSQP by just calling\n\n.. code:: python\n\n   problem.solve()\n\n\nOSQP is the default QP solver used. To specify it explicitly together with some options you can execute\n\n.. code:: python\n\n   problem.solve(solver=OSQP, max_iter=2000)\n\nwhere we set the :code:`max_iter` option to :code:`2000`.\n"
  },
  {
    "path": "osqp/docs/parsers/index.rst",
    "content": "Parsers\n=======\n\n.. toctree::\n   :maxdepth: 1\n   :glob:\n\n   yalmip.rst\n   cvxpy.rst\n   jump.rst\n"
  },
  {
    "path": "osqp/docs/parsers/jump.rst",
    "content": "JuMP\n=====\n\n`JuMP <https://github.com/JuliaOpt/JuMP.jl>`_ supports the OSQP solver using the `MathOptInterface interface <https://github.com/JuliaOpt/MathOptInterface.jl>`_. \nYou can define a JuMP model to be solved via OSQP as follows\n\n\n.. code:: julia\n\n   # Load JuMP and OSQP\n   using JuMP, OSQP\n\n   # Create OSQP Solver instance\n   s = OSQPMathProgBaseInterface.OSQPSolver(verbose=false)\n\n   # Create JuMP model\n   model = Model(solver=s)\n\n   ...\n\nNote that here we set the verbosity to :code:`false`.\nAfter defining your model, you can solve it by just calling\n\n.. code:: julia\n\n   solve(model)\n\n\nFor more details on how to create and modify the models, see the `JuMP Documentation <https://www.juliaopt.org/JuMP.jl/stable/>`_."
  },
  {
    "path": "osqp/docs/parsers/yalmip.rst",
    "content": "YALMIP\n======\n\n`YALMIP <https://yalmip.github.io/>`_ support the OSQP solver. You can easily define problems in high-level format and then specify OSQP by simply setting\n\n.. code:: matlab\n\n   options = sdpsettings('solver','osqp', 'max_iter', 2000);\n\n\nwhere we set the :code:`max_iter` option to :code:`2000`.\n"
  },
  {
    "path": "osqp/docs/requirements.txt",
    "content": "breathe\n"
  },
  {
    "path": "osqp/docs/solver/index.rst",
    "content": "The solver\n==========\n\nProblem statement\n-----------------\n\nOSQP solves convex quadratic programs (QPs) of the form\n\n.. math::\n  \\begin{array}{ll}\n    \\mbox{minimize} & \\frac{1}{2} x^T P x + q^T x \\\\\n    \\mbox{subject to} & l \\leq A x \\leq u\n  \\end{array}\n\nwhere :math:`x\\in\\mathbf{R}^{n}` is the optimization variable.\nThe objective function is defined by a positive semidefinite matrix\n:math:`P \\in \\mathbf{S}^{n}_{+}` and vector :math:`q\\in \\mathbf{R}^{n}`.\nThe linear constraints are defined by matrix :math:`A\\in\\mathbf{R}^{m \\times n}`\nand vectors :math:`l` and :math:`u` so that :math:`l_i \\in \\mathbf{R} \\cup \\{-\\infty\\}` and :math:`u_i \\in \\mathbf{R} \\cup \\{+\\infty\\}` for all :math:`i \\in \\{1,\\ldots,m\\}`.\n\n\nAlgorithm\n---------\n\nThe solver runs the following `ADMM algorithm <http://web.stanford.edu/~boyd/papers/admm_distr_stats.html>`_ (for more details see the related papers at the :ref:`citing` section):\n\n\n.. math::\n\n   (x^{k+1}, \\nu^{k+1}) & \\gets \\text{solve linear system}\\\\\n   \\tilde{z}^{k+1} & \\gets z^{k} + \\rho^{-1}(\\nu^{k+1} - y^{k})\\\\\n   z^{k+1} &\\gets \\Pi(\\tilde{z}^{k} + \\rho^{-1}y^{k})\\\\\n   y^{k+1} &\\gets y^{k} + \\rho (\\tilde{z}^{k+1} - z^{k+1})\n\nwhere :math:`\\Pi` is the projection onto the hyperbox :math:`[l,u]`. \n:math:`\\rho` is the ADMM step-size. \n\n\nLinear system solution\n^^^^^^^^^^^^^^^^^^^^^^\nThe linear system solution is the core part of the algorithm.\nIt can be done using a **direct** or **indirect** method.\n\nWith a **direct linear system solver** we solve the following linear system with a quasi-definite matrix \n\n.. math::\n\n   \\begin{bmatrix} P + \\sigma I & A^T \\\\ A & -\\rho^{-1}I \\end{bmatrix} \\begin{bmatrix} x^{k+1} \\\\ \\nu^{k+1} \\end{bmatrix}= \\begin{bmatrix}\\sigma x^{k} - q \\\\ z^{k} - \\rho^{-1} y^k \\end{bmatrix}.\n\nWith an **indirect linear system solver** we solve the following linear system with a positive definite matrix \n\n\n.. math::\n\n\t\\left(P + \\sigma I + \\rho A^T A \\right)x^{k+1} = \\sigma x^{k} - q + A^T (\\rho z^{k} - y^{k}).\n\n\nOSQP core is designed to support different linear system solvers.\nFor their installation see :ref:`this section <linear_system_solvers_installation>`.\nTo specify your favorite linear system solver see :ref:`this section <linear_system_solvers_setting>`.\n\nConvergence\n^^^^^^^^^^^\nAt each iteration :math:`k` OSQP produces a tuple :math:`(x^{k}, z^{k}, y^{k})` with :math:`x^{k} \\in \\mathbf{R}^{n}` and :math:`z^{k}, y^{k} \\in \\mathbf{R}^{m}`.\n\nThe primal and and dual residuals associated to :math:`(x^{k}, z^{k}, y^{k})` are\n\n.. math::\n\n   \\begin{aligned}\n   r_{\\rm prim}^{k} &= Ax^{k} - z^{k}\\\\\n   r_{\\rm dual}^{k} &= Px^{k} + q + A^{T} y^{k}.\n   \\end{aligned}\n\nComplementary slackness is satisfied by construction at machine precision. If the problem is feasible, the residuals converge to zero as :math:`k\\to\\infty`. The algorithm stops when the norms of :math:`r_{\\rm prim}^{k}` and :math:`r_{\\rm dual}^{k}` are within the specified tolerance levels :math:`\\epsilon_{\\rm prim}>0` and :math:`\\epsilon_{\\rm dual}>0` \n\n.. math::\n\n    \\| r_{\\rm prim}^{k} \\|_{\\infty} \\le \n   \\epsilon_{\\rm prim},\n    \\quad\n    \\| r_{\\rm dual}^{k} \\|_{\\infty} \\le \n   \\epsilon_{\\rm dual}.\n\nWe set the tolerance levels as\n    \n.. math:: \n\n    \\epsilon_{\\rm prim} &= \\epsilon_{\\rm abs} + \\epsilon_{\\rm rel} \\max\\lbrace \\|Ax^{k}\\|_{\\infty}, \\| z^{k} \\|_{\\infty} \\rbrace \\\\\n    \\epsilon_{\\rm dual} &= \\epsilon_{\\rm abs} + \\epsilon_{\\rm rel} \\max\\lbrace \\| P x^{k} \\|_{\\infty}, \\| A^T y^{k} \\|_{\\infty}, \\| q \\|_{\\infty} \\rbrace.\n\n\n.. _rho_step_size :\n\n:math:`\\rho` step-size \n^^^^^^^^^^^^^^^^^^^^^^\nTo ensure quick convergence of the algorithm we adapt :math:`\\rho` by balancing the residuals. \nIn default mode, the inteval (*i.e.*, number of iterations) at which we update :math:`\\rho` is defined by a time measurement.\nWhen the iterations time becomes greater than a certain fraction of the setup time, *i.e.* :code:`adaptive_rho_fraction`, we set the current number of iterations as the interval to update :math:`\\rho`.\nThe update happens as follows \n\n.. math::\n\n    \\rho^{k+1} \\gets \\rho^{k} \\sqrt{\\frac{\\|r_{\\rm prim}\\|_{\\infty}}{\\|r_{\\rm dual}\\|_{\\infty}}}.\n\n\nNote that :math:`\\rho` is updated only if it is sufficiently different than the current one.\nIn particular if it is :code:`adaptive_rho_tolerance` times larger or smaller than the current one.\n\n\n\nInfeasible problems\n-------------------------------\n\nOSQP is able to detect if the problem is primal or dual infeasible.\n\n\nPrimal infeasibility\n^^^^^^^^^^^^^^^^^^^^\n\nWhen the problem is primal infeasible, the algorithm generates a certificate of infeasibility, *i.e.*, a vector :math:`v\\in\\mathbf{R}^{m}` such that\n\n.. math::\n\n   A^T v = 0, \\quad u^T v_{+} + l^T v_{-} < 0,\n\nwhere :math:`v_+=\\max(v,0)` and :math:`v_-=\\min(v,0)`.\n\nThe primal infeasibility check is then\n\n.. math::\n\n\t\\left\\|A^T v \\right\\|_{\\infty} \\le \\epsilon_{\\rm prim\\_inf}, \\quad u^T (v)_{+} + l^T (v)_{-} \\le - \\epsilon_{\\rm prim\\_inf}.\n\n\n\nDual infeasibility\n^^^^^^^^^^^^^^^^^^^^\n\nWhen the problem is dual infeasible, OSQP generates a vector :math:`s\\in\\mathbf{R}^{n}` being a certificate of dual infeasibility, *i.e.*,\n\n.. math::\n\n   P s = 0, \\quad q^T s < 0, \\quad (As)_i = \\begin{cases} 0 & l_i \\in \\mathbf{R}, u_i\\in\\mathbf{R} \\\\ \\ge 0 & l_i\\in\\mathbf{R}, u_i=+\\infty \\\\ \\le 0 & u_i\\in\\mathbf{R}, l_i=-\\infty. \\end{cases}\n\n\nThe dual infeasibility check is then\n\n.. math::\n\n        \\| P s \\|_{\\infty} \\le \\epsilon_{\\rm dual\\_inf} , \\quad\n        q^T s \\le -\\epsilon_{\\rm dual\\_inf}, \\\\\n        (A s)_i \\begin{cases} \\in \\left[-\\epsilon_{\\rm dual\\_inf}, \\epsilon_{\\rm dual\\_inf}\\right] & u_i, l_i \\in \\mathbf{R}\\\\\n        \\ge \\epsilon_{\\rm dual\\_inf} &u_i = +\\infty\\\\\n        \\le  -\\epsilon_{\\rm dual\\_inf} &l_i = -\\infty.\\end{cases}\n\n\n\n\nPolishing\n^^^^^^^^^^^\n\nPolishing is an additional algorithm step where OSQP tries to compute a high-accuracy solution. \nWe can enable it by turning the setting :code:`polish` to 1.\n\nPolishing works by guessing the active constraints at the optimum and solving an additional linear system.\nIf the guess is correct, OSQP returns a high accuracy solution.\nOtherwise OSQP returns the ADMM solution.\nThe status of the polishing phase appears in the information :code:`status_polish`.\n\nNote that polishing requires the solution of an additional linear system and thereby, an additional factorization if the linear system solver is direct.\nHowever, the linear system is usually much smaller than the one solved during the ADMM iterations.\n\nThe chances to have a successful polishing increase if the tolerances :code:`eps_abs` and :code:`eps_rel` are small. \nHowever, low tolerances might require a very large number of iterations.\n\n\n"
  },
  {
    "path": "osqp/examples/osqp_demo.c",
    "content": "#include \"osqp.h\"\n\n\nint main(int argc, char **argv) {\n  // Load problem data\n  c_float P_x[3] = { 4.0, 1.0, 2.0, };\n  c_int   P_nnz  = 3;\n  c_int   P_i[3] = { 0, 0, 1, };\n  c_int   P_p[3] = { 0, 1, 3, };\n  c_float q[2]   = { 1.0, 1.0, };\n  c_float A_x[4] = { 1.0, 1.0, 1.0, 1.0, };\n  c_int   A_nnz  = 4;\n  c_int   A_i[4] = { 0, 1, 0, 2, };\n  c_int   A_p[3] = { 0, 2, 4, };\n  c_float l[3]   = { 1.0, 0.0, 0.0, };\n  c_float u[3]   = { 1.0, 0.7, 0.7, };\n  c_int n = 2;\n  c_int m = 3;\n\n  // Exitflag\n  c_int exitflag = 0;\n\n  // Workspace structures\n  OSQPWorkspace *work;\n  OSQPSettings  *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n  OSQPData      *data     = (OSQPData *)c_malloc(sizeof(OSQPData));\n\n  // Populate data\n  if (data) {\n    data->n = n;\n    data->m = m;\n    data->P = csc_matrix(data->n, data->n, P_nnz, P_x, P_i, P_p);\n    data->q = q;\n    data->A = csc_matrix(data->m, data->n, A_nnz, A_x, A_i, A_p);\n    data->l = l;\n    data->u = u;\n  }\n\n  // Define solver settings as default\n  if (settings) osqp_set_default_settings(settings);\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, data, settings);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Clean workspace\n  osqp_cleanup(work);\n  if (data) {\n    if (data->A) c_free(data->A);\n    if (data->P) c_free(data->P);\n    c_free(data);\n  }\n  if (settings)  c_free(settings);\n\n  return exitflag;\n}\n"
  },
  {
    "path": "osqp/include/.gitignore",
    "content": "# Ignore Cmake-generated osqp_configure.h file\nosqp_configure.h\n"
  },
  {
    "path": "osqp/include/CMakeLists.txt",
    "content": "# Add the OSQP headers\nset(\n    osqp_headers\n    \"${CMAKE_CURRENT_SOURCE_DIR}/auxil.h\"\n    \"${CMAKE_CURRENT_SOURCE_DIR}/constants.h\"\n    \"${CMAKE_CURRENT_SOURCE_DIR}/error.h\"\n    \"${CMAKE_CURRENT_SOURCE_DIR}/glob_opts.h\"\n    \"${CMAKE_CURRENT_SOURCE_DIR}/lin_alg.h\"\n    \"${CMAKE_CURRENT_SOURCE_DIR}/osqp.h\"\n    \"${CMAKE_CURRENT_SOURCE_DIR}/osqp_configure.h\"\n    \"${CMAKE_CURRENT_SOURCE_DIR}/proj.h\"\n    \"${CMAKE_CURRENT_SOURCE_DIR}/scaling.h\"\n    \"${CMAKE_CURRENT_SOURCE_DIR}/types.h\"\n    \"${CMAKE_CURRENT_SOURCE_DIR}/util.h\"\n)\n\n# Add the KKT update only in normal mode and matrix-updating embedded mode (not mode 1)\nif (NOT (EMBEDDED EQUAL 1))\n    list(\n      APPEND\n      osqp_src\n      \"${CMAKE_CURRENT_SOURCE_DIR}/kkt.h\"\n    )\nendif()\n\n# Add more files that should only be in non-embedded code\nif (NOT DEFINED EMBEDDED)\n    list(\n      APPEND\n      osqp_headers\n      \"${CMAKE_CURRENT_SOURCE_DIR}/cs.h\"\n      \"${CMAKE_CURRENT_SOURCE_DIR}/polish.h\"\n      \"${CMAKE_CURRENT_SOURCE_DIR}/lin_sys.h\"\n    )\nendif()\n\n# Add the ctrl-c handler if enabled\nif (CTRLC)\n    list(\n      APPEND\n      osqp_headers\n      \"${CMAKE_CURRENT_SOURCE_DIR}/ctrlc.h\"\n    )\nendif()\n\n# Pass the header list up to the main CMakeLists scope\nset(\n  osqp_headers\n  \"${osqp_headers}\"\n  PARENT_SCOPE\n)\n"
  },
  {
    "path": "osqp/include/auxil.h",
    "content": "#ifndef AUXIL_H\n# define AUXIL_H\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif // ifdef __cplusplus\n\n# include \"types.h\"\n\n\n/***********************************************************\n* Auxiliary functions needed to compute ADMM iterations * *\n***********************************************************/\n# if EMBEDDED != 1\n\n/**\n * Compute rho estimate from residuals\n * @param work Workspace\n * @return     rho estimate\n */\nc_float compute_rho_estimate(OSQPWorkspace *work);\n\n/**\n * Adapt rho value based on current unscaled primal/dual residuals\n * @param work Workspace\n * @return     Exitflag\n */\nc_int   adapt_rho(OSQPWorkspace *work);\n\n/**\n * Set values of rho vector based on constraint types\n * @param work Workspace\n */\nvoid    set_rho_vec(OSQPWorkspace *work);\n\n/**\n * Update values of rho vector based on updated constraints.\n * If the constraints change, update the linear systems solver.\n *\n * @param work Workspace\n * @return     Exitflag\n */\nc_int   update_rho_vec(OSQPWorkspace *work);\n\n# endif // EMBEDDED\n\n/**\n * Swap c_float vector pointers\n * @param a first vector\n * @param b second vector\n */\nvoid swap_vectors(c_float **a,\n                  c_float **b);\n\n\n/**\n * Cold start workspace variables xz and y\n * @param work Workspace\n */\nvoid cold_start(OSQPWorkspace *work);\n\n\n/**\n * Update x_tilde and z_tilde variable (first ADMM step)\n * @param work [description]\n */\nvoid update_xz_tilde(OSQPWorkspace *work);\n\n\n/**\n * Update x (second ADMM step)\n * Update also delta_x (For for dual infeasibility)\n * @param work Workspace\n */\nvoid update_x(OSQPWorkspace *work);\n\n\n/**\n * Update z (third ADMM step)\n * @param work Workspace\n */\nvoid update_z(OSQPWorkspace *work);\n\n\n/**\n * Update y variable (fourth ADMM step)\n * Update also delta_y to check for primal infeasibility\n * @param work Workspace\n */\nvoid update_y(OSQPWorkspace *work);\n\n\n/**\n * Compute objective function from data at value x\n * @param  work OSQPWorkspace structure\n * @param  x    Value x\n * @return      Objective function value\n */\nc_float compute_obj_val(OSQPWorkspace *work,\n                        c_float       *x);\n\n/**\n * Check whether QP has solution\n * @param info OSQPInfo\n */\nc_int has_solution(OSQPInfo *info);\n\n/**\n * Store the QP solution\n * @param work Workspace\n */\nvoid store_solution(OSQPWorkspace *work);\n\n\n/**\n * Update solver information\n * @param work               Workspace\n * @param iter               Iteration number\n * @param compute_objective  Boolean (if compute the objective or not)\n * @param polish             Boolean (if called from polish)\n */\nvoid update_info(OSQPWorkspace *work,\n                 c_int          iter,\n                 c_int          compute_objective,\n                 c_int          polish);\n\n\n/**\n * Reset solver information (after problem updates)\n * @param info               Information structure\n */\nvoid reset_info(OSQPInfo *info);\n\n\n/**\n * Update solver status (value and string)\n * @param info OSQPInfo\n * @param status_val new status value\n */\nvoid update_status(OSQPInfo *info,\n                   c_int     status_val);\n\n\n/**\n * Check if termination conditions are satisfied\n * If the boolean flag is ON, it checks for approximate conditions (10 x larger\n * tolerances than the ones set)\n *\n * @param  work        Workspace\n * @param  approximate Boolean\n * @return      Residuals check\n */\nc_int check_termination(OSQPWorkspace *work,\n                        c_int          approximate);\n\n\n# ifndef EMBEDDED\n\n/**\n * Validate problem data\n * @param  data OSQPData to be validated\n * @return      Exitflag to check\n */\nc_int validate_data(const OSQPData *data);\n\n\n/**\n * Validate problem settings\n * @param  settings OSQPSettings to be validated\n * @return          Exitflag to check\n */\nc_int validate_settings(const OSQPSettings *settings);\n\n\n# endif // #ifndef EMBEDDED\n\n# ifdef __cplusplus\n}\n# endif // ifdef __cplusplus\n\n#endif // ifndef AUXIL_H\n"
  },
  {
    "path": "osqp/include/constants.h",
    "content": "#ifndef CONSTANTS_H\n# define CONSTANTS_H\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif // ifdef __cplusplus\n\n\n/*******************\n* OSQP Versioning *\n*******************/\n# define OSQP_VERSION (\"0.6.0\") /* string literals automatically null-terminated\n                                   */\n\n/******************\n* Solver Status  *\n******************/\n# define OSQP_DUAL_INFEASIBLE_INACCURATE (4)\n# define OSQP_PRIMAL_INFEASIBLE_INACCURATE (3)\n# define OSQP_SOLVED_INACCURATE (2)\n# define OSQP_SOLVED (1)\n# define OSQP_MAX_ITER_REACHED (-2)\n# define OSQP_PRIMAL_INFEASIBLE (-3)    /* primal infeasible  */\n# define OSQP_DUAL_INFEASIBLE (-4)      /* dual infeasible */\n# define OSQP_SIGINT (-5)               /* interrupted by user */\n# ifdef PROFILING\n#  define OSQP_TIME_LIMIT_REACHED (-6)\n# endif // ifdef PROFILING\n# define OSQP_NON_CVX (-7)              /* problem non convex */\n# define OSQP_UNSOLVED (-10)            /* Unsolved. Only setup function has been called */\n\n\n/*************************\n* Linear System Solvers *\n*************************/\nenum linsys_solver_type { QDLDL_SOLVER, MKL_PARDISO_SOLVER };\nextern const char * LINSYS_SOLVER_NAME[];\n\n\n/******************\n* Solver Errors  *\n******************/\nenum osqp_error_type {\n    OSQP_DATA_VALIDATION_ERROR = 1,  /* Start errors from 1 */\n    OSQP_SETTINGS_VALIDATION_ERROR,\n    OSQP_LINSYS_SOLVER_LOAD_ERROR,\n    OSQP_LINSYS_SOLVER_INIT_ERROR,\n    OSQP_NONCVX_ERROR,\n    OSQP_MEM_ALLOC_ERROR,\n    OSQP_WORKSPACE_NOT_INIT_ERROR,\n};\nextern const char * OSQP_ERROR_MESSAGE[];\n\n\n/**********************************\n* Solver Parameters and Settings *\n**********************************/\n\n# define RHO (0.1)\n# define SIGMA (1E-06)\n# define MAX_ITER (4000)\n# define EPS_ABS (1E-3)\n# define EPS_REL (1E-3)\n# define EPS_PRIM_INF (1E-4)\n# define EPS_DUAL_INF (1E-4)\n# define ALPHA (1.6)\n# define LINSYS_SOLVER (QDLDL_SOLVER)\n\n# define RHO_MIN (1e-06)\n# define RHO_MAX (1e06)\n# define RHO_EQ_OVER_RHO_INEQ (1e03)\n# define RHO_TOL (1e-04) ///< tolerance for detecting if an inequality is set to equality\n\n\n# ifndef EMBEDDED\n#  define DELTA (1E-6)\n#  define POLISH (0)\n#  define POLISH_REFINE_ITER (3)\n#  define VERBOSE (1)\n# endif // ifndef EMBEDDED\n\n# define SCALED_TERMINATION (0)\n# define CHECK_TERMINATION (25)\n# define WARM_START (1)\n# define SCALING (10)\n\n# define MIN_SCALING (1e-04) ///< minimum scaling value\n# define MAX_SCALING (1e+04) ///< maximum scaling value\n\n\n# ifndef OSQP_NULL\n#  define OSQP_NULL 0\n# endif /* ifndef OSQP_NULL */\n\n# ifndef OSQP_NAN\n#  define OSQP_NAN ((c_float)0x7fc00000UL)  // not a number\n# endif /* ifndef OSQP_NAN */\n\n# ifndef OSQP_INFTY\n#  define OSQP_INFTY ((c_float)1e30)        // infinity\n# endif /* ifndef OSQP_INFTY */\n\n\n# if EMBEDDED != 1\n#  define ADAPTIVE_RHO (1)\n#  define ADAPTIVE_RHO_INTERVAL (0)\n#  define ADAPTIVE_RHO_FRACTION (0.4)           ///< fraction of setup time after which we update rho\n#  define ADAPTIVE_RHO_MULTIPLE_TERMINATION (4) ///< multiple of check_termination after which we update rho (if PROFILING disabled)\n#  define ADAPTIVE_RHO_FIXED (100)              ///< number of iterations after which we update rho if termination_check  and PROFILING are disabled\n#  define ADAPTIVE_RHO_TOLERANCE (5)            ///< tolerance for adopting new rho; minimum ratio between new rho and the current one\n# endif // if EMBEDDED != 1\n\n# ifdef PROFILING\n#  define TIME_LIMIT (0)                        ///< Disable time limit as default\n# endif // ifdef PROFILING\n\n/* Printing */\n# define PRINT_INTERVAL 200\n\n\n# ifdef __cplusplus\n}\n# endif // ifdef __cplusplus\n\n#endif // ifndef CONSTANTS_H\n"
  },
  {
    "path": "osqp/include/cs.h",
    "content": "#ifndef CS_H\n# define CS_H\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif // ifdef __cplusplus\n\n# include \"types.h\"   // CSC matrix type\n# include \"lin_alg.h\" // Vector copy operations\n\n/*****************************************************************************\n* Create and free CSC Matrices                                              *\n*****************************************************************************/\n\n/**\n * Create Compressed-Column-Sparse matrix from existing arrays\n    (no MALLOC to create inner arrays x, i, p)\n * @param  m     First dimension\n * @param  n     Second dimension\n * @param  nzmax Maximum number of nonzero elements\n * @param  x     Vector of data\n * @param  i     Vector of row indices\n * @param  p     Vector of column pointers\n * @return       New matrix pointer\n */\ncsc* csc_matrix(c_int    m,\n                c_int    n,\n                c_int    nzmax,\n                c_float *x,\n                c_int   *i,\n                c_int   *p);\n\n\n/**\n * Create uninitialized CSC matrix atricture\n    (uses MALLOC to create inner arrays x, i, p)\n * @param  m       First dimension\n * @param  n       Second dimension\n * @param  nzmax   Maximum number of nonzero elements\n * @param  values  Allocate values (0/1)\n * @param  triplet Allocate CSC or triplet format matrix (1/0)\n * @return         Matrix pointer\n */\ncsc* csc_spalloc(c_int m,\n                 c_int n,\n                 c_int nzmax,\n                 c_int values,\n                 c_int triplet);\n\n\n/**\n * Free sparse matrix\n    (uses FREE to free inner arrays x, i, p)\n * @param  A Matrix in CSC format\n */\nvoid csc_spfree(csc *A);\n\n\n/**\n * free workspace and return a sparse matrix result\n * @param  C  CSC matrix\n * @param  w  Workspace vector\n * @param  x  Workspace vector\n * @param  ok flag\n * @return    Return result C if OK, otherwise free it\n */\ncsc* csc_done(csc  *C,\n              void *w,\n              void *x,\n              c_int ok);\n\n/*****************************************************************************\n* Copy Matrices                                                             *\n*****************************************************************************/\n\n/**\n *  Copy sparse CSC matrix A to output.\n *  output is allocated by this function (uses MALLOC)\n */\ncsc* copy_csc_mat(const csc *A);\n\n\n/**\n *  Copy sparse CSC matrix A to B (B is preallocated, NO MALOC)\n */\nvoid prea_copy_csc_mat(const csc *A,\n                       csc       *B);\n\n\n/*****************************************************************************\n* Matrices Conversion                                                       *\n*****************************************************************************/\n\n\n/**\n * C = compressed-column CSC from matrix T in triplet form\n *\n * TtoC stores the vector of indices from T to C\n *  -> C[TtoC[i]] = T[i]\n *\n * @param  T    matrix in triplet format\n * @param  TtoC vector of indices from triplet to CSC format\n * @return      matrix in CSC format\n */\ncsc* triplet_to_csc(const csc *T,\n                    c_int     *TtoC);\n\n\n/**\n * C = compressed-row CSR from matrix T in triplet form\n *\n * TtoC stores the vector of indices from T to C\n *  -> C[TtoC[i]] = T[i]\n *\n * @param  T    matrix in triplet format\n * @param  TtoC vector of indices from triplet to CSR format\n * @return      matrix in CSR format\n */\ncsc* triplet_to_csr(const csc *T,\n                    c_int     *TtoC);\n\n\n/**\n * Convert sparse to dense\n */\nc_float* csc_to_dns(csc *M);\n\n\n/**\n * Convert square CSC matrix into upper triangular one\n *\n * @param  M         Matrix to be converted\n * @return           Upper triangular matrix in CSC format\n */\ncsc* csc_to_triu(csc *M);\n\n\n/*****************************************************************************\n* Extra operations                                                          *\n*****************************************************************************/\n\n/**\n * p [0..n] = cumulative sum of c [0..n-1], and then copy p [0..n-1] into c\n *\n * @param  p Create cumulative sum into p\n * @param  c Vector of which we compute cumulative sum\n * @param  n Number of elements\n * @return   Exitflag\n */\nc_int csc_cumsum(c_int *p,\n                 c_int *c,\n                 c_int  n);\n\n/**\n * Compute inverse of permutation matrix stored in the vector p.\n * The computed inverse is also stored in a vector.\n */\nc_int* csc_pinv(c_int const *p,\n                c_int        n);\n\n/**\n * C = A(p,p)= PAP' where A and C are symmetric the upper part stored;\n *  NB: pinv not p!\n * @param  A      Original matrix (upper-triangular)\n * @param  pinv   Inverse of permutation vector\n * @param  AtoC   Mapping from indices of A-x to C->x\n * @param  values Are values of A allocated?\n * @return        New matrix (allocated)\n */\ncsc* csc_symperm(const csc   *A,\n                 const c_int *pinv,\n                 c_int       *AtoC,\n                 c_int        values);\n\n\n# ifdef __cplusplus\n}\n# endif // ifdef __cplusplus\n\n#endif // ifndef CS_H\n"
  },
  {
    "path": "osqp/include/ctrlc.h",
    "content": "/*\n * Interface for OSQP signal handling.\n */\n\n#ifndef CTRLC_H\n# define CTRLC_H\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif // ifdef __cplusplus\n\n# include \"glob_opts.h\"\n\n# if defined MATLAB\n\n/* No header file available here; define the prototypes ourselves */\nbool utIsInterruptPending(void);\nbool utSetInterruptEnabled(bool);\n\n# elif defined IS_WINDOWS\n\n/* Use Windows SetConsoleCtrlHandler for signal handling */\n#  include <windows.h>\n\n# else // if defined MATLAB\n\n/* Use sigaction for signal handling on non-Windows machines */\n#  include <signal.h>\n\n# endif // if defined MATLAB\n\n/* METHODS are the same for both */\n\n/**\n * Start listener for ctrl-c interrupts\n */\nvoid osqp_start_interrupt_listener(void);\n\n/**\n * End listener for ctrl-c interrupts\n */\nvoid osqp_end_interrupt_listener(void);\n\n/**\n * Check if the solver has been interrupted\n * @return  Boolean indicating if the solver has been interrupted\n */\nint osqp_is_interrupted(void);\n\n\n# ifdef __cplusplus\n}\n# endif // ifdef __cplusplus\n\n\n#endif /* END IFDEF CTRLC */\n"
  },
  {
    "path": "osqp/include/error.h",
    "content": "#ifndef ERROR_H\n# define ERROR_H\n\n/* OSQP error handling */\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif // ifdef __cplusplus\n\n# include \"types.h\"\n\n\n/* OSQP error macro */\n# if __STDC_VERSION__ >= 199901L\n/* The C99 standard gives the __func__ macro, which is preferred over __FUNCTION__ */\n#  define osqp_error(error_code) _osqp_error(error_code, __func__);\n#else\n#  define osqp_error(error_code) _osqp_error(error_code, __FUNCTION__);\n#endif\n\n\n\n/**\n * Internal function to print error description and return error code.\n * @param  Error code\n * @param  Function name\n * @return Error code\n */\n  c_int _osqp_error(enum osqp_error_type error_code,\n\t\t    const char * function_name);\n\n\n\n# ifdef __cplusplus\n}\n# endif // ifdef __cplusplus\n\n#endif // ifndef ERROR_H\n"
  },
  {
    "path": "osqp/include/glob_opts.h",
    "content": "#ifndef GLOB_OPTS_H\n# define GLOB_OPTS_H\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif /* ifdef __cplusplus */\n\n/*\n   Define OSQP compiler flags\n */\n\n// cmake generated compiler flags\n#include \"osqp_configure.h\"\n\n/* DATA CUSTOMIZATIONS (depending on memory manager)-----------------------   */\n\n// We do not need memory allocation functions if EMBEDDED is enabled\n# ifndef EMBEDDED\n\n/* define custom printfs and memory allocation (e.g. matlab/python) */\n#  ifdef MATLAB\n    #   include \"mex.h\"\nstatic void* c_calloc(size_t num, size_t size) {\n  void *m = mxCalloc(num, size);\n  mexMakeMemoryPersistent(m);\n  return m;\n}\n\nstatic void* c_malloc(size_t size) {\n  void *m = mxMalloc(size);\n  mexMakeMemoryPersistent(m);\n  return m;\n}\n\nstatic void* c_realloc(void *ptr, size_t size) {\n  void *m = mxRealloc(ptr, size);\n  mexMakeMemoryPersistent(m);\n  return m;\n}\n\n    #   define c_free mxFree\n#  elif defined PYTHON\n// Define memory allocation for python. Note that in Python 2 memory manager\n// Calloc is not implemented\n    #   include <Python.h>\n    #   define c_malloc PyMem_Malloc\n    #   if PY_MAJOR_VERSION >= 3\n    #    define c_calloc PyMem_Calloc\n    #   else  /* if PY_MAJOR_VERSION >= 3 */\nstatic void* c_calloc(size_t num, size_t size) {\n  void *m = PyMem_Malloc(num * size);\n  memset(m, 0, num * size);\n  return m;\n}\n    #   endif /* if PY_MAJOR_VERSION >= 3 */\n    #   define c_free PyMem_Free\n    #   define c_realloc PyMem_Realloc\n\n# elif !defined OSQP_CUSTOM_MEMORY\n/* If no custom memory allocator defined, use\n * standard linux functions. Custom memory allocator definitions\n * appear in the osqp_configure.h generated file. */\n    #  include <stdlib.h>\n    #  define c_malloc  malloc\n    #  define c_calloc  calloc\n    #  define c_free    free\n    #  define c_realloc realloc\n#  endif /* ifdef MATLAB */\n\n# endif // end ifndef EMBEDDED\n\n\n/* Use customized number representation -----------------------------------   */\n# ifdef DLONG            // long integers\ntypedef long long c_int; /* for indices */\n# else // standard integers\ntypedef int c_int;       /* for indices */\n# endif /* ifdef DLONG */\n\n\n# ifndef DFLOAT         // Doubles\ntypedef double c_float; /* for numerical values  */\n# else                  // Floats\ntypedef float c_float;  /* for numerical values  */\n# endif /* ifndef DFLOAT */\n\n\n/* Use customized operations */\n\n# ifndef c_absval\n#  define c_absval(x) (((x) < 0) ? -(x) : (x))\n# endif /* ifndef c_absval */\n\n# ifndef c_max\n#  define c_max(a, b) (((a) > (b)) ? (a) : (b))\n# endif /* ifndef c_max */\n\n# ifndef c_min\n#  define c_min(a, b) (((a) < (b)) ? (a) : (b))\n# endif /* ifndef c_min */\n\n// Round x to the nearest multiple of N\n# ifndef c_roundmultiple\n#  define c_roundmultiple(x, N) ((x) + .5 * (N)-c_fmod((x) + .5 * (N), (N)))\n# endif /* ifndef c_roundmultiple */\n\n\n/* Use customized functions -----------------------------------------------   */\n\n# if EMBEDDED != 1\n\n#  include <math.h>\n#  ifndef DFLOAT // Doubles\n#   define c_sqrt sqrt\n#   define c_fmod fmod\n#  else          // Floats\n#   define c_sqrt sqrtf\n#   define c_fmod fmodf\n#  endif /* ifndef DFLOAT */\n\n# endif // end EMBEDDED\n\n\n# ifdef PRINTING\n#  include <stdio.h>\n#  include <string.h>\n\n#  ifdef MATLAB\n#   define c_print mexPrintf\n\n// The following trick slows down the performance a lot. Since many solvers\n// actually\n// call mexPrintf and immediately force print buffer flush\n// otherwise messages don't appear until solver termination\n// ugly because matlab does not provide a vprintf mex interface\n// #include <stdarg.h>\n// static int c_print(char *msg, ...)\n// {\n//   va_list argList;\n//   va_start(argList, msg);\n//   //message buffer\n//   int bufferSize = 256;\n//   char buffer[bufferSize];\n//   vsnprintf(buffer,bufferSize-1, msg, argList);\n//   va_end(argList);\n//   int out = mexPrintf(buffer); //print to matlab display\n//   mexEvalString(\"drawnow;\");   // flush matlab print buffer\n//   return out;\n// }\n#  elif defined PYTHON\n#   include <Python.h>\n#   define c_print PySys_WriteStdout\n#  elif defined R_LANG\n#   include <R_ext/Print.h>\n#   define c_print Rprintf\n#  else  /* ifdef MATLAB */\n#   define c_print printf\n#  endif /* ifdef MATLAB */\n\n/* Print error macro */\n#  define c_eprint(...) c_print(\"ERROR in %s: \", __FUNCTION__); c_print(\\\n    __VA_ARGS__); c_print(\"\\n\");\n\n# endif  /* PRINTING */\n\n\n# ifdef __cplusplus\n}\n# endif /* ifdef __cplusplus */\n\n#endif /* ifndef GLOB_OPTS_H */\n"
  },
  {
    "path": "osqp/include/kkt.h",
    "content": "#ifndef KKT_H\n# define KKT_H\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif // ifdef __cplusplus\n\n# include \"types.h\"\n\n# ifndef EMBEDDED\n\n#  include \"cs.h\"\n\n/**\n * Form square symmetric KKT matrix of the form\n *\n * [P + param1 I,            A';\n *  A             -diag(param2)]\n *\n * NB: Only the upper triangular part is stuffed!\n *\n *\n *  If Pdiag_idx is not OSQP_NULL, it saves the index of the diagonal\n * elements of P there and the number of diagonal elements in Pdiag_n.\n *\n * Similarly, if rhotoKKT is not null,\n * it saves where the values of param2 go in the final KKT matrix\n *\n * NB: Pdiag_idx needs to be freed!\n *\n * @param  P          cost matrix (already just upper triangular part)\n * @param  A          linear constraint matrix\n * @param  format     CSC (0) or CSR (1)\n * @param  param1     regularization parameter\n * @param  param2     regularization parameter (vector)\n * @param  PtoKKT     (modified) index mapping from elements of P to KKT matrix\n * @param  AtoKKT     (modified) index mapping from elements of A to KKT matrix\n * @param  Pdiag_idx  (modified) Address of the index of diagonal elements in P\n * @param  Pdiag_n    (modified) Address to the number of diagonal elements in P\n * @param  param2toKKT    (modified) index mapping from param2 to elements of\n *KKT\n * @return            return status flag\n */\ncsc* form_KKT(const csc  *P,\n              const  csc *A,\n              c_int       format,\n              c_float     param1,\n              c_float    *param2,\n              c_int      *PtoKKT,\n              c_int      *AtoKKT,\n              c_int     **Pdiag_idx,\n              c_int      *Pdiag_n,\n              c_int      *param2toKKT);\n# endif // ifndef EMBEDDED\n\n\n# if EMBEDDED != 1\n\n/**\n * Update KKT matrix using the elements of P\n *\n * @param KKT       KKT matrix in CSC form (upper-triangular)\n * @param P         P matrix in CSC form (upper-triangular)\n * @param PtoKKT    Vector of pointers from P->x to KKT->x\n * @param param1    Parameter added to the diagonal elements of P\n * @param Pdiag_idx Index of diagonal elements in P->x\n * @param Pdiag_n   Number of diagonal elements of P\n */\nvoid update_KKT_P(csc          *KKT,\n                  const csc    *P,\n                  const c_int  *PtoKKT,\n                  const c_float param1,\n                  const c_int  *Pdiag_idx,\n                  const c_int   Pdiag_n);\n\n\n/**\n * Update KKT matrix using the elements of A\n *\n * @param KKT       KKT matrix in CSC form (upper-triangular)\n * @param A         A matrix in CSC form (upper-triangular)\n * @param AtoKKT    Vector of pointers from A->x to KKT->x\n */\nvoid update_KKT_A(csc         *KKT,\n                  const csc   *A,\n                  const c_int *AtoKKT);\n\n\n/**\n * Update KKT matrix with new param2\n *\n * @param KKT           KKT matrix\n * @param param2        Parameter of the KKT matrix (vector)\n * @param param2toKKT   index where param2 enters in the KKT matrix\n * @param m             number of constraints\n */\nvoid update_KKT_param2(csc           *KKT,\n                       const c_float *param2,\n                       const c_int   *param2toKKT,\n                       const c_int    m);\n\n# endif // EMBEDDED != 1\n\n\n# ifdef __cplusplus\n}\n# endif // ifdef __cplusplus\n\n#endif // ifndef KKT_H\n"
  },
  {
    "path": "osqp/include/lin_alg.h",
    "content": "#ifndef LIN_ALG_H\n# define LIN_ALG_H\n\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif // ifdef __cplusplus\n\n# include \"types.h\"\n\n\n/* VECTOR FUNCTIONS ----------------------------------------------------------*/\n\n# ifndef EMBEDDED\n\n/* copy vector a into output (Uses MALLOC)*/\nc_float* vec_copy(c_float *a,\n                  c_int    n);\n# endif // ifndef EMBEDDED\n\n/* copy vector a into preallocated vector b */\nvoid prea_vec_copy(const c_float *a,\n                   c_float       *b,\n                   c_int          n);\n\n/* copy integer vector a into preallocated vector b */\nvoid prea_int_vec_copy(const c_int *a,\n                       c_int       *b,\n                       c_int        n);\n\n/* set float vector to scalar */\nvoid vec_set_scalar(c_float *a,\n                    c_float  sc,\n                    c_int    n);\n\n/* set integer vector to scalar */\nvoid int_vec_set_scalar(c_int *a,\n                        c_int  sc,\n                        c_int  n);\n\n/* add scalar to vector*/\nvoid vec_add_scalar(c_float *a,\n                    c_float  sc,\n                    c_int    n);\n\n/* multiply scalar to vector */\nvoid vec_mult_scalar(c_float *a,\n                     c_float  sc,\n                     c_int    n);\n\n/* c = a + sc*b */\nvoid vec_add_scaled(c_float       *c,\n                    const c_float *a,\n                    const c_float *b,\n                    c_int          n,\n                    c_float        sc);\n\n/* ||v||_inf */\nc_float vec_norm_inf(const c_float *v,\n                     c_int          l);\n\n/* ||Sv||_inf */\nc_float vec_scaled_norm_inf(const c_float *S,\n                            const c_float *v,\n                            c_int          l);\n\n/* ||a - b||_inf */\nc_float vec_norm_inf_diff(const c_float *a,\n                          const c_float *b,\n                          c_int          l);\n\n/* mean of vector elements */\nc_float vec_mean(const c_float *a,\n                 c_int          n);\n\n# if EMBEDDED != 1\n\n/* Vector elementwise reciprocal b = 1./a (needed for scaling)*/\nvoid vec_ew_recipr(const c_float *a,\n                   c_float       *b,\n                   c_int          n);\n# endif // if EMBEDDED != 1\n\n/* Inner product a'b */\nc_float vec_prod(const c_float *a,\n                 const c_float *b,\n                 c_int          n);\n\n/* Elementwise product a.*b stored in c*/\nvoid vec_ew_prod(const c_float *a,\n                 const c_float *b,\n                 c_float       *c,\n                 c_int          n);\n\n# if EMBEDDED != 1\n\n/* Elementwise sqrt of the vector elements */\nvoid vec_ew_sqrt(c_float *a,\n                 c_int    n);\n\n/* Elementwise max between each vector component and max_val */\nvoid vec_ew_max(c_float *a,\n                c_int    n,\n                c_float  max_val);\n\n/* Elementwise min between each vector component and max_val */\nvoid vec_ew_min(c_float *a,\n                c_int    n,\n                c_float  min_val);\n\n/* Elementwise maximum between vectors c = max(a, b) */\nvoid vec_ew_max_vec(const c_float *a,\n                    const c_float *b,\n                    c_float       *c,\n                    c_int          n);\n\n/* Elementwise minimum between vectors c = min(a, b) */\nvoid vec_ew_min_vec(const c_float *a,\n                    const c_float *b,\n                    c_float       *c,\n                    c_int          n);\n\n# endif // if EMBEDDED != 1\n\n\n/* MATRIX FUNCTIONS ----------------------------------------------------------*/\n\n/* multiply scalar to matrix */\nvoid mat_mult_scalar(csc    *A,\n                     c_float sc);\n\n/* Premultiply matrix A by diagonal matrix with diagonal d,\n   i.e. scale the rows of A by d\n */\nvoid mat_premult_diag(csc           *A,\n                      const c_float *d);\n\n/* Premultiply matrix A by diagonal matrix with diagonal d,\n   i.e. scale the columns of A by d\n */\nvoid mat_postmult_diag(csc           *A,\n                       const c_float *d);\n\n\n/* Matrix-vector multiplication\n *    y  =  A*x  (if plus_eq == 0)\n *    y +=  A*x  (if plus_eq == 1)\n *    y -=  A*x  (if plus_eq == -1)\n */\nvoid mat_vec(const csc     *A,\n             const c_float *x,\n             c_float       *y,\n             c_int          plus_eq);\n\n\n/* Matrix-transpose-vector multiplication\n *    y  =  A'*x  (if plus_eq == 0)\n *    y +=  A'*x  (if plus_eq == 1)\n *    y -=  A'*x  (if plus_eq == -1)\n * If skip_diag == 1, then diagonal elements of A are assumed to be zero.\n */\nvoid mat_tpose_vec(const csc     *A,\n                   const c_float *x,\n                   c_float       *y,\n                   c_int          plus_eq,\n                   c_int          skip_diag);\n\n\n# if EMBEDDED != 1\n\n/**\n * Infinity norm of each matrix column\n * @param M\tInput matrix\n * @param E     Vector of infinity norms\n *\n */\nvoid mat_inf_norm_cols(const csc *M,\n                       c_float   *E);\n\n/**\n * Infinity norm of each matrix row\n * @param M\tInput matrix\n * @param E     Vector of infinity norms\n *\n */\nvoid mat_inf_norm_rows(const csc *M,\n                       c_float   *E);\n\n/**\n * Infinity norm of each matrix column\n * Matrix M is symmetric upper-triangular\n *\n * @param M\tInput matrix (symmetric, upper-triangular)\n * @param E     Vector of infinity norms\n *\n */\nvoid mat_inf_norm_cols_sym_triu(const csc *M,\n                                c_float   *E);\n\n# endif // EMBEDDED != 1\n\n/**\n * Compute quadratic form f(x) = 1/2 x' P x\n * @param  P quadratic matrix in CSC form (only upper triangular)\n * @param  x argument float vector\n * @return   quadratic form value\n */\nc_float quad_form(const csc     *P,\n                  const c_float *x);\n\n\n# ifdef __cplusplus\n}\n# endif // ifdef __cplusplus\n\n#endif // ifndef LIN_ALG_H\n"
  },
  {
    "path": "osqp/include/lin_sys.h",
    "content": "#ifndef LIN_SYS_H\n# define LIN_SYS_H\n\n/* KKT linear system definition and solution */\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif // ifdef __cplusplus\n\n# include \"types.h\"\n\n/**\n * Load linear system solver shared library\n * @param\tlinsys_solver  Linear system solver\n * @return Zero on success, nonzero on failure.\n */\nc_int load_linsys_solver(enum linsys_solver_type linsys_solver);\n\n\n/**\n * Unload linear system solver shared library\n * @param\tlinsys_solver  Linear system solver\n * @return Zero on success, nonzero on failure.\n */\nc_int unload_linsys_solver(enum linsys_solver_type linsys_solver);\n\n\n// NB: Only the upper triangular part of P is stuffed!\n\n/**\n * Initialize linear system solver structure\n * @param   s             Pointer to linear system solver structure\n * @param   P             Cost function matrix\n * @param\tA             Constraint matrix\n * @param\tsigma         Algorithm parameter\n * @param\trho_vec       Algorithm parameter\n * @param\tlinsys_solver Linear system solver\n * @param\tpolish        0/1 depending whether we are allocating for\n *polishing or not\n * @return                Exitflag for error (0 if no errors)\n */\nc_int init_linsys_solver(LinSysSolver          **s,\n                         const csc              *P,\n                         const csc              *A,\n                         c_float                 sigma,\n                         const c_float          *rho_vec,\n                         enum linsys_solver_type linsys_solver,\n                         c_int                   polish);\n\n# ifdef __cplusplus\n}\n# endif // ifdef __cplusplus\n\n#endif // ifndef LIN_SYS_H\n"
  },
  {
    "path": "osqp/include/osqp.h",
    "content": "#ifndef OSQP_H\n# define OSQP_H\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif // ifdef __cplusplus\n\n/* Includes */\n# include \"types.h\"\n# include \"util.h\" // Needed for osqp_set_default_settings functions\n\n\n// Library to deal with sparse matrices enabled only if embedded not defined\n# ifndef EMBEDDED\n#  include \"cs.h\"\n# endif // ifndef EMBEDDED\n\n/********************\n* Main Solver API  *\n********************/\n\n/**\n * @name Main solver API\n * @{\n */\n\n/**\n * Set default settings from constants.h file\n * assumes settings already allocated in memory\n * @param settings settings structure\n */\nvoid osqp_set_default_settings(OSQPSettings *settings);\n\n\n# ifndef EMBEDDED\n\n/**\n * Initialize OSQP solver allocating memory.\n *\n * All the inputs must be already allocated in memory before calling.\n *\n * It performs:\n * - data and settings validation\n * - problem data scaling\n * - automatic parameters tuning (if enabled)\n * - setup linear system solver:\n *      - direct solver: KKT matrix factorization is performed here\n *      - indirect solver: KKT matrix preconditioning is performed here\n *\n * NB: This is the only function that allocates dynamic memory and is not used\n *during code generation\n *\n * @param  workp        Solver workspace pointer\n * @param  data         Problem data\n * @param  settings     Solver settings\n * @return              Exitflag for errors (0 if no errors)\n */\nc_int osqp_setup(OSQPWorkspace** workp, const OSQPData* data, const OSQPSettings* settings);\n\n# endif // #ifndef EMBEDDED\n\n/**\n * Solve quadratic program\n *\n * The final solver information is stored in the \\a work->info  structure\n *\n * The solution is stored in the  \\a work->solution  structure\n *\n * If the problem is primal infeasible, the certificate is stored\n * in \\a work->delta_y\n *\n * If the problem is dual infeasible, the certificate is stored in \\a\n * work->delta_x\n *\n * @param  work Workspace allocated\n * @return      Exitflag for errors\n */\nc_int osqp_solve(OSQPWorkspace *work);\n\n\n# ifndef EMBEDDED\n\n/**\n * Cleanup workspace by deallocating memory\n *\n * This function is not used in code generation\n * @param  work Workspace\n * @return      Exitflag for errors\n */\nc_int osqp_cleanup(OSQPWorkspace *work);\n\n# endif // ifndef EMBEDDED\n\n/** @} */\n\n\n/********************************************\n* Sublevel API                             *\n*                                          *\n* Edit data without performing setup again *\n********************************************/\n\n/**\n * @name Sublevel API\n * @{\n */\n\n/**\n * Update linear cost in the problem\n * @param  work  Workspace\n * @param  q_new New linear cost\n * @return       Exitflag for errors and warnings\n */\nc_int osqp_update_lin_cost(OSQPWorkspace *work,\n                           const c_float *q_new);\n\n\n/**\n * Update lower and upper bounds in the problem constraints\n * @param  work   Workspace\n * @param  l_new New lower bound\n * @param  u_new New upper bound\n * @return        Exitflag: 1 if new lower bound is not <= than new upper bound\n */\nc_int osqp_update_bounds(OSQPWorkspace *work,\n                         const c_float *l_new,\n                         const c_float *u_new);\n\n\n/**\n * Update lower bound in the problem constraints\n * @param  work   Workspace\n * @param  l_new New lower bound\n * @return        Exitflag: 1 if new lower bound is not <= than upper bound\n */\nc_int osqp_update_lower_bound(OSQPWorkspace *work,\n                              const c_float *l_new);\n\n\n/**\n * Update upper bound in the problem constraints\n * @param  work   Workspace\n * @param  u_new New upper bound\n * @return        Exitflag: 1 if new upper bound is not >= than lower bound\n */\nc_int osqp_update_upper_bound(OSQPWorkspace *work,\n                              const c_float *u_new);\n\n\n/**\n * Warm start primal and dual variables\n * @param  work Workspace structure\n * @param  x    Primal variable\n * @param  y    Dual variable\n * @return      Exitflag\n */\nc_int osqp_warm_start(OSQPWorkspace *work,\n                      const c_float *x,\n                      const c_float *y);\n\n\n/**\n * Warm start primal variable\n * @param  work Workspace structure\n * @param  x    Primal variable\n * @return      Exitflag\n */\nc_int osqp_warm_start_x(OSQPWorkspace *work,\n                        const c_float *x);\n\n\n/**\n * Warm start dual variable\n * @param  work Workspace structure\n * @param  y    Dual variable\n * @return      Exitflag\n */\nc_int osqp_warm_start_y(OSQPWorkspace *work,\n                        const c_float *y);\n\n\n# if EMBEDDED != 1\n\n/**\n * Update elements of matrix P (upper triangular)\n * without changing sparsity structure.\n *\n *\n *  If Px_new_idx is OSQP_NULL, Px_new is assumed to be as long as P->x\n *  and the whole P->x is replaced.\n *\n * @param  work       Workspace structure\n * @param  Px_new     Vector of new elements in P->x (upper triangular)\n * @param  Px_new_idx Index mapping new elements to positions in P->x\n * @param  P_new_n    Number of new elements to be changed\n * @return            output flag:  0: OK\n *                                  1: P_new_n > nnzP\n *                                 <0: error in the update\n */\nc_int osqp_update_P(OSQPWorkspace *work,\n                    const c_float *Px_new,\n                    const c_int   *Px_new_idx,\n                    c_int          P_new_n);\n\n\n/**\n * Update elements of matrix A without changing sparsity structure.\n *\n *\n *  If Ax_new_idx is OSQP_NULL, Ax_new is assumed to be as long as A->x\n *  and the whole A->x is replaced.\n *\n * @param  work       Workspace structure\n * @param  Ax_new     Vector of new elements in A->x\n * @param  Ax_new_idx Index mapping new elements to positions in A->x\n * @param  A_new_n    Number of new elements to be changed\n * @return            output flag:  0: OK\n *                                  1: A_new_n > nnzA\n *                                 <0: error in the update\n */\nc_int osqp_update_A(OSQPWorkspace *work,\n                    const c_float *Ax_new,\n                    const c_int   *Ax_new_idx,\n                    c_int          A_new_n);\n\n\n/**\n * Update elements of matrix P (upper triangular) and elements of matrix A\n * without changing sparsity structure.\n *\n *\n *  If Px_new_idx is OSQP_NULL, Px_new is assumed to be as long as P->x\n *  and the whole P->x is replaced.\n *\n *  If Ax_new_idx is OSQP_NULL, Ax_new is assumed to be as long as A->x\n *  and the whole A->x is replaced.\n *\n * @param  work       Workspace structure\n * @param  Px_new     Vector of new elements in P->x (upper triangular)\n * @param  Px_new_idx Index mapping new elements to positions in P->x\n * @param  P_new_n    Number of new elements to be changed\n * @param  Ax_new     Vector of new elements in A->x\n * @param  Ax_new_idx Index mapping new elements to positions in A->x\n * @param  A_new_n    Number of new elements to be changed\n * @return            output flag:  0: OK\n *                                  1: P_new_n > nnzP\n *                                  2: A_new_n > nnzA\n *                                 <0: error in the update\n */\nc_int osqp_update_P_A(OSQPWorkspace *work,\n                      const c_float *Px_new,\n                      const c_int   *Px_new_idx,\n                      c_int          P_new_n,\n                      const c_float *Ax_new,\n                      const c_int   *Ax_new_idx,\n                      c_int          A_new_n);\n\n/**\n * Update rho. Limit it between RHO_MIN and RHO_MAX.\n * @param  work         Workspace\n * @param  rho_new      New rho setting\n * @return              Exitflag\n */\nc_int osqp_update_rho(OSQPWorkspace *work,\n                      c_float        rho_new);\n\n# endif // if EMBEDDED != 1\n\n/** @} */\n\n\n/**\n * @name Update settings\n * @{\n */\n\n\n/**\n * Update max_iter setting\n * @param  work         Workspace\n * @param  max_iter_new New max_iter setting\n * @return              Exitflag\n */\nc_int osqp_update_max_iter(OSQPWorkspace *work,\n                           c_int          max_iter_new);\n\n\n/**\n * Update absolute tolernace value\n * @param  work        Workspace\n * @param  eps_abs_new New absolute tolerance value\n * @return             Exitflag\n */\nc_int osqp_update_eps_abs(OSQPWorkspace *work,\n                          c_float        eps_abs_new);\n\n\n/**\n * Update relative tolernace value\n * @param  work        Workspace\n * @param  eps_rel_new New relative tolerance value\n * @return             Exitflag\n */\nc_int osqp_update_eps_rel(OSQPWorkspace *work,\n                          c_float        eps_rel_new);\n\n\n/**\n * Update primal infeasibility tolerance\n * @param  work          Workspace\n * @param  eps_prim_inf_new  New primal infeasibility tolerance\n * @return               Exitflag\n */\nc_int osqp_update_eps_prim_inf(OSQPWorkspace *work,\n                               c_float        eps_prim_inf_new);\n\n\n/**\n * Update dual infeasibility tolerance\n * @param  work          Workspace\n * @param  eps_dual_inf_new  New dual infeasibility tolerance\n * @return               Exitflag\n */\nc_int osqp_update_eps_dual_inf(OSQPWorkspace *work,\n                               c_float        eps_dual_inf_new);\n\n\n/**\n * Update relaxation parameter alpha\n * @param  work  Workspace\n * @param  alpha_new New relaxation parameter value\n * @return       Exitflag\n */\nc_int osqp_update_alpha(OSQPWorkspace *work,\n                        c_float        alpha_new);\n\n\n/**\n * Update warm_start setting\n * @param  work           Workspace\n * @param  warm_start_new New warm_start setting\n * @return                Exitflag\n */\nc_int osqp_update_warm_start(OSQPWorkspace *work,\n                             c_int          warm_start_new);\n\n\n/**\n * Update scaled_termination setting\n * @param  work                 Workspace\n * @param  scaled_termination_new  New scaled_termination setting\n * @return                      Exitflag\n */\nc_int osqp_update_scaled_termination(OSQPWorkspace *work,\n                                     c_int          scaled_termination_new);\n\n/**\n * Update check_termination setting\n * @param  work                   Workspace\n * @param  check_termination_new  New check_termination setting\n * @return                        Exitflag\n */\nc_int osqp_update_check_termination(OSQPWorkspace *work,\n                                    c_int          check_termination_new);\n\n\n# ifndef EMBEDDED\n\n/**\n * Update regularization parameter in polish\n * @param  work      Workspace\n * @param  delta_new New regularization parameter\n * @return           Exitflag\n */\nc_int osqp_update_delta(OSQPWorkspace *work,\n                        c_float        delta_new);\n\n\n/**\n * Update polish setting\n * @param  work          Workspace\n * @param  polish_new New polish setting\n * @return               Exitflag\n */\nc_int osqp_update_polish(OSQPWorkspace *work,\n                         c_int          polish_new);\n\n\n/**\n * Update number of iterative refinement steps in polish\n * @param  work                Workspace\n * @param  polish_refine_iter_new New iterative reginement steps\n * @return                     Exitflag\n */\nc_int osqp_update_polish_refine_iter(OSQPWorkspace *work,\n                                     c_int          polish_refine_iter_new);\n\n\n/**\n * Update verbose setting\n * @param  work        Workspace\n * @param  verbose_new New verbose setting\n * @return             Exitflag\n */\nc_int osqp_update_verbose(OSQPWorkspace *work,\n                          c_int          verbose_new);\n\n\n# endif // #ifndef EMBEDDED\n\n# ifdef PROFILING\n\n/**\n * Update time_limit setting\n * @param  work            Workspace\n * @param  time_limit_new  New time_limit setting\n * @return                 Exitflag\n */\nc_int osqp_update_time_limit(OSQPWorkspace *work,\n                             c_float        time_limit_new);\n# endif // ifdef PROFILING\n\n/** @} */\n\n\n# ifdef __cplusplus\n}\n# endif // ifdef __cplusplus\n\n#endif // ifndef OSQP_H\n"
  },
  {
    "path": "osqp/include/polish.h",
    "content": "/* Solution polish based on assuming the active set */\n#ifndef POLISH_H\n# define POLISH_H\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif // ifdef __cplusplus\n\n\n# include \"types.h\"\n\n/**\n * Solution polish: Solve equality constrained QP with assumed active\n *constraints\n * @param  work Workspace\n * @return      Exitflag\n */\nc_int polish(OSQPWorkspace *work);\n\n\n# ifdef __cplusplus\n}\n# endif // ifdef __cplusplus\n\n#endif // ifndef POLISH_H\n"
  },
  {
    "path": "osqp/include/proj.h",
    "content": "#ifndef PROJ_H\n# define PROJ_H\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif // ifdef __cplusplus\n\n# include \"types.h\"\n\n\n/* Define Projections onto set C involved in the ADMM algorithm */\n\n/**\n * Project z onto \\f$C = [l, u]\\f$\n * @param z    Vector to project\n * @param work Workspace\n */\nvoid project(OSQPWorkspace *work,\n             c_float       *z);\n\n\n/**\n * Ensure z satisfies box constraints and y is is normal cone of z\n * @param work Workspace\n * @param z    Primal variable z\n * @param y    Dual variable y\n */\nvoid project_normalcone(OSQPWorkspace *work,\n                        c_float       *z,\n                        c_float       *y);\n\n\n# ifdef __cplusplus\n}\n# endif // ifdef __cplusplus\n\n#endif // ifndef PROJ_H\n"
  },
  {
    "path": "osqp/include/scaling.h",
    "content": "#ifndef SCALING_H\n# define SCALING_H\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif // ifdef __cplusplus\n\n// Functions to scale problem data\n# include \"types.h\"\n# include \"lin_alg.h\"\n# include \"constants.h\"\n\n// Enable data scaling if EMBEDDED is disabled or if EMBEDDED == 2\n# if EMBEDDED != 1\n\n/**\n * Scale problem matrices\n * @param  work Workspace\n * @return      exitflag\n */\nc_int scale_data(OSQPWorkspace *work);\n# endif // if EMBEDDED != 1\n\n\n/**\n * Unscale problem matrices\n * @param  work Workspace\n * @return      exitflag\n */\nc_int unscale_data(OSQPWorkspace *work);\n\n\n/**\n * Unscale solution\n * @param  work Workspace\n * @return      exitflag\n */\nc_int unscale_solution(OSQPWorkspace *work);\n\n# ifdef __cplusplus\n}\n# endif // ifdef __cplusplus\n\n#endif // ifndef SCALING_H\n"
  },
  {
    "path": "osqp/include/types.h",
    "content": "#ifndef OSQP_TYPES_H\n# define OSQP_TYPES_H\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif // ifdef __cplusplus\n\n# include \"glob_opts.h\"\n# include \"constants.h\"\n\n\n/******************\n* Internal types *\n******************/\n\n/**\n *  Matrix in compressed-column form.\n *  The structure is used internally to store matrices in the triplet form as well,\n *  but the API requires that the matrices are in the CSC format.\n */\ntypedef struct {\n  c_int    nzmax; ///< maximum number of entries\n  c_int    m;     ///< number of rows\n  c_int    n;     ///< number of columns\n  c_int   *p;     ///< column pointers (size n+1); col indices (size nzmax) start from 0 when using triplet format (direct KKT matrix formation)\n  c_int   *i;     ///< row indices, size nzmax starting from 0\n  c_float *x;     ///< numerical values, size nzmax\n  c_int    nz;    ///< number of entries in triplet matrix, -1 for csc\n} csc;\n\n/**\n * Linear system solver structure (sublevel objects initialize it differently)\n */\n\ntypedef struct linsys_solver LinSysSolver;\n\n/**\n * OSQP Timer for statistics\n */\ntypedef struct OSQP_TIMER OSQPTimer;\n\n/**\n * Problem scaling matrices stored as vectors\n */\ntypedef struct {\n  c_float  c;    ///< cost function scaling\n  c_float *D;    ///< primal variable scaling\n  c_float *E;    ///< dual variable scaling\n  c_float  cinv; ///< cost function rescaling\n  c_float *Dinv; ///< primal variable rescaling\n  c_float *Einv; ///< dual variable rescaling\n} OSQPScaling;\n\n/**\n * Solution structure\n */\ntypedef struct {\n  c_float *x; ///< primal solution\n  c_float *y; ///< Lagrange multiplier associated to \\f$l <= Ax <= u\\f$\n} OSQPSolution;\n\n\n/**\n * Solver return information\n */\ntypedef struct {\n  c_int iter;          ///< number of iterations taken\n  char  status[32];    ///< status string, e.g. 'solved'\n  c_int status_val;    ///< status as c_int, defined in constants.h\n\n# ifndef EMBEDDED\n  c_int status_polish; ///< polish status: successful (1), unperformed (0), (-1) unsuccessful\n# endif // ifndef EMBEDDED\n\n  c_float obj_val;     ///< primal objective\n  c_float pri_res;     ///< norm of primal residual\n  c_float dua_res;     ///< norm of dual residual\n\n# ifdef PROFILING\n  c_float setup_time;  ///< time taken for setup phase (seconds)\n  c_float solve_time;  ///< time taken for solve phase (seconds)\n  c_float update_time; ///< time taken for update phase (seconds)\n  c_float polish_time; ///< time taken for polish phase (seconds)\n  c_float run_time;    ///< total time  (seconds)\n# endif // ifdef PROFILING\n\n# if EMBEDDED != 1\n  c_int   rho_updates;  ///< number of rho updates\n  c_float rho_estimate; ///< best rho estimate so far from residuals\n# endif // if EMBEDDED != 1\n} OSQPInfo;\n\n\n# ifndef EMBEDDED\n\n/**\n * Polish structure\n */\ntypedef struct {\n  csc *Ared;          ///< active rows of A\n  ///<    Ared = vstack[Alow, Aupp]\n  c_int    n_low;     ///< number of lower-active rows\n  c_int    n_upp;     ///< number of upper-active rows\n  c_int   *A_to_Alow; ///< Maps indices in A to indices in Alow\n  c_int   *A_to_Aupp; ///< Maps indices in A to indices in Aupp\n  c_int   *Alow_to_A; ///< Maps indices in Alow to indices in A\n  c_int   *Aupp_to_A; ///< Maps indices in Aupp to indices in A\n  c_float *x;         ///< optimal x-solution obtained by polish\n  c_float *z;         ///< optimal z-solution obtained by polish\n  c_float *y;         ///< optimal y-solution obtained by polish\n  c_float  obj_val;   ///< objective value at polished solution\n  c_float  pri_res;   ///< primal residual at polished solution\n  c_float  dua_res;   ///< dual residual at polished solution\n} OSQPPolish;\n# endif // ifndef EMBEDDED\n\n\n/**********************************\n* Main structures and Data Types *\n**********************************/\n\n/**\n * Data structure\n */\ntypedef struct {\n  c_int    n; ///< number of variables n\n  c_int    m; ///< number of constraints m\n  csc     *P; ///< the upper triangular part of the quadratic cost matrix P in csc format (size n x n).\n  csc     *A; ///< linear constraints matrix A in csc format (size m x n)\n  c_float *q; ///< dense array for linear part of cost function (size n)\n  c_float *l; ///< dense array for lower bound (size m)\n  c_float *u; ///< dense array for upper bound (size m)\n} OSQPData;\n\n\n/**\n * Settings struct\n */\ntypedef struct {\n  c_float rho;                    ///< ADMM step rho\n  c_float sigma;                  ///< ADMM step sigma\n  c_int   scaling;                ///< heuristic data scaling iterations; if 0, then disabled.\n\n# if EMBEDDED != 1\n  c_int   adaptive_rho;           ///< boolean, is rho step size adaptive?\n  c_int   adaptive_rho_interval;  ///< number of iterations between rho adaptations; if 0, then it is automatic\n  c_float adaptive_rho_tolerance; ///< tolerance X for adapting rho. The new rho has to be X times larger or 1/X times smaller than the current one to trigger a new factorization.\n#  ifdef PROFILING\n  c_float adaptive_rho_fraction;  ///< interval for adapting rho (fraction of the setup time)\n#  endif // Profiling\n# endif // EMBEDDED != 1\n\n  c_int                   max_iter;      ///< maximum number of iterations\n  c_float                 eps_abs;       ///< absolute convergence tolerance\n  c_float                 eps_rel;       ///< relative convergence tolerance\n  c_float                 eps_prim_inf;  ///< primal infeasibility tolerance\n  c_float                 eps_dual_inf;  ///< dual infeasibility tolerance\n  c_float                 alpha;         ///< relaxation parameter\n  enum linsys_solver_type linsys_solver; ///< linear system solver to use\n\n# ifndef EMBEDDED\n  c_float delta;                         ///< regularization parameter for polishing\n  c_int   polish;                        ///< boolean, polish ADMM solution\n  c_int   polish_refine_iter;            ///< number of iterative refinement steps in polishing\n\n  c_int verbose;                         ///< boolean, write out progress\n# endif // ifndef EMBEDDED\n\n  c_int scaled_termination;              ///< boolean, use scaled termination criteria\n  c_int check_termination;               ///< integer, check termination interval; if 0, then termination checking is disabled\n  c_int warm_start;                      ///< boolean, warm start\n\n# ifdef PROFILING\n  c_float time_limit;                    ///< maximum number of seconds allowed to solve the problem; if 0, then disabled\n# endif // ifdef PROFILING\n} OSQPSettings;\n\n\n/**\n * OSQP Workspace\n */\ntypedef struct {\n  /// Problem data to work on (possibly scaled)\n  OSQPData *data;\n\n  /// Linear System solver structure\n  LinSysSolver *linsys_solver;\n\n# ifndef EMBEDDED\n  /// Polish structure\n  OSQPPolish *pol;\n# endif // ifndef EMBEDDED\n\n  /**\n   * @name Vector used to store a vectorized rho parameter\n   * @{\n   */\n  c_float *rho_vec;     ///< vector of rho values\n  c_float *rho_inv_vec; ///< vector of inv rho values\n\n  /** @} */\n\n# if EMBEDDED != 1\n  c_int *constr_type; ///< Type of constraints: loose (-1), equality (1), inequality (0)\n# endif // if EMBEDDED != 1\n\n  /**\n   * @name Iterates\n   * @{\n   */\n  c_float *x;        ///< Iterate x\n  c_float *y;        ///< Iterate y\n  c_float *z;        ///< Iterate z\n  c_float *xz_tilde; ///< Iterate xz_tilde\n\n  c_float *x_prev;   ///< Previous x\n\n  /**< NB: Used also as workspace vector for dual residual */\n  c_float *z_prev;   ///< Previous z\n\n  /**< NB: Used also as workspace vector for primal residual */\n\n  /**\n   * @name Primal and dual residuals workspace variables\n   *\n   * Needed for residuals computation, tolerances computation,\n   * approximate tolerances computation and adapting rho\n   * @{\n   */\n  c_float *Ax;  ///< scaled A * x\n  c_float *Px;  ///< scaled P * x\n  c_float *Aty; ///< scaled A' * y\n\n  /** @} */\n\n  /**\n   * @name Primal infeasibility variables\n   * @{\n   */\n  c_float *delta_y;   ///< difference between consecutive dual iterates\n  c_float *Atdelta_y; ///< A' * delta_y\n\n  /** @} */\n\n  /**\n   * @name Dual infeasibility variables\n   * @{\n   */\n  c_float *delta_x;  ///< difference between consecutive primal iterates\n  c_float *Pdelta_x; ///< P * delta_x\n  c_float *Adelta_x; ///< A * delta_x\n\n  /** @} */\n\n  /**\n   * @name Temporary vectors used in scaling\n   * @{\n   */\n\n  c_float *D_temp;   ///< temporary primal variable scaling vectors\n  c_float *D_temp_A; ///< temporary primal variable scaling vectors storing norms of A columns\n  c_float *E_temp;   ///< temporary constraints scaling vectors storing norms of A' columns\n\n\n  /** @} */\n\n  OSQPSettings *settings; ///< problem settings\n  OSQPScaling  *scaling;  ///< scaling vectors\n  OSQPSolution *solution; ///< problem solution\n  OSQPInfo     *info;     ///< solver information\n\n# ifdef PROFILING\n  OSQPTimer *timer;       ///< timer object\n\n  /// flag indicating whether the solve function has been run before\n  c_int first_run;\n\n  /// flag indicating whether the update_time should be cleared\n  c_int clear_update_time;\n\n  /// flag indicating that osqp_update_rho is called from osqp_solve function\n  c_int rho_update_from_solve;\n# endif // ifdef PROFILING\n\n# ifdef PRINTING\n  c_int summary_printed; ///< Has last summary been printed? (true/false)\n# endif // ifdef PRINTING\n\n} OSQPWorkspace;\n\n\n/**\n * Define linsys_solver prototype structure\n *\n * NB: The details are defined when the linear solver is initialized depending\n *      on the choice\n */\nstruct linsys_solver {\n  enum linsys_solver_type type;                 ///< linear system solver type functions\n  c_int (*solve)(LinSysSolver *self,\n                 c_float      *b);              ///< solve linear system\n\n# ifndef EMBEDDED\n  void (*free)(LinSysSolver *self);             ///< free linear system solver (only in desktop version)\n# endif // ifndef EMBEDDED\n\n# if EMBEDDED != 1\n  c_int (*update_matrices)(LinSysSolver *s,\n                           const csc *P,            ///< update matrices P\n                           const csc *A);           //   and A in the solver\n\n  c_int (*update_rho_vec)(LinSysSolver  *s,\n                          const c_float *rho_vec);  ///< Update rho_vec\n# endif // if EMBEDDED != 1\n\n# ifndef EMBEDDED\n  c_int nthreads; ///< number of threads active\n# endif // ifndef EMBEDDED\n};\n\n\n# ifdef __cplusplus\n}\n# endif // ifdef __cplusplus\n\n#endif // ifndef OSQP_TYPES_H\n"
  },
  {
    "path": "osqp/include/util.h",
    "content": "#ifndef UTIL_H\n# define UTIL_H\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif // ifdef __cplusplus\n\n# include \"types.h\"\n# include \"constants.h\"\n\n/******************\n* Versioning     *\n******************/\n\n/**\n * Return OSQP version\n * @return  OSQP version\n */\nconst char* osqp_version(void);\n\n\n/**********************\n* Utility Functions  *\n**********************/\n\n# ifndef EMBEDDED\n\n/**\n * Copy settings creating a new settings structure (uses MALLOC)\n * @param  settings Settings to be copied\n * @return          New settings structure\n */\nOSQPSettings* copy_settings(const OSQPSettings *settings);\n\n# endif // #ifndef EMBEDDED\n\n/**\n * Custom string copy to avoid string.h library\n * @param dest   destination string\n * @param source source string\n */\nvoid c_strcpy(char       dest[],\n              const char source[]);\n\n\n# ifdef PRINTING\n\n/**\n * Print Header before running the algorithm\n * @param work     osqp workspace\n */\nvoid print_setup_header(const OSQPWorkspace *work);\n\n/**\n * Print header with data to be displayed per iteration\n */\nvoid print_header(void);\n\n/**\n * Print iteration summary\n * @param work current workspace\n */\nvoid print_summary(OSQPWorkspace *work);\n\n/**\n * Print information after polish\n * @param work current workspace\n */\nvoid print_polish(OSQPWorkspace *work);\n\n/**\n * Print footer when algorithm terminates\n * @param info   info structure\n * @param polish is polish enabled?\n */\nvoid print_footer(OSQPInfo *info,\n                  c_int     polish);\n\n\n# endif // ifdef PRINTING\n\n\n/*********************************\n* Timer Structs and Functions * *\n*********************************/\n\n/*! \\cond PRIVATE */\n\n# ifdef PROFILING\n\n// Windows\n#  ifdef IS_WINDOWS\n\n  // Some R packages clash with elements\n  // of the windows.h header, so use a\n  // slimmer version for conflict avoidance\n# ifdef R_LANG\n#define NOGDI\n# endif\n\n#   include <windows.h>\n\nstruct OSQP_TIMER {\n  LARGE_INTEGER tic;\n  LARGE_INTEGER toc;\n  LARGE_INTEGER freq;\n};\n\n// Mac\n#  elif defined IS_MAC\n\n#   include <mach/mach_time.h>\n\n/* Use MAC OSX  mach_time for timing */\nstruct OSQP_TIMER {\n  uint64_t                  tic;\n  uint64_t                  toc;\n  mach_timebase_info_data_t tinfo;\n};\n\n// Linux\n#  else // ifdef IS_WINDOWS\n\n/* Use POSIX clock_gettime() for timing on non-Windows machines */\n#   include <time.h>\n#   include <sys/time.h>\n\n\nstruct OSQP_TIMER {\n  struct timespec tic;\n  struct timespec toc;\n};\n\n#  endif // ifdef IS_WINDOWS\n\n/*! \\endcond */\n\n/**\n * Timer Methods\n */\n\n/**\n * Start timer\n * @param t Timer object\n */\nvoid    osqp_tic(OSQPTimer *t);\n\n/**\n * Report time\n * @param  t Timer object\n * @return   Reported time\n */\nc_float osqp_toc(OSQPTimer *t);\n\n# endif /* END #ifdef PROFILING */\n\n\n/* ================================= DEBUG FUNCTIONS ======================= */\n\n/*! \\cond PRIVATE */\n\n\n# ifndef EMBEDDED\n\n/* Compare CSC matrices */\nc_int is_eq_csc(csc    *A,\n                csc    *B,\n                c_float tol);\n\n/* Convert sparse CSC to dense */\nc_float* csc_to_dns(csc *M);\n\n# endif // #ifndef EMBEDDED\n\n\n# ifdef PRINTING\n#  include <stdio.h>\n\n\n/* Print a csc sparse matrix */\nvoid print_csc_matrix(csc        *M,\n                      const char *name);\n\n/* Dump csc sparse matrix to file */\nvoid dump_csc_matrix(csc        *M,\n                     const char *file_name);\n\n/* Print a triplet format sparse matrix */\nvoid print_trip_matrix(csc        *M,\n                       const char *name);\n\n/* Print a dense matrix */\nvoid print_dns_matrix(c_float    *M,\n                      c_int       m,\n                      c_int       n,\n                      const char *name);\n\n/* Print vector  */\nvoid print_vec(c_float    *v,\n               c_int       n,\n               const char *name);\n\n/* Dump vector to file */\nvoid dump_vec(c_float    *v,\n              c_int       len,\n              const char *file_name);\n\n// Print int array\nvoid print_vec_int(c_int      *x,\n                   c_int       n,\n                   const char *name);\n\n# endif // ifdef PRINTING\n\n/*! \\endcond */\n\n\n# ifdef __cplusplus\n}\n# endif // ifdef __cplusplus\n\n#endif // ifndef UTIL_H\n"
  },
  {
    "path": "osqp/lin_sys/CMakeLists.txt",
    "content": "# Add subdirectory for each linear systems solver\n\nif (NOT DEFINED EMBEDDED)\n# Include this directory for library handler\n# NB Needed for subfolders\ninclude_directories(${CMAKE_CURRENT_SOURCE_DIR})\n\nendif()\n\n# Direct solver\nadd_subdirectory(direct)\n\n# Indirect solver\n# TODO: Add!\n\n# Add linsys handler if not embedded\nif (NOT DEFINED EMBEDDED)\nset(linsys_lib_handler\n    ${CMAKE_CURRENT_SOURCE_DIR}/lib_handler.c\n    ${CMAKE_CURRENT_SOURCE_DIR}/lib_handler.h)\nendif()\n\n\n\n# Combine solvers\n# TODO: Add indirect ones\n# Add library handler if desktop version\nset(linsys_solvers\n    ${direct_linsys_solvers}\n    ${linsys_lib_handler}\n    PARENT_SCOPE)\n\n\n\n# Combine solvers external libraries\nset(linsys_solvers_includes\n    ${direct_linsys_solvers_includes}\n    PARENT_SCOPE)\n"
  },
  {
    "path": "osqp/lin_sys/direct/CMakeLists.txt",
    "content": "# Add direct linear systems solvers\n\n# QDLDL (Default)\n# -------------------------\nadd_subdirectory(qdldl)\n\n# Need to add qdldlobject only here because it cannot be\n# included in another object library such as linsys_qdldl\nset(direct_linsys_solvers $<TARGET_OBJECTS:linsys_qdldl> $<TARGET_OBJECTS:qdldlobject>)\n\n# NB. The second directory is added because we need to include the \"qdldl_types.h\" file in \"qdldl_interface.h\"\nset(direct_linsys_solvers_includes\n\t\"${CMAKE_CURRENT_SOURCE_DIR}/qdldl/\"\n\t\"${CMAKE_CURRENT_SOURCE_DIR}/qdldl/qdldl_sources/include\")\n\n\n# Add other solvers if embedded option is false\nif(NOT DEFINED EMBEDDED)\n\n# MKL Pardiso MKL\n# -----------\n# If MKL Pardiso is enabled, include pardiso directory\nif (ENABLE_MKL_PARDISO)\n\t# Add Pardiso interface\n\tadd_subdirectory(pardiso)\n\n\tadd_library(linsys_pardiso OBJECT ${pardiso_sources})\n\n\t# Add parent directory for library handler\n\ttarget_include_directories(linsys_pardiso PRIVATE  ${pardiso_includes} ${PROJECT_SOURCE_DIR}/include)\n\n\tset(direct_linsys_solvers ${direct_linsys_solvers} $<TARGET_OBJECTS:linsys_pardiso>)\n\n\tset(direct_linsys_solvers_includes \"${direct_linsys_solvers_includes};${CMAKE_CURRENT_SOURCE_DIR}/pardiso/\")\nendif()\n\n\nendif()\n\n# Make direct_linsys_solvers list visible from parent directory\nset(direct_linsys_solvers ${direct_linsys_solvers} PARENT_SCOPE)\n\n# Make external linsys solvers includes visible from parent directory\nset(direct_linsys_solvers_includes ${direct_linsys_solvers_includes} PARENT_SCOPE)\n"
  },
  {
    "path": "osqp/lin_sys/direct/pardiso/CMakeLists.txt",
    "content": "set(pardiso_includes\n    ${CMAKE_CURRENT_SOURCE_DIR}\n    PARENT_SCOPE\n)\n\n# Set source files\nset(\n    pardiso_sources\n    ${CMAKE_CURRENT_SOURCE_DIR}/pardiso_interface.h\n    ${CMAKE_CURRENT_SOURCE_DIR}/pardiso_interface.c\n    ${CMAKE_CURRENT_SOURCE_DIR}/pardiso_loader.h\n    ${CMAKE_CURRENT_SOURCE_DIR}/pardiso_loader.c\n    PARENT_SCOPE\n)\n"
  },
  {
    "path": "osqp/lin_sys/direct/pardiso/pardiso_interface.c",
    "content": "#include \"pardiso_interface.h\"\n\n#define MKL_INT c_int\n\n// Single Dynamic library interface\n#define MKL_INTERFACE_LP64  0x0\n#define MKL_INTERFACE_ILP64 0x1\n\n// Solver Phases\n#define PARDISO_SYMBOLIC  (11)\n#define PARDISO_NUMERIC   (22)\n#define PARDISO_SOLVE     (33)\n#define PARDISO_CLEANUP   (-1)\n\n\n// Prototypes for Pardiso functions\nvoid pardiso(void**,         // pt\n             const c_int*,   // maxfct\n             const c_int*,   // mnum\n             const c_int*,   // mtype\n             const c_int*,   // phase\n             const c_int*,   // n\n             const c_float*, // a\n             const c_int*,   // ia\n             const c_int*,   // ja\n             c_int*,         // perm\n             const c_int*,   //nrhs\n             c_int*,         // iparam\n             const c_int*,   //msglvl\n             c_float*,       // b\n             c_float*,       // x\n             c_int*          // error\n             );\nc_int mkl_set_interface_layer(c_int);\nc_int mkl_get_max_threads();\n\n// Free LDL Factorization structure\nvoid free_linsys_solver_pardiso(pardiso_solver *s) {\n    if (s) {\n\n        // Free pardiso solver using internal function\n        s->phase = PARDISO_CLEANUP;\n        pardiso (s->pt, &(s->maxfct), &(s->mnum), &(s->mtype), &(s->phase),\n                 &(s->nKKT), &(s->fdum), s->KKT_p, s->KKT_i, &(s->idum), &(s->nrhs),\n                 s->iparm, &(s->msglvl), &(s->fdum), &(s->fdum), &(s->error));\n\n      if ( s->error != 0 ){\n#ifdef PRINTING\n          c_eprint(\"Error during MKL Pardiso cleanup: %d\", (int)s->error);\n#endif\n      }\n        // Check each attribute of the structure and free it if it exists\n        if (s->KKT)         csc_spfree(s->KKT);\n        if (s->KKT_i)       c_free(s->KKT_i);\n        if (s->KKT_p)       c_free(s->KKT_p);\n        if (s->bp)          c_free(s->bp);\n        if (s->sol)         c_free(s->sol);\n        if (s->rho_inv_vec) c_free(s->rho_inv_vec);\n\n        // These are required for matrix updates\n        if (s->Pdiag_idx) c_free(s->Pdiag_idx);\n        if (s->PtoKKT)    c_free(s->PtoKKT);\n        if (s->AtoKKT)    c_free(s->AtoKKT);\n        if (s->rhotoKKT)  c_free(s->rhotoKKT);\n\n        c_free(s);\n\n    }\n}\n\n\n// Initialize factorization structure\nc_int init_linsys_solver_pardiso(pardiso_solver ** sp, const csc * P, const csc * A, c_float sigma, const c_float * rho_vec, c_int polish){\n    c_int i;                     // loop counter\n    c_int nnzKKT;                // Number of nonzeros in KKT\n    // Define Variables\n    c_int n_plus_m;              // n_plus_m dimension\n\n\n    // Allocate private structure to store KKT factorization\n    pardiso_solver *s;\n    s = c_calloc(1, sizeof(pardiso_solver));\n    *sp = s;\n\n    // Size of KKT\n    s->n = P->n;\n    s->m = A->m;\n    n_plus_m = s->n + s->m;\n    s->nKKT = n_plus_m;\n\n    // Sigma parameter\n    s->sigma = sigma;\n\n    // Polishing flag\n    s->polish = polish;\n\n    // Link Functions\n    s->solve = &solve_linsys_pardiso;\n    s->free = &free_linsys_solver_pardiso;\n    s->update_matrices = &update_linsys_solver_matrices_pardiso;\n    s->update_rho_vec = &update_linsys_solver_rho_vec_pardiso;\n\n    // Assign type\n    s->type = MKL_PARDISO_SOLVER;\n\n    // Working vector\n    s->bp = (c_float *)c_malloc(sizeof(c_float) * n_plus_m);\n\n    // Solution vector\n    s->sol  = (c_float *)c_malloc(sizeof(c_float) * n_plus_m);\n\n    // Parameter vector\n    s->rho_inv_vec = (c_float *)c_malloc(sizeof(c_float) * n_plus_m);\n\n    // Form KKT matrix\n    if (polish){ // Called from polish()\n        // Use s->rho_inv_vec for storing param2 = vec(delta)\n        for (i = 0; i < A->m; i++){\n            s->rho_inv_vec[i] = sigma;\n        }\n\n        s->KKT = form_KKT(P, A, 1, sigma, s->rho_inv_vec, OSQP_NULL, OSQP_NULL, OSQP_NULL, OSQP_NULL, OSQP_NULL);\n    }\n    else { // Called from ADMM algorithm\n\n        // Allocate vectors of indices\n        s->PtoKKT = c_malloc((P->p[P->n]) * sizeof(c_int));\n        s->AtoKKT = c_malloc((A->p[A->n]) * sizeof(c_int));\n        s->rhotoKKT = c_malloc((A->m) * sizeof(c_int));\n\n        // Use s->rho_inv_vec for storing param2 = rho_inv_vec\n        for (i = 0; i < A->m; i++){\n            s->rho_inv_vec[i] = 1. / rho_vec[i];\n        }\n\n        s->KKT = form_KKT(P, A, 1, sigma, s->rho_inv_vec,\n                             s->PtoKKT, s->AtoKKT,\n                             &(s->Pdiag_idx), &(s->Pdiag_n), s->rhotoKKT);\n    }\n\n    // Check if matrix has been created\n    if (!(s->KKT)) {\n#ifdef PRINTING\n\t    c_eprint(\"Error in forming KKT matrix\");\n#endif\n        free_linsys_solver_pardiso(s);\n        return OSQP_LINSYS_SOLVER_INIT_ERROR;\n    } else {\n\t    // Adjust indexing for Pardiso\n\t    nnzKKT = s->KKT->p[s->KKT->m];\n\t    s->KKT_i = c_malloc((nnzKKT) * sizeof(c_int));\n\t    s->KKT_p = c_malloc((s->KKT->m + 1) * sizeof(c_int));\n\n\t    for(i = 0; i < nnzKKT; i++){\n\t    \ts->KKT_i[i] = s->KKT->i[i] + 1;\n\t    }\n\t    for(i = 0; i < n_plus_m+1; i++){\n\t    \ts->KKT_p[i] = s->KKT->p[i] + 1;\n\t    }\n\n    }\n\n    // Set MKL interface layer (Long integers if activated)\n#ifdef DLONG\n    mkl_set_interface_layer(MKL_INTERFACE_ILP64);\n#else\n    mkl_set_interface_layer(MKL_INTERFACE_LP64);\n#endif\n\n    // Set Pardiso variables\n    s->mtype = -2;        // Real symmetric indefinite matrix\n    s->nrhs = 1;          // Number of right hand sides\n    s->maxfct = 1;        // Maximum number of numerical factorizations\n    s->mnum = 1;          // Which factorization to use\n    s->msglvl = 0;        // Do not print statistical information\n    s->error = 0;         // Initialize error flag\n    for ( i = 0; i < 64; i++ ) {\n        s->iparm[i] = 0;  // Setup Pardiso control parameters\n        s->pt[i] = 0;     // Initialize the internal solver memory pointer\n    }\n    s->iparm[0] = 1;      // No solver default\n    s->iparm[1] = 3;      // Fill-in reordering from OpenMP\n    if (polish) {\n        s->iparm[5] = 1;  // Write solution into b\n    } else {\n        s->iparm[5] = 0;  // Do NOT write solution into b\n    }\n    /* s->iparm[7] = 2;      // Max number of iterative refinement steps */\n    s->iparm[7] = 0;      // Number of iterative refinement steps (auto, performs them only if perturbed pivots are obtained)\n    s->iparm[9] = 13;     // Perturb the pivot elements with 1E-13\n    s->iparm[34] = 0;     // Use Fortran-style indexing for indices\n    /* s->iparm[34] = 1;     // Use C-style indexing for indices */\n\n    // Print number of threads\n    s->nthreads = mkl_get_max_threads();\n\n    // Reordering and symbolic factorization\n    s->phase = PARDISO_SYMBOLIC;\n    pardiso (s->pt, &(s->maxfct), &(s->mnum), &(s->mtype), &(s->phase),\n             &(s->nKKT), s->KKT->x, s->KKT_p, s->KKT_i, &(s->idum), &(s->nrhs),\n             s->iparm, &(s->msglvl), &(s->fdum), &(s->fdum), &(s->error));\n    if ( s->error != 0 ){\n#ifdef PRINTING\n        c_eprint(\"Error during symbolic factorization: %d\", (int)s->error);\n#endif\n        free_linsys_solver_pardiso(s);\n        *sp = OSQP_NULL;\n        return OSQP_LINSYS_SOLVER_INIT_ERROR;\n    }\n\n    // Numerical factorization\n    s->phase = PARDISO_NUMERIC;\n    pardiso (s->pt, &(s->maxfct), &(s->mnum), &(s->mtype), &(s->phase),\n             &(s->nKKT), s->KKT->x, s->KKT_p, s->KKT_i, &(s->idum), &(s->nrhs),\n             s->iparm, &(s->msglvl), &(s->fdum), &(s->fdum), &(s->error));\n    if ( s->error ){\n#ifdef PRINTING\n        c_eprint(\"Error during numerical factorization: %d\", (int)s->error);\n#endif\n        free_linsys_solver_pardiso(s);\n        *sp = OSQP_NULL;\n        return OSQP_LINSYS_SOLVER_INIT_ERROR;\n    }\n\n\n    // No error\n    return 0;\n}\n\n// Returns solution to linear system  Ax = b with solution stored in b\nc_int solve_linsys_pardiso(pardiso_solver * s, c_float * b) {\n    c_int j;\n\n    // Back substitution and iterative refinement\n    s->phase = PARDISO_SOLVE;\n    pardiso (s->pt, &(s->maxfct), &(s->mnum), &(s->mtype), &(s->phase),\n             &(s->nKKT), s->KKT->x, s->KKT_p, s->KKT_i, &(s->idum), &(s->nrhs),\n             s->iparm, &(s->msglvl), b, s->sol, &(s->error));\n    if ( s->error != 0 ){\n#ifdef PRINTING\n        c_eprint(\"Error during linear system solution: %d\", (int)s->error);\n#endif\n        return 1;\n    }\n\n    if (!(s->polish)) {\n        /* copy x_tilde from s->sol */\n        for (j = 0 ; j < s->n ; j++) {\n            b[j] = s->sol[j];\n        }\n\n        /* compute z_tilde from b and s->sol */\n        for (j = 0 ; j < s->m ; j++) {\n            b[j + s->n] += s->rho_inv_vec[j] * s->sol[j + s->n];\n        }\n    }\n\n    return 0;\n}\n\n// Update solver structure with new P and A\nc_int update_linsys_solver_matrices_pardiso(pardiso_solver * s, const csc *P, const csc *A) {\n\n    // Update KKT matrix with new P\n    update_KKT_P(s->KKT, P, s->PtoKKT, s->sigma, s->Pdiag_idx, s->Pdiag_n);\n\n    // Update KKT matrix with new A\n    update_KKT_A(s->KKT, A, s->AtoKKT);\n\n    // Perform numerical factorization\n    s->phase = PARDISO_NUMERIC;\n    pardiso (s->pt, &(s->maxfct), &(s->mnum), &(s->mtype), &(s->phase),\n             &(s->nKKT), s->KKT->x, s->KKT_p, s->KKT_i, &(s->idum), &(s->nrhs),\n             s->iparm, &(s->msglvl), &(s->fdum), &(s->fdum), &(s->error));\n\n    // Return exit flag\n    return s->error;\n}\n\n\nc_int update_linsys_solver_rho_vec_pardiso(pardiso_solver * s, const c_float * rho_vec) {\n    c_int i;\n\n    // Update internal rho_inv_vec\n    for (i = 0; i < s->m; i++){\n        s->rho_inv_vec[i] = 1. / rho_vec[i];\n    }\n\n    // Update KKT matrix with new rho_vec\n    update_KKT_param2(s->KKT, s->rho_inv_vec, s->rhotoKKT, s->m);\n\n    // Perform numerical factorization\n    s->phase = PARDISO_NUMERIC;\n    pardiso (s->pt, &(s->maxfct), &(s->mnum), &(s->mtype), &(s->phase),\n             &(s->nKKT), s->KKT->x, s->KKT_p, s->KKT_i, &(s->idum), &(s->nrhs),\n             s->iparm, &(s->msglvl), &(s->fdum), &(s->fdum), &(s->error));\n\n    // Return exit flag\n    return s->error;\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/pardiso/pardiso_interface.h",
    "content": "#ifndef PARDISO_H\n#define PARDISO_H\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\n#include \"lin_alg.h\"\n#include \"kkt.h\"\n\n/**\n * Pardiso solver structure\n *\n * NB: If we use Pardiso, we suppose that EMBEDDED is not enabled\n */\ntypedef struct pardiso pardiso_solver;\n\nstruct pardiso {\n    enum linsys_solver_type type;\n\n    /**\n     * @name Functions\n     * @{\n     */\n    c_int (*solve)(struct pardiso * self, c_float * b);\n\n    void (*free)(struct pardiso * self); ///< Free workspace (only if desktop)\n\n    c_int (*update_matrices)(struct pardiso * self, const csc *P, const csc *A);    ///< Update solver matrices\n    c_int (*update_rho_vec)(struct pardiso * self, const c_float * rho_vec);        ///< Update rho_vec parameter\n\n    c_int nthreads;\n    /** @} */\n\n\n    /**\n     * @name Attributes\n     * @{\n     */\n    // Attributes\n    csc *KKT;               ///< KKT matrix (in CSR format!)\n    c_int *KKT_i;           ///< KKT column indices in 1-indexing for Pardiso\n    c_int *KKT_p;           ///< KKT row pointers in 1-indexing for Pardiso\n    c_float *bp;            ///< workspace memory for solves (rhs)\n    c_float *sol;           ///< solution to the KKT system\n    c_float *rho_inv_vec;   ///< parameter vector\n    c_float sigma;          ///< scalar parameter\n    c_int polish;           ///< polishing flag\n    c_int n;                ///< number of QP variables\n    c_int m;                ///< number of QP constraints\n\n    // Pardiso variables\n    void *pt[64];     ///< internal solver memory pointer pt\n    c_int iparm[64];  ///< Pardiso control parameters\n    c_int nKKT;       ///< dimension of the linear system\n    c_int mtype;      ///< matrix type (-2 for real and symmetric indefinite)\n    c_int nrhs;       ///< number of right-hand sides (1 for our needs)\n    c_int maxfct;     ///< maximum number of factors (1 for our needs)\n    c_int mnum;       ///< indicates matrix for the solution phase (1 for our needs)\n    c_int phase;      ///< control the execution phases of the solver\n    c_int error;      ///< the error indicator (0 for no error)\n    c_int msglvl;     ///< Message level information (0 for no output)\n    c_int idum;       ///< dummy integer\n    c_float fdum;     ///< dummy float\n\n    // These are required for matrix updates\n    c_int * Pdiag_idx, Pdiag_n;  ///< index and number of diagonal elements in P\n    c_int * PtoKKT, * AtoKKT;    ///< Index of elements from P and A to KKT matrix\n    c_int * rhotoKKT;            ///< Index of rho places in KKT matrix\n\n    /** @} */\n};\n\n\n/**\n * Initialize Pardiso Solver\n *\n * @param  s         Pointer to a private structure\n * @param  P         Cost function matrix (upper triangular form)\n * @param  A         Constraints matrix\n * @param  sigma     Algorithm parameter. If polish, then sigma = delta.\n * @param  rho_vec   Algorithm parameter. If polish, then rho_vec = OSQP_NULL.\n * @param  polish    Flag whether we are initializing for polish or not\n * @return           Exitflag for error (0 if no errors)\n */\nc_int init_linsys_solver_pardiso(pardiso_solver ** sp, const csc * P, const csc * A, c_float sigma, const c_float * rho_vec, c_int polish);\n\n\n/**\n * Solve linear system and store result in b\n * @param  s        Linear system solver structure\n * @param  b        Right-hand side\n * @return          Exitflag\n */\nc_int solve_linsys_pardiso(pardiso_solver * s, c_float * b);\n\n\n/**\n * Update linear system solver matrices\n * @param  s        Linear system solver structure\n * @param  P        Matrix P\n * @param  A        Matrix A\n * @return          Exitflag\n */\nc_int update_linsys_solver_matrices_pardiso(pardiso_solver * s, const csc *P, const csc *A);\n\n\n/**\n * Update rho parameter in linear system solver structure\n * @param  s        Linear system solver structure\n * @param  rho_vec  new rho_vec value\n * @return          exitflag\n */\nc_int update_linsys_solver_rho_vec_pardiso(pardiso_solver * s, const c_float * rho_vec);\n\n\n/**\n * Free linear system solver\n * @param s linear system solver object\n */\nvoid free_linsys_solver_pardiso(pardiso_solver * s);\n\n#ifdef __cplusplus\n}\n#endif\n\n#endif\n"
  },
  {
    "path": "osqp/lin_sys/direct/pardiso/pardiso_loader.c",
    "content": "#include \"lib_handler.h\"\n#include \"pardiso_loader.h\"\n\n#include \"glob_opts.h\"\n#include \"constants.h\"\n\n#ifdef IS_WINDOWS\n#define PARDISOLIBNAME \"mkl_rt.\" SHAREDLIBEXT\n#else\n#define PARDISOLIBNAME \"libmkl_rt.\" SHAREDLIBEXT\n#endif\n\ntypedef void (*voidfun)(void);\n\nvoidfun lh_load_sym (soHandle_t h, const char *symName);\n\n\n// Interfaces for Pardiso functions\ntypedef void (*pardiso_t)(void**, const c_int*, const c_int*, const c_int*,\n                          const c_int*, const c_int*, const c_float*,\n                          const c_int*, const c_int*, c_int*,\n                          const c_int*, c_int*, const c_int*, c_float*,\n                          c_float*, c_int*);\ntypedef int (*mkl_set_ifl_t)(int);\ntypedef int (*mkl_get_mt_t)();\n\n\n// Handlers are static variables\nstatic soHandle_t Pardiso_handle = OSQP_NULL;\nstatic pardiso_t func_pardiso = OSQP_NULL;\nstatic mkl_set_ifl_t func_mkl_set_interface_layer = OSQP_NULL;\nstatic mkl_get_mt_t func_mkl_get_max_threads = OSQP_NULL;\n\n// Wrappers for loaded Pardiso function handlers\nvoid pardiso(void** pt, const c_int* maxfct, const c_int* mnum,\n                  const c_int* mtype, const c_int* phase, const c_int* n,\n                  const c_float* a, const c_int* ia, const c_int* ja,\n                  c_int* perm, const c_int* nrhs, c_int* iparm,\n                  const c_int* msglvl, c_float* b, c_float* x,\n                  c_int* error) {\n\tif(func_pardiso){\n            // Call function pardiso only if it has been initialized\n\t    func_pardiso(pt, maxfct, mnum, mtype, phase, n, a, ia, ja,\n\t\t\t perm, nrhs, iparm, msglvl, b, x, error);\n\t}\n\telse\n\t{\n#ifdef PRINTING\n\t\tc_eprint(\"Pardiso not loaded correctly\");\n#endif\n\t}\n}\n\nc_int mkl_set_interface_layer(c_int code) {\n    return (c_int)func_mkl_set_interface_layer((int)code);\n}\n\nc_int mkl_get_max_threads() {\n    return (c_int)func_mkl_get_max_threads();\n}\n\n\nc_int lh_load_pardiso(const char* libname) {\n    // DEBUG\n    // if (Pardiso_handle) return 0;\n\n    // Load Pardiso library\n    if (libname) {\n        Pardiso_handle = lh_load_lib(libname);\n    } else { /* try a default library name */\n        Pardiso_handle = lh_load_lib(PARDISOLIBNAME);\n    }\n    if (!Pardiso_handle) return 1;\n\n    // Load Pardiso functions\n    func_pardiso = (pardiso_t)lh_load_sym(Pardiso_handle, \"pardiso\");\n    if (!func_pardiso) return 1;\n\n    func_mkl_set_interface_layer = (mkl_set_ifl_t)lh_load_sym(Pardiso_handle,\n                                                    \"MKL_Set_Interface_Layer\");\n    if (!func_mkl_set_interface_layer) return 1;\n\n    func_mkl_get_max_threads = (mkl_get_mt_t)lh_load_sym(Pardiso_handle,\n                                                    \"MKL_Get_Max_Threads\");\n    if (!func_mkl_get_max_threads) return 1;\n\n    return 0;\n}\n\nc_int lh_unload_pardiso() {\n\n    if (Pardiso_handle == OSQP_NULL) return 0;\n\n    return lh_unload_lib(Pardiso_handle);\n\n    /* If multiple OSQP objects are laoded, the lines below cause a crash */\n    // Pardiso_handle = OSQP_NULL;\n    // func_pardiso = OSQP_NULL;\n    // func_mkl_set_interface_layer = OSQP_NULL;\n    // func_mkl_get_max_threads = OSQP_NULL;\n\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/pardiso/pardiso_loader.h",
    "content": "#ifndef PARDISOLOADER_H\n#define PARDISOLOADER_H\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\n\n\n/**\n * Tries to load a shared library with Pardiso.\n * Return a failure if the library cannot be loaded or not all Pardiso symbols are found.\n * @param libname The name under which the Pardiso lib can be found, or OSQP_NULL to use a default name (mkl_rt.SHAREDLIBEXT).\n * @return Zero on success, nonzero on failure.\n */\nc_int lh_load_pardiso(const char* libname);\n\n/**\n * Unloads the loaded Pardiso shared library.\n * @return Zero on success, nonzero on failure.\n */\nc_int lh_unload_pardiso();\n\n\n#ifdef __cplusplus\n}\n#endif\n\n#endif /*PARADISOLOADER_H*/\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/CMakeLists.txt",
    "content": "# Add qdldl\nadd_subdirectory(qdldl_sources)\n\n\nif(NOT DEFINED EMBEDDED)\nset(\n    amd_sources\n    ${CMAKE_CURRENT_SOURCE_DIR}/amd/include/amd_internal.h\n    ${CMAKE_CURRENT_SOURCE_DIR}/amd/include/amd.h\n    ${CMAKE_CURRENT_SOURCE_DIR}/amd/include/SuiteSparse_config.h\n    ${CMAKE_CURRENT_SOURCE_DIR}/amd/src/amd_1.c\n    ${CMAKE_CURRENT_SOURCE_DIR}/amd/src/amd_2.c\n    ${CMAKE_CURRENT_SOURCE_DIR}/amd/src/amd_aat.c\n    ${CMAKE_CURRENT_SOURCE_DIR}/amd/src/amd_control.c\n    ${CMAKE_CURRENT_SOURCE_DIR}/amd/src/amd_defaults.c\n    ${CMAKE_CURRENT_SOURCE_DIR}/amd/src/amd_info.c\n    ${CMAKE_CURRENT_SOURCE_DIR}/amd/src/amd_order.c\n    ${CMAKE_CURRENT_SOURCE_DIR}/amd/src/amd_post_tree.c\n    ${CMAKE_CURRENT_SOURCE_DIR}/amd/src/amd_postorder.c\n    ${CMAKE_CURRENT_SOURCE_DIR}/amd/src/amd_preprocess.c\n    ${CMAKE_CURRENT_SOURCE_DIR}/amd/src/amd_valid.c\n    ${CMAKE_CURRENT_SOURCE_DIR}/amd/src/SuiteSparse_config.c\n)\nendif()\n\n\nset(qdldl_interface_includes\n    ${CMAKE_CURRENT_SOURCE_DIR}\n    ${CMAKE_CURRENT_SOURCE_DIR}/amd/include\n    ${CMAKE_CURRENT_SOURCE_DIR}/qdldl_sources/include\n)\n\nset(qdldl_interface_src\n    ${amd_sources}\n    ${CMAKE_CURRENT_SOURCE_DIR}/qdldl_interface.h\n    ${CMAKE_CURRENT_SOURCE_DIR}/qdldl_interface.c\n)\n\n# Create object library for linear system solver interface\nadd_library(linsys_qdldl OBJECT ${qdldl_interface_src})\ntarget_include_directories(linsys_qdldl PRIVATE ${qdldl_interface_includes} ${PROJECT_SOURCE_DIR}/include)\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/amd/LICENSE",
    "content": "AMD, Copyright (c), 1996-2015, Timothy A. Davis,\nPatrick R. Amestoy, and Iain S. Duff.  All Rights Reserved.\n\nAvailability:\n\n    http://www.suitesparse.com\n\n-------------------------------------------------------------------------------\nAMD License: BSD 3-clause:\n-------------------------------------------------------------------------------\n\n    Redistribution and use in source and binary forms, with or without\n    modification, are permitted provided that the following conditions are met:\n        * Redistributions of source code must retain the above copyright\n          notice, this list of conditions and the following disclaimer.\n        * Redistributions in binary form must reproduce the above copyright\n          notice, this list of conditions and the following disclaimer in the\n          documentation and/or other materials provided with the distribution.\n        * Neither the name of the organizations to which the authors are\n          affiliated, nor the names of its contributors may be used to endorse\n          or promote products derived from this software without specific prior\n          written permission.\n\n    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS BE LIABLE FOR ANY\n    DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n    CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT\n    LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY\n    OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH\n    DAMAGE.\n\n\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/amd/include/SuiteSparse_config.h",
    "content": "/* ========================================================================== */\n/* === SuiteSparse_config =================================================== */\n/* ========================================================================== */\n\n/* Configuration file for SuiteSparse: a Suite of Sparse matrix packages\n * (AMD, COLAMD, CCOLAMD, CAMD, CHOLMOD, UMFPACK, CXSparse, and others).\n *\n * SuiteSparse_config.h provides the definition of the long integer.  On most\n * systems, a C program can be compiled in LP64 mode, in which long's and\n * pointers are both 64-bits, and int's are 32-bits.  Windows 64, however, uses\n * the LLP64 model, in which int's and long's are 32-bits, and long long's and\n * pointers are 64-bits.\n *\n * SuiteSparse packages that include long integer versions are\n * intended for the LP64 mode.  However, as a workaround for Windows 64\n * (and perhaps other systems), the long integer can be redefined.\n *\n * If _WIN64 is defined, then the __int64 type is used instead of long.\n *\n * The long integer can also be defined at compile time.  For example, this\n * could be added to SuiteSparse_config.mk:\n *\n * CFLAGS = -O -D'SuiteSparse_long=long long' \\\n *  -D'SuiteSparse_long_max=9223372036854775801' -D'SuiteSparse_long_idd=\"lld\"'\n *\n * This file defines SuiteSparse_long as either long (on all but _WIN64) or\n * __int64 on Windows 64.  The intent is that a SuiteSparse_long is always a\n * 64-bit integer in a 64-bit code.  ptrdiff_t might be a better choice than\n * long; it is always the same size as a pointer.\n *\n * This file also defines the SUITESPARSE_VERSION and related definitions.\n *\n * Copyright (c) 2012, Timothy A. Davis.  No licensing restrictions apply\n * to this file or to the SuiteSparse_config directory.\n * Author: Timothy A. Davis.\n */\n\n#ifndef SUITESPARSE_CONFIG_H\n#define SUITESPARSE_CONFIG_H\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\n#include \"glob_opts.h\"\n#include <limits.h>\n#include <stdlib.h>\n\n/* ========================================================================== */\n/* === SuiteSparse_long ===================================================== */\n/* ========================================================================== */\n\n#ifndef SuiteSparse_long\n\n#define SuiteSparse_long long long\n#define SuiteSparse_long_max LONG_MAX\n#define SuiteSparse_long_idd \"ld\"\n\n#define SuiteSparse_long_id \"%\" SuiteSparse_long_idd\n#endif\n\n/* ========================================================================== */\n/* === SuiteSparse_config parameters and functions ========================== */\n/* ========================================================================== */\n\n/* SuiteSparse-wide parameters are placed in this struct.  It is meant to be\n   an extern, globally-accessible struct.  It is not meant to be updated\n   frequently by multiple threads.  Rather, if an application needs to modify\n   SuiteSparse_config, it should do it once at the beginning of the application,\n   before multiple threads are launched.\n\n   The intent of these function pointers is that they not be used in your\n   application directly, except to assign them to the desired user-provided\n   functions.  Rather, you should use the\n */\n\nstruct SuiteSparse_config_struct\n{\n    void *(*malloc_func) (size_t) ;             /* pointer to malloc */\n    void *(*realloc_func) (void *, size_t) ;    /* pointer to realloc */\n    void (*free_func) (void *) ;                /* pointer to free */\n#if defined PYTHON\n    void (*printf_func) (const char *, ...) ;    /* pointer to printf (in Python it returns void)*/\n#elif defined R_LANG\n    void (*printf_func) (const char *, ...) ;    /* pointer to printf (in R it returns void)*/\n#else\n    int (*printf_func) (const char *, ...) ;    /* pointer to printf */\n#endif\n    c_float (*hypot_func) (c_float, c_float) ;     /* pointer to hypot */\n    int (*divcomplex_func) (c_float, c_float, c_float, c_float, c_float *, c_float *);\n} ;\n\nextern struct SuiteSparse_config_struct SuiteSparse_config ;\n\nvoid *SuiteSparse_malloc    /* pointer to allocated block of memory */\n(\n    size_t nitems,          /* number of items to malloc (>=1 is enforced) */\n    size_t size_of_item     /* sizeof each item */\n) ;\n\nvoid *SuiteSparse_calloc    /* pointer to allocated block of memory */\n(\n    size_t nitems,          /* number of items to calloc (>=1 is enforced) */\n    size_t size_of_item     /* sizeof each item */\n) ;\n\nvoid *SuiteSparse_realloc   /* pointer to reallocated block of memory, or\n                               to original block if the realloc failed. */\n(\n    size_t nitems_new,      /* new number of items in the object */\n    size_t nitems_old,      /* old number of items in the object */\n    size_t size_of_item,    /* sizeof each item */\n    void *p,                /* old object to reallocate */\n    int *ok                 /* 1 if successful, 0 otherwise */\n) ;\n\nvoid *SuiteSparse_free      /* always returns NULL */\n(\n    void *p                 /* block to free */\n) ;\n\nvoid SuiteSparse_tic    /* start the timer */\n(\n    c_float tic [2]      /* output, contents undefined on input */\n) ;\n\nc_float SuiteSparse_toc  /* return time in seconds since last tic */\n(\n    c_float tic [2]      /* input: from last call to SuiteSparse_tic */\n) ;\n\nc_float SuiteSparse_time  /* returns current wall clock time in seconds */\n(\n    void\n) ;\n\n/* returns sqrt (x^2 + y^2), computed reliably */\nc_float SuiteSparse_hypot (c_float x, c_float y) ;\n\n/* complex division of c = a/b */\nint SuiteSparse_divcomplex\n(\n    c_float ar, c_float ai,\t/* real and imaginary parts of a */\n    c_float br, c_float bi,\t/* real and imaginary parts of b */\n    c_float *cr, c_float *ci\t/* real and imaginary parts of c */\n) ;\n\n/* OSQP: disabling this timing code */\n#define NTIMER\n\n/* determine which timer to use, if any */\n#ifndef NTIMER\n#ifdef _POSIX_C_SOURCE\n#if    _POSIX_C_SOURCE >= 199309L\n#define SUITESPARSE_TIMER_ENABLED\n#endif\n#endif\n#endif\n\n/* SuiteSparse printf macro */\n#define SUITESPARSE_PRINTF(params) \\\n{ \\\n    if (SuiteSparse_config.printf_func != NULL) \\\n    { \\\n        (void) (SuiteSparse_config.printf_func) params ; \\\n    } \\\n}\n\n/* ========================================================================== */\n/* === SuiteSparse version ================================================== */\n/* ========================================================================== */\n\n/* SuiteSparse is not a package itself, but a collection of packages, some of\n * which must be used together (UMFPACK requires AMD, CHOLMOD requires AMD,\n * COLAMD, CAMD, and CCOLAMD, etc).  A version number is provided here for the\n * collection itself.  The versions of packages within each version of\n * SuiteSparse are meant to work together.  Combining one package from one\n * version of SuiteSparse, with another package from another version of\n * SuiteSparse, may or may not work.\n *\n * SuiteSparse contains the following packages:\n *\n *  SuiteSparse_config version 4.5.3 (version always the same as SuiteSparse)\n *  AMD             version 2.4.6\n *  BTF             version 1.2.6\n *  CAMD            version 2.4.6\n *  CCOLAMD         version 2.9.6\n *  CHOLMOD         version 3.0.11\n *  COLAMD          version 2.9.6\n *  CSparse         version 3.1.9\n *  CXSparse        version 3.1.9\n *  GPUQREngine     version 1.0.5\n *  KLU             version 1.3.8\n *  LDL             version 2.2.6\n *  RBio            version 2.2.6\n *  SPQR            version 2.0.7\n *  SuiteSparse_GPURuntime  version 1.0.5\n *  UMFPACK         version 5.7.6\n *  MATLAB_Tools    various packages & M-files\n *  xerbla          version 1.0.3\n *\n * Other package dependencies:\n *  BLAS            required by CHOLMOD and UMFPACK\n *  LAPACK          required by CHOLMOD\n *  METIS 5.1.0     required by CHOLMOD (optional) and KLU (optional)\n *  CUBLAS, CUDART  NVIDIA libraries required by CHOLMOD and SPQR when\n *                  they are compiled with GPU acceleration.\n */\n\nint SuiteSparse_version     /* returns SUITESPARSE_VERSION */\n(\n    /* output, not defined on input.  Not used if NULL.  Returns\n       the three version codes in version [0..2]:\n       version [0] is SUITESPARSE_MAIN_VERSION\n       version [1] is SUITESPARSE_SUB_VERSION\n       version [2] is SUITESPARSE_SUBSUB_VERSION\n       */\n    int version [3]\n) ;\n\n/* Versions prior to 4.2.0 do not have the above function.  The following\n   code fragment will work with any version of SuiteSparse:\n\n   #ifdef SUITESPARSE_HAS_VERSION_FUNCTION\n   v = SuiteSparse_version (NULL) ;\n   #else\n   v = SUITESPARSE_VERSION ;\n   #endif\n*/\n#define SUITESPARSE_HAS_VERSION_FUNCTION\n\n#define SUITESPARSE_DATE \"May 4, 2016\"\n#define SUITESPARSE_VER_CODE(main,sub) ((main) * 1000 + (sub))\n#define SUITESPARSE_MAIN_VERSION 4\n#define SUITESPARSE_SUB_VERSION 5\n#define SUITESPARSE_SUBSUB_VERSION 3\n#define SUITESPARSE_VERSION \\\n    SUITESPARSE_VER_CODE(SUITESPARSE_MAIN_VERSION,SUITESPARSE_SUB_VERSION)\n\n#ifdef __cplusplus\n}\n#endif\n#endif\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/amd/include/amd.h",
    "content": "/* ========================================================================= */\n/* === AMD:  approximate minimum degree ordering =========================== */\n/* ========================================================================= */\n\n/* ------------------------------------------------------------------------- */\n/* AMD Version 2.4, Copyright (c) 1996-2013 by Timothy A. Davis,             */\n/* Patrick R. Amestoy, and Iain S. Duff.  See ../README.txt for License.     */\n/* email: DrTimothyAldenDavis@gmail.com                                      */\n/* ------------------------------------------------------------------------- */\n\n/* AMD finds a symmetric ordering P of a matrix A so that the Cholesky\n * factorization of P*A*P' has fewer nonzeros and takes less work than the\n * Cholesky factorization of A.  If A is not symmetric, then it performs its\n * ordering on the matrix A+A'.  Two sets of user-callable routines are\n * provided, one for int integers and the other for SuiteSparse_long integers.\n *\n * The method is based on the approximate minimum degree algorithm, discussed\n * in Amestoy, Davis, and Duff, \"An approximate degree ordering algorithm\",\n * SIAM Journal of Matrix Analysis and Applications, vol. 17, no. 4, pp.\n * 886-905, 1996.  This package can perform both the AMD ordering (with\n * aggressive absorption), and the AMDBAR ordering (without aggressive\n * absorption) discussed in the above paper.  This package differs from the\n * Fortran codes discussed in the paper:\n *\n *       (1) it can ignore \"dense\" rows and columns, leading to faster run times\n *       (2) it computes the ordering of A+A' if A is not symmetric\n *       (3) it is followed by a depth-first post-ordering of the assembly tree\n *           (or supernodal elimination tree)\n *\n * For historical reasons, the Fortran versions, amd.f and amdbar.f, have\n * been left (nearly) unchanged.  They compute the identical ordering as\n * described in the above paper.\n */\n\n#ifndef AMD_H\n#define AMD_H\n\n/* make it easy for C++ programs to include AMD */\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\n#include \"SuiteSparse_config.h\"\n\n/* get the definition of size_t: */\n#include <stddef.h>\n\n\nint amd_order                  /* returns AMD_OK, AMD_OK_BUT_JUMBLED,\n                                * AMD_INVALID, or AMD_OUT_OF_MEMORY */\n(\n    int n,                     /* A is n-by-n.  n must be >= 0. */\n    const int Ap [ ],          /* column pointers for A, of size n+1 */\n    const int Ai [ ],          /* row indices of A, of size nz = Ap [n] */\n    int P [ ],                 /* output permutation, of size n */\n    c_float Control [ ],        /* input Control settings, of size AMD_CONTROL */\n    c_float Info [ ]            /* output Info statistics, of size AMD_INFO */\n) ;\n\nSuiteSparse_long amd_l_order    /* see above for description of arguments */\n(\n    SuiteSparse_long n,\n    const SuiteSparse_long Ap [ ],\n    const SuiteSparse_long Ai [ ],\n    SuiteSparse_long P [ ],\n    c_float Control [ ],\n    c_float Info [ ]\n) ;\n\n/* Input arguments (not modified):\n *\n *       n: the matrix A is n-by-n.\n *       Ap: an int/SuiteSparse_long array of size n+1, containing column\n *              pointers of A.\n *       Ai: an int/SuiteSparse_long array of size nz, containing the row\n *              indices of A, where nz = Ap [n].\n *       Control:  a c_float array of size AMD_CONTROL, containing control\n *           parameters.  Defaults are used if Control is NULL.\n *\n * Output arguments (not defined on input):\n *\n *       P: an int/SuiteSparse_long array of size n, containing the output\n *           permutation. If row i is the kth pivot row, then P [k] = i.  In\n *           MATLAB notation, the reordered matrix is A (P,P).\n *       Info: a c_float array of size AMD_INFO, containing statistical\n *           information.  Ignored if Info is NULL.\n *\n * On input, the matrix A is stored in column-oriented form.  The row indices\n * of nonzero entries in column j are stored in Ai [Ap [j] ... Ap [j+1]-1].\n *\n * If the row indices appear in ascending order in each column, and there\n * are no duplicate entries, then amd_order is slightly more efficient in\n * terms of time and memory usage.  If this condition does not hold, a copy\n * of the matrix is created (where these conditions do hold), and the copy is\n * ordered.  This feature is new to v2.0 (v1.2 and earlier required this\n * condition to hold for the input matrix).\n *\n * Row indices must be in the range 0 to\n * n-1.  Ap [0] must be zero, and thus nz = Ap [n] is the number of nonzeros\n * in A.  The array Ap is of size n+1, and the array Ai is of size nz = Ap [n].\n * The matrix does not need to be symmetric, and the diagonal does not need to\n * be present (if diagonal entries are present, they are ignored except for\n * the output statistic Info [AMD_NZDIAG]).  The arrays Ai and Ap are not\n * modified.  This form of the Ap and Ai arrays to represent the nonzero\n * pattern of the matrix A is the same as that used internally by MATLAB.\n * If you wish to use a more flexible input structure, please see the\n * umfpack_*_triplet_to_col routines in the UMFPACK package, at\n * http://www.suitesparse.com.\n *\n * Restrictions:  n >= 0.  Ap [0] = 0.  Ap [j] <= Ap [j+1] for all j in the\n *       range 0 to n-1.  nz = Ap [n] >= 0.  Ai [0..nz-1] must be in the range 0\n *       to n-1.  Finally, Ai, Ap, and P must not be NULL.  If any of these\n *       restrictions are not met, AMD returns AMD_INVALID.\n *\n * AMD returns:\n *\n *       AMD_OK if the matrix is valid and sufficient memory can be allocated to\n *           perform the ordering.\n *\n *       AMD_OUT_OF_MEMORY if not enough memory can be allocated.\n *\n *       AMD_INVALID if the input arguments n, Ap, Ai are invalid, or if P is\n *           NULL.\n *\n *       AMD_OK_BUT_JUMBLED if the matrix had unsorted columns, and/or duplicate\n *           entries, but was otherwise valid.\n *\n * The AMD routine first forms the pattern of the matrix A+A', and then\n * computes a fill-reducing ordering, P.  If P [k] = i, then row/column i of\n * the original is the kth pivotal row.  In MATLAB notation, the permuted\n * matrix is A (P,P), except that 0-based indexing is used instead of the\n * 1-based indexing in MATLAB.\n *\n * The Control array is used to set various parameters for AMD.  If a NULL\n * pointer is passed, default values are used.  The Control array is not\n * modified.\n *\n *       Control [AMD_DENSE]:  controls the threshold for \"dense\" rows/columns.\n *           A dense row/column in A+A' can cause AMD to spend a lot of time in\n *           ordering the matrix.  If Control [AMD_DENSE] >= 0, rows/columns\n *           with more than Control [AMD_DENSE] * sqrt (n) entries are ignored\n *           during the ordering, and placed last in the output order.  The\n *           default value of Control [AMD_DENSE] is 10.  If negative, no\n *           rows/columns are treated as \"dense\".  Rows/columns with 16 or\n *           fewer off-diagonal entries are never considered \"dense\".\n *\n *       Control [AMD_AGGRESSIVE]: controls whether or not to use aggressive\n *           absorption, in which a prior element is absorbed into the current\n *           element if is a subset of the current element, even if it is not\n *           adjacent to the current pivot element (refer to Amestoy, Davis,\n *           & Duff, 1996, for more details).  The default value is nonzero,\n *           which means to perform aggressive absorption.  This nearly always\n *           leads to a better ordering (because the approximate degrees are\n *           more accurate) and a lower execution time.  There are cases where\n *           it can lead to a slightly worse ordering, however.  To turn it off,\n *           set Control [AMD_AGGRESSIVE] to 0.\n *\n *       Control [2..4] are not used in the current version, but may be used in\n *           future versions.\n *\n * The Info array provides statistics about the ordering on output.  If it is\n * not present, the statistics are not returned.  This is not an error\n * condition.\n *\n *       Info [AMD_STATUS]:  the return value of AMD, either AMD_OK,\n *           AMD_OK_BUT_JUMBLED, AMD_OUT_OF_MEMORY, or AMD_INVALID.\n *\n *       Info [AMD_N]: n, the size of the input matrix\n *\n *       Info [AMD_NZ]: the number of nonzeros in A, nz = Ap [n]\n *\n *       Info [AMD_SYMMETRY]:  the symmetry of the matrix A.  It is the number\n *           of \"matched\" off-diagonal entries divided by the total number of\n *           off-diagonal entries.  An entry A(i,j) is matched if A(j,i) is also\n *           an entry, for any pair (i,j) for which i != j.  In MATLAB notation,\n *                S = spones (A) ;\n *                B = tril (S, -1) + triu (S, 1) ;\n *                symmetry = nnz (B & B') / nnz (B) ;\n *\n *       Info [AMD_NZDIAG]: the number of entries on the diagonal of A.\n *\n *       Info [AMD_NZ_A_PLUS_AT]:  the number of nonzeros in A+A', excluding the\n *           diagonal.  If A is perfectly symmetric (Info [AMD_SYMMETRY] = 1)\n *           with a fully nonzero diagonal, then Info [AMD_NZ_A_PLUS_AT] = nz-n\n *           (the smallest possible value).  If A is perfectly unsymmetric\n *           (Info [AMD_SYMMETRY] = 0, for an upper triangular matrix, for\n *           example) with no diagonal, then Info [AMD_NZ_A_PLUS_AT] = 2*nz\n *           (the largest possible value).\n *\n *       Info [AMD_NDENSE]: the number of \"dense\" rows/columns of A+A' that were\n *           removed from A prior to ordering.  These are placed last in the\n *           output order P.\n *\n *       Info [AMD_MEMORY]: the amount of memory used by AMD, in bytes.  In the\n *           current version, this is 1.2 * Info  [AMD_NZ_A_PLUS_AT] + 9*n\n *           times the size of an integer.  This is at most 2.4nz + 9n.  This\n *           excludes the size of the input arguments Ai, Ap, and P, which have\n *           a total size of nz + 2*n + 1 integers.\n *\n *       Info [AMD_NCMPA]: the number of garbage collections performed.\n *\n *       Info [AMD_LNZ]: the number of nonzeros in L (excluding the diagonal).\n *           This is a slight upper bound because mass elimination is combined\n *           with the approximate degree update.  It is a rough upper bound if\n *           there are many \"dense\" rows/columns.  The rest of the statistics,\n *           below, are also slight or rough upper bounds, for the same reasons.\n *           The post-ordering of the assembly tree might also not exactly\n *           correspond to a true elimination tree postordering.\n *\n *       Info [AMD_NDIV]: the number of divide operations for a subsequent LDL'\n *           or LU factorization of the permuted matrix A (P,P).\n *\n *       Info [AMD_NMULTSUBS_LDL]:  the number of multiply-subtract pairs for a\n *           subsequent LDL' factorization of A (P,P).\n *\n *       Info [AMD_NMULTSUBS_LU]:  the number of multiply-subtract pairs for a\n *           subsequent LU factorization of A (P,P), assuming that no numerical\n *           pivoting is required.\n *\n *       Info [AMD_DMAX]:  the maximum number of nonzeros in any column of L,\n *           including the diagonal.\n *\n *       Info [14..19] are not used in the current version, but may be used in\n *           future versions.\n */\n\n/* ------------------------------------------------------------------------- */\n/* direct interface to AMD */\n/* ------------------------------------------------------------------------- */\n\n/* amd_2 is the primary AMD ordering routine.  It is not meant to be\n * user-callable because of its restrictive inputs and because it destroys\n * the user's input matrix.  It does not check its inputs for errors, either.\n * However, if you can work with these restrictions it can be faster than\n * amd_order and use less memory (assuming that you can create your own copy\n * of the matrix for AMD to destroy).  Refer to AMD/Source/amd_2.c for a\n * description of each parameter. */\n\nvoid amd_2\n(\n    int n,\n    int Pe [ ],\n    int Iw [ ],\n    int Len [ ],\n    int iwlen,\n    int pfree,\n    int Nv [ ],\n    int Next [ ],\n    int Last [ ],\n    int Head [ ],\n    int Elen [ ],\n    int Degree [ ],\n    int W [ ],\n    c_float Control [ ],\n    c_float Info [ ]\n) ;\n\nvoid amd_l2\n(\n    SuiteSparse_long n,\n    SuiteSparse_long Pe [ ],\n    SuiteSparse_long Iw [ ],\n    SuiteSparse_long Len [ ],\n    SuiteSparse_long iwlen,\n    SuiteSparse_long pfree,\n    SuiteSparse_long Nv [ ],\n    SuiteSparse_long Next [ ],\n    SuiteSparse_long Last [ ],\n    SuiteSparse_long Head [ ],\n    SuiteSparse_long Elen [ ],\n    SuiteSparse_long Degree [ ],\n    SuiteSparse_long W [ ],\n    c_float Control [ ],\n    c_float Info [ ]\n) ;\n\n/* ------------------------------------------------------------------------- */\n/* amd_valid */\n/* ------------------------------------------------------------------------- */\n\n/* Returns AMD_OK or AMD_OK_BUT_JUMBLED if the matrix is valid as input to\n * amd_order; the latter is returned if the matrix has unsorted and/or\n * duplicate row indices in one or more columns.  Returns AMD_INVALID if the\n * matrix cannot be passed to amd_order.  For amd_order, the matrix must also\n * be square.  The first two arguments are the number of rows and the number\n * of columns of the matrix.  For its use in AMD, these must both equal n.\n *\n * NOTE: this routine returned TRUE/FALSE in v1.2 and earlier.\n */\n\nint amd_valid\n(\n    int n_row,                 /* # of rows */\n    int n_col,                 /* # of columns */\n    const int Ap [ ],          /* column pointers, of size n_col+1 */\n    const int Ai [ ]           /* row indices, of size Ap [n_col] */\n) ;\n\nSuiteSparse_long amd_l_valid\n(\n    SuiteSparse_long n_row,\n    SuiteSparse_long n_col,\n    const SuiteSparse_long Ap [ ],\n    const SuiteSparse_long Ai [ ]\n) ;\n\n/* ------------------------------------------------------------------------- */\n/* AMD memory manager and printf routines */\n/* ------------------------------------------------------------------------- */\n\n    /* moved to SuiteSparse_config.c */\n\n/* ------------------------------------------------------------------------- */\n/* AMD Control and Info arrays */\n/* ------------------------------------------------------------------------- */\n\n/* amd_defaults:  sets the default control settings */\nvoid amd_defaults   (c_float Control [ ]) ;\nvoid amd_l_defaults (c_float Control [ ]) ;\n\n/* amd_control: prints the control settings */\nvoid amd_control    (c_float Control [ ]) ;\nvoid amd_l_control  (c_float Control [ ]) ;\n\n/* amd_info: prints the statistics */\nvoid amd_info       (c_float Info [ ]) ;\nvoid amd_l_info     (c_float Info [ ]) ;\n\n#define AMD_CONTROL 5          /* size of Control array */\n#define AMD_INFO 20            /* size of Info array */\n\n/* contents of Control */\n#define AMD_DENSE 0            /* \"dense\" if degree > Control [0] * sqrt (n) */\n#define AMD_AGGRESSIVE 1    /* do aggressive absorption if Control [1] != 0 */\n\n/* default Control settings */\n#define AMD_DEFAULT_DENSE 10.0          /* default \"dense\" degree 10*sqrt(n) */\n#define AMD_DEFAULT_AGGRESSIVE 1    /* do aggressive absorption by default */\n\n/* contents of Info */\n#define AMD_STATUS 0           /* return value of amd_order and amd_l_order */\n#define AMD_N 1                /* A is n-by-n */\n#define AMD_NZ 2      /* number of nonzeros in A */\n#define AMD_SYMMETRY 3         /* symmetry of pattern (1 is sym., 0 is unsym.) */\n#define AMD_NZDIAG 4           /* # of entries on diagonal */\n#define AMD_NZ_A_PLUS_AT 5  /* nz in A+A' */\n#define AMD_NDENSE 6           /* number of \"dense\" rows/columns in A */\n#define AMD_MEMORY 7           /* amount of memory used by AMD */\n#define AMD_NCMPA 8            /* number of garbage collections in AMD */\n#define AMD_LNZ 9     /* approx. nz in L, excluding the diagonal */\n#define AMD_NDIV 10            /* number of fl. point divides for LU and LDL' */\n#define AMD_NMULTSUBS_LDL 11 /* number of fl. point (*,-) pairs for LDL' */\n#define AMD_NMULTSUBS_LU 12  /* number of fl. point (*,-) pairs for LU */\n#define AMD_DMAX 13             /* max nz. in any column of L, incl. diagonal */\n\n/* ------------------------------------------------------------------------- */\n/* return values of AMD */\n/* ------------------------------------------------------------------------- */\n\n#define AMD_OK 0           /* success */\n#define AMD_OUT_OF_MEMORY -1        /* malloc failed, or problem too large */\n#define AMD_INVALID -2              /* input arguments are not valid */\n#define AMD_OK_BUT_JUMBLED 1        /* input matrix is OK for amd_order, but\n    * columns were not sorted, and/or duplicate entries were present.  AMD had\n    * to do extra work before ordering the matrix.  This is a warning, not an\n    * error.  */\n\n/* ========================================================================== */\n/* === AMD version ========================================================== */\n/* ========================================================================== */\n\n/* AMD Version 1.2 and later include the following definitions.\n * As an example, to test if the version you are using is 1.2 or later:\n *\n * #ifdef AMD_VERSION\n *       if (AMD_VERSION >= AMD_VERSION_CODE (1,2)) ...\n * #endif\n *\n * This also works during compile-time:\n *\n *       #if defined(AMD_VERSION) && (AMD_VERSION >= AMD_VERSION_CODE (1,2))\n *           printf (\"This is version 1.2 or later\\n\") ;\n *       #else\n *           printf (\"This is an early version\\n\") ;\n *       #endif\n *\n * Versions 1.1 and earlier of AMD do not include a #define'd version number.\n */\n\n#define AMD_DATE \"May 4, 2016\"\n#define AMD_VERSION_CODE(main,sub) ((main) * 1000 + (sub))\n#define AMD_MAIN_VERSION 2\n#define AMD_SUB_VERSION 4\n#define AMD_SUBSUB_VERSION 6\n#define AMD_VERSION AMD_VERSION_CODE(AMD_MAIN_VERSION,AMD_SUB_VERSION)\n\n#ifdef __cplusplus\n}\n#endif\n\n#endif\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/amd/include/amd_internal.h",
    "content": "/* ========================================================================= */\n/* === amd_internal.h ====================================================== */\n/* ========================================================================= */\n\n/* ------------------------------------------------------------------------- */\n/* AMD, Copyright (c) Timothy A. Davis,                                      */\n/* Patrick R. Amestoy, and Iain S. Duff.  See ../README.txt for License.     */\n/* email: DrTimothyAldenDavis@gmail.com                                      */\n/* ------------------------------------------------------------------------- */\n\n/* This file is for internal use in AMD itself, and does not normally need to\n * be included in user code (it is included in UMFPACK, however).   All others\n * should use amd.h instead.\n */\n\n/* ========================================================================= */\n/* === NDEBUG ============================================================== */\n/* ========================================================================= */\n\n/*\n * Turning on debugging takes some work (see below).   If you do not edit this\n * file, then debugging is always turned off, regardless of whether or not\n * -DNDEBUG is specified in your compiler options.\n *\n * If AMD is being compiled as a mexFunction, then MATLAB is defined,\n * and mxAssert is used instead of assert.  If debugging is not enabled, no\n * MATLAB include files or functions are used.  Thus, the AMD library libamd.a\n * can be safely used in either a stand-alone C program or in another\n * mexFunction, without any change.\n */\n\n/*\n    AMD will be exceedingly slow when running in debug mode.  The next three\n    lines ensure that debugging is turned off.\n*/\n#ifndef NDEBUG\n#define NDEBUG\n#endif\n\n#include \"glob_opts.h\"\n\n/*\n    To enable debugging, uncomment the following line:\n#undef NDEBUG\n*/\n\n/* ------------------------------------------------------------------------- */\n/* ANSI include files */\n/* ------------------------------------------------------------------------- */\n\n/* from stdlib.h:  size_t, malloc, free, realloc, and calloc */\n#include <stdlib.h>\n\n#if !defined(NPRINT) || !defined(NDEBUG)\n/* from stdio.h:  printf.  Not included if NPRINT is defined at compile time.\n * fopen and fscanf are used when debugging. */\n#include <stdio.h>\n#endif\n\n/* from limits.h:  INT_MAX and LONG_MAX */\n#include <limits.h>\n\n/* from math.h: sqrt */\n#include <math.h>\n\n/* ------------------------------------------------------------------------- */\n/* MATLAB include files (only if being used in or via MATLAB) */\n/* ------------------------------------------------------------------------- */\n\n#ifdef MATLAB\n#include \"matrix.h\"\n#include \"mex.h\"\n#endif\n\n/* ------------------------------------------------------------------------- */\n/* basic definitions */\n/* ------------------------------------------------------------------------- */\n\n#ifdef FLIP\n#undef FLIP\n#endif\n\n#ifdef MAX\n#undef MAX\n#endif\n\n#ifdef MIN\n#undef MIN\n#endif\n\n#ifdef EMPTY\n#undef EMPTY\n#endif\n\n#ifdef GLOBAL\n#undef GLOBAL\n#endif\n\n#ifdef PRIVATE\n#undef PRIVATE\n#endif\n\n/* FLIP is a \"negation about -1\", and is used to mark an integer i that is\n * normally non-negative.  FLIP (EMPTY) is EMPTY.  FLIP of a number > EMPTY\n * is negative, and FLIP of a number < EMTPY is positive.  FLIP (FLIP (i)) = i\n * for all integers i.  UNFLIP (i) is >= EMPTY. */\n#define EMPTY (-1)\n#define FLIP(i) (-(i)-2)\n#define UNFLIP(i) ((i < EMPTY) ? FLIP (i) : (i))\n\n/* for integer MAX/MIN, or for c_floats when we don't care how NaN's behave: */\n#define MAX(a,b) (((a) > (b)) ? (a) : (b))\n#define MIN(a,b) (((a) < (b)) ? (a) : (b))\n\n/* logical expression of p implies q: */\n#define IMPLIES(p,q) (!(p) || (q))\n\n/* Note that the IBM RS 6000 xlc predefines TRUE and FALSE in <types.h>. */\n/* The Compaq Alpha also predefines TRUE and FALSE. */\n#ifdef TRUE\n#undef TRUE\n#endif\n#ifdef FALSE\n#undef FALSE\n#endif\n\n#define TRUE (1)\n#define FALSE (0)\n#define PRIVATE static\n#define GLOBAL\n#define EMPTY (-1)\n\n/* Note that Linux's gcc 2.96 defines NULL as ((void *) 0), but other */\n/* compilers (even gcc 2.95.2 on Solaris) define NULL as 0 or (0).  We */\n/* need to use the ANSI standard value of 0. */\n#ifdef NULL\n#undef NULL\n#endif\n\n#define NULL 0\n\n/* largest value of size_t */\n#ifndef SIZE_T_MAX\n#ifdef SIZE_MAX\n/* C99 only */\n#define SIZE_T_MAX SIZE_MAX\n#else\n#define SIZE_T_MAX ((size_t) (-1))\n#endif\n#endif\n\n/* ------------------------------------------------------------------------- */\n/* integer type for AMD: int or SuiteSparse_long */\n/* ------------------------------------------------------------------------- */\n\n#include \"amd.h\"\n\n#if defined (DLONG) || defined (ZLONG)\n\n#define Int SuiteSparse_long\n#define ID  SuiteSparse_long_id\n#define Int_MAX SuiteSparse_long_max\n\n#define AMD_order amd_l_order\n#define AMD_defaults amd_l_defaults\n#define AMD_control amd_l_control\n#define AMD_info amd_l_info\n#define AMD_1 amd_l1\n#define AMD_2 amd_l2\n#define AMD_valid amd_l_valid\n#define AMD_aat amd_l_aat\n#define AMD_postorder amd_l_postorder\n#define AMD_post_tree amd_l_post_tree\n#define AMD_dump amd_l_dump\n#define AMD_debug amd_l_debug\n#define AMD_debug_init amd_l_debug_init\n#define AMD_preprocess amd_l_preprocess\n\n#else\n\n#define Int int\n#define ID \"%d\"\n#define Int_MAX INT_MAX\n\n#define AMD_order amd_order\n#define AMD_defaults amd_defaults\n#define AMD_control amd_control\n#define AMD_info amd_info\n#define AMD_1 amd_1\n#define AMD_2 amd_2\n#define AMD_valid amd_valid\n#define AMD_aat amd_aat\n#define AMD_postorder amd_postorder\n#define AMD_post_tree amd_post_tree\n#define AMD_dump amd_dump\n#define AMD_debug amd_debug\n#define AMD_debug_init amd_debug_init\n#define AMD_preprocess amd_preprocess\n\n#endif\n\n/* ------------------------------------------------------------------------- */\n/* AMD routine definitions (not user-callable) */\n/* ------------------------------------------------------------------------- */\n\nGLOBAL size_t AMD_aat\n(\n    Int n,\n    const Int Ap [ ],\n    const Int Ai [ ],\n    Int Len [ ],\n    Int Tp [ ],\n    c_float Info [ ]\n) ;\n\nGLOBAL void AMD_1\n(\n    Int n,\n    const Int Ap [ ],\n    const Int Ai [ ],\n    Int P [ ],\n    Int Pinv [ ],\n    Int Len [ ],\n    Int slen,\n    Int S [ ],\n    c_float Control [ ],\n    c_float Info [ ]\n) ;\n\nGLOBAL void AMD_postorder\n(\n    Int nn,\n    Int Parent [ ],\n    Int Npiv [ ],\n    Int Fsize [ ],\n    Int Order [ ],\n    Int Child [ ],\n    Int Sibling [ ],\n    Int Stack [ ]\n) ;\n\nGLOBAL Int AMD_post_tree\n(\n    Int root,\n    Int k,\n    Int Child [ ],\n    const Int Sibling [ ],\n    Int Order [ ],\n    Int Stack [ ]\n#ifndef NDEBUG\n    , Int nn\n#endif\n) ;\n\nGLOBAL void AMD_preprocess\n(\n    Int n,\n    const Int Ap [ ],\n    const Int Ai [ ],\n    Int Rp [ ],\n    Int Ri [ ],\n    Int W [ ],\n    Int Flag [ ]\n) ;\n\n/* ------------------------------------------------------------------------- */\n/* debugging definitions */\n/* ------------------------------------------------------------------------- */\n\n#ifndef NDEBUG\n\n/* from assert.h:  assert macro */\n#include <assert.h>\n\n#ifndef EXTERN\n#define EXTERN extern\n#endif\n\nEXTERN Int AMD_debug ;\n\nGLOBAL void AMD_debug_init ( char *s ) ;\n\nGLOBAL void AMD_dump\n(\n    Int n,\n    Int Pe [ ],\n    Int Iw [ ],\n    Int Len [ ],\n    Int iwlen,\n    Int pfree,\n    Int Nv [ ],\n    Int Next [ ],\n    Int Last [ ],\n    Int Head [ ],\n    Int Elen [ ],\n    Int Degree [ ],\n    Int W [ ],\n    Int nel\n) ;\n\n#ifdef ASSERT\n#undef ASSERT\n#endif\n\n/* Use mxAssert if AMD is compiled into a mexFunction */\n#ifdef MATLAB\n#define ASSERT(expression) (mxAssert ((expression), \"\"))\n#else\n#define ASSERT(expression) (assert (expression))\n#endif\n\n#define AMD_DEBUG0(params) { SUITESPARSE_PRINTF (params) ; }\n#define AMD_DEBUG1(params) { if (AMD_debug >= 1) SUITESPARSE_PRINTF (params) ; }\n#define AMD_DEBUG2(params) { if (AMD_debug >= 2) SUITESPARSE_PRINTF (params) ; }\n#define AMD_DEBUG3(params) { if (AMD_debug >= 3) SUITESPARSE_PRINTF (params) ; }\n#define AMD_DEBUG4(params) { if (AMD_debug >= 4) SUITESPARSE_PRINTF (params) ; }\n\n#else\n\n/* no debugging */\n#define ASSERT(expression)\n#define AMD_DEBUG0(params)\n#define AMD_DEBUG1(params)\n#define AMD_DEBUG2(params)\n#define AMD_DEBUG3(params)\n#define AMD_DEBUG4(params)\n\n#endif\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/amd/src/SuiteSparse_config.c",
    "content": "/* ========================================================================== */\n/* === SuiteSparse_config =================================================== */\n/* ========================================================================== */\n\n/* SuiteSparse configuration : memory manager and printf functions. */\n\n/* Copyright (c) 2013, Timothy A. Davis.  No licensing restrictions\n * apply to this file or to the SuiteSparse_config directory.\n * Author: Timothy A. Davis.\n */\n\n// Include OSQP Global options for memory management\n#include \"glob_opts.h\"\n\n#include <math.h>\n#include <stdlib.h>\n\n\n\n#ifndef NPRINT\n#include <stdio.h>\n#endif\n\n#ifdef MATLAB\n#include \"mex.h\"\n#include \"matrix.h\"\n#endif\n\n#ifndef NULL\n#define NULL ((void *) 0)\n#endif\n\n#include \"SuiteSparse_config.h\"\n\n/* -------------------------------------------------------------------------- */\n/* SuiteSparse_config : a global extern struct */\n/* -------------------------------------------------------------------------- */\n\n/* The SuiteSparse_config struct is available to all SuiteSparse functions and\n    to all applications that use those functions.  It must be modified with\n    care, particularly in a multithreaded context.  Normally, the application\n    will initialize this object once, via SuiteSparse_start, possibily followed\n    by application-specific modifications if the applications wants to use\n    alternative memory manager functions.\n\n    The user can redefine these global pointers at run-time to change the\n    memory manager and printf function used by SuiteSparse.\n\n    If -DNMALLOC is defined at compile-time, then no memory-manager is\n    specified.  You must define them at run-time, after calling\n    SuiteSparse_start.\n\n    If -DPRINT is defined a compile time, then printf is disabled, and\n    SuiteSparse will not use printf.\n */\n\nstruct SuiteSparse_config_struct SuiteSparse_config =\n{\n    // Memory allocation from glob_opts.h in OSQP\n    c_malloc, c_realloc, c_free,\n\n    #ifdef PRINTING\n    // Printing function from glop_opts.h in OSQP\n    c_print,\n    #else\n    NULL,\n    #endif\n\n    SuiteSparse_hypot,\n    SuiteSparse_divcomplex\n\n} ;\n\n\n/* -------------------------------------------------------------------------- */\n/* SuiteSparse_malloc: malloc wrapper */\n/* -------------------------------------------------------------------------- */\n\nvoid *SuiteSparse_malloc    /* pointer to allocated block of memory */\n(\n    size_t nitems,          /* number of items to malloc */\n    size_t size_of_item     /* sizeof each item */\n)\n{\n    void *p ;\n    size_t size ;\n    if (nitems < 1) nitems = 1 ;\n    if (size_of_item < 1) size_of_item = 1 ;\n    size = nitems * size_of_item  ;\n\n    if (size != ((c_float) nitems) * size_of_item)\n    {\n        /* size_t overflow */\n        p = NULL ;\n    }\n    else\n    {\n        p = (void *) (SuiteSparse_config.malloc_func) (size) ;\n        // p = (void *) c_malloc(size) ;\n    }\n    return (p) ;\n}\n\n\n\n/* -------------------------------------------------------------------------- */\n/* SuiteSparse_realloc: realloc wrapper */\n/* -------------------------------------------------------------------------- */\n\n/* If p is non-NULL on input, it points to a previously allocated object of\n   size nitems_old * size_of_item.  The object is reallocated to be of size\n   nitems_new * size_of_item.  If p is NULL on input, then a new object of that\n   size is allocated.  On success, a pointer to the new object is returned,\n   and ok is returned as 1.  If the allocation fails, ok is set to 0 and a\n   pointer to the old (unmodified) object is returned.\n */\n\nvoid *SuiteSparse_realloc   /* pointer to reallocated block of memory, or\n                               to original block if the realloc failed. */\n(\n    size_t nitems_new,      /* new number of items in the object */\n    size_t nitems_old,      /* old number of items in the object */\n    size_t size_of_item,    /* sizeof each item */\n    void *p,                /* old object to reallocate */\n    int *ok                 /* 1 if successful, 0 otherwise */\n)\n{\n    size_t size ;\n    if (nitems_old < 1) nitems_old = 1 ;\n    if (nitems_new < 1) nitems_new = 1 ;\n    if (size_of_item < 1) size_of_item = 1 ;\n    size = nitems_new * size_of_item  ;\n\n    if (size != ((c_float) nitems_new) * size_of_item)\n    {\n        /* size_t overflow */\n        (*ok) = 0 ;\n    }\n    else if (p == NULL)\n    {\n        /* a fresh object is being allocated */\n        p = SuiteSparse_malloc (nitems_new, size_of_item) ;\n        (*ok) = (p != NULL) ;\n    }\n    else if (nitems_old == nitems_new)\n    {\n        /* the object does not change; do nothing */\n        (*ok) = 1 ;\n    }\n    else\n    {\n        /* change the size of the object from nitems_old to nitems_new */\n        void *pnew ;\n        // pnew = (void *) (SuiteSparse_config.realloc_func) (p, size) ;\n        pnew = (void *) c_realloc (p, size) ;\n        if (pnew == NULL)\n        {\n            if (nitems_new < nitems_old)\n            {\n                /* the attempt to reduce the size of the block failed, but\n                   the old block is unchanged.  So pretend to succeed. */\n                (*ok) = 1 ;\n            }\n            else\n            {\n                /* out of memory */\n                (*ok) = 0 ;\n            }\n        }\n        else\n        {\n            /* success */\n            p = pnew ;\n            (*ok) = 1 ;\n        }\n    }\n    return (p) ;\n}\n\n/* -------------------------------------------------------------------------- */\n/* SuiteSparse_free: free wrapper */\n/* -------------------------------------------------------------------------- */\n\nvoid *SuiteSparse_free      /* always returns NULL */\n(\n    void *p                 /* block to free */\n)\n{\n    if (p)\n    {\n        (SuiteSparse_config.free_func) (p) ;\n        // c_free(p) ;\n    }\n    return (NULL) ;\n}\n\n\n/* -------------------------------------------------------------------------- */\n/* SuiteSparse_tic: return current wall clock time */\n/* -------------------------------------------------------------------------- */\n\n/* Returns the number of seconds (tic [0]) and nanoseconds (tic [1]) since some\n * unspecified but fixed time in the past.  If no timer is installed, zero is\n * returned.  A scalar c_float precision value for 'tic' could be used, but this\n * might cause loss of precision because clock_getttime returns the time from\n * some distant time in the past.  Thus, an array of size 2 is used.\n *\n * The timer is enabled by default.  To disable the timer, compile with\n * -DNTIMER.  If enabled on a POSIX C 1993 system, the timer requires linking\n * with the -lrt library.\n *\n * example:\n *\n *      c_float tic [2], r, s, t ;\n *      SuiteSparse_tic (tic) ;     // start the timer\n *      // do some work A\n *      t = SuiteSparse_toc (tic) ; // t is time for work A, in seconds\n *      // do some work B\n *      s = SuiteSparse_toc (tic) ; // s is time for work A and B, in seconds\n *      SuiteSparse_tic (tic) ;     // restart the timer\n *      // do some work C\n *      r = SuiteSparse_toc (tic) ; // s is time for work C, in seconds\n *\n * A c_float array of size 2 is used so that this routine can be more easily\n * ported to non-POSIX systems.  The caller does not rely on the POSIX\n * <time.h> include file.\n */\n\n#ifdef SUITESPARSE_TIMER_ENABLED\n\n#include <time.h>\n\nvoid SuiteSparse_tic\n(\n    c_float tic [2]      /* output, contents undefined on input */\n)\n{\n    /* POSIX C 1993 timer, requires -librt */\n    struct timespec t ;\n    clock_gettime (CLOCK_MONOTONIC, &t) ;\n    tic [0] = (c_float) (t.tv_sec) ;\n    tic [1] = (c_float) (t.tv_nsec) ;\n}\n\n#else\n\nvoid SuiteSparse_tic\n(\n    c_float tic [2]      /* output, contents undefined on input */\n)\n{\n    /* no timer installed */\n    tic [0] = 0 ;\n    tic [1] = 0 ;\n}\n\n#endif\n\n\n/* -------------------------------------------------------------------------- */\n/* SuiteSparse_toc: return time since last tic */\n/* -------------------------------------------------------------------------- */\n\n/* Assuming SuiteSparse_tic is accurate to the nanosecond, this function is\n * accurate down to the nanosecond for 2^53 nanoseconds since the last call to\n * SuiteSparse_tic, which is sufficient for SuiteSparse (about 104 days).  If\n * additional accuracy is required, the caller can use two calls to\n * SuiteSparse_tic and do the calculations differently.\n */\n\nc_float SuiteSparse_toc  /* returns time in seconds since last tic */\n(\n    c_float tic [2]  /* input, not modified from last call to SuiteSparse_tic */\n)\n{\n    c_float toc [2] ;\n    SuiteSparse_tic (toc) ;\n    return ((toc [0] - tic [0]) + 1e-9 * (toc [1] - tic [1])) ;\n}\n\n\n/* -------------------------------------------------------------------------- */\n/* SuiteSparse_time: return current wallclock time in seconds */\n/* -------------------------------------------------------------------------- */\n\n/* This function might not be accurate down to the nanosecond. */\n\nc_float SuiteSparse_time  /* returns current wall clock time in seconds */\n(\n    void\n)\n{\n    c_float toc [2] ;\n    SuiteSparse_tic (toc) ;\n    return (toc [0] + 1e-9 * toc [1]) ;\n}\n\n\n/* -------------------------------------------------------------------------- */\n/* SuiteSparse_version: return the current version of SuiteSparse */\n/* -------------------------------------------------------------------------- */\n\nint SuiteSparse_version\n(\n    int version [3]\n)\n{\n    if (version != NULL)\n    {\n        version [0] = SUITESPARSE_MAIN_VERSION ;\n        version [1] = SUITESPARSE_SUB_VERSION ;\n        version [2] = SUITESPARSE_SUBSUB_VERSION ;\n    }\n    return (SUITESPARSE_VERSION) ;\n}\n\n/* -------------------------------------------------------------------------- */\n/* SuiteSparse_hypot */\n/* -------------------------------------------------------------------------- */\n\n/* There is an equivalent routine called hypot in <math.h>, which conforms\n * to ANSI C99.  However, SuiteSparse does not assume that ANSI C99 is\n * available.  You can use the ANSI C99 hypot routine with:\n *\n *      #include <math.h>\n *i     SuiteSparse_config.hypot_func = hypot ;\n *\n * Default value of the SuiteSparse_config.hypot_func pointer is\n * SuiteSparse_hypot, defined below.\n *\n * s = hypot (x,y) computes s = sqrt (x*x + y*y) but does so more accurately.\n * The NaN cases for the c_float relops x >= y and x+y == x are safely ignored.\n *\n * Source: Algorithm 312, \"Absolute value and square root of a complex number,\"\n * P. Friedland, Comm. ACM, vol 10, no 10, October 1967, page 665.\n */\n\nc_float SuiteSparse_hypot (c_float x, c_float y)\n{\n    c_float s, r ;\n    x = fabs (x) ;\n    y = fabs (y) ;\n    if (x >= y)\n    {\n        if (x + y == x)\n        {\n            s = x ;\n        }\n        else\n        {\n            r = y / x ;\n            s = x * sqrt (1.0 + r*r) ;\n        }\n    }\n    else\n    {\n        if (y + x == y)\n        {\n            s = y ;\n        }\n        else\n        {\n            r = x / y ;\n            s = y * sqrt (1.0 + r*r) ;\n        }\n    }\n    return (s) ;\n}\n\n/* -------------------------------------------------------------------------- */\n/* SuiteSparse_divcomplex */\n/* -------------------------------------------------------------------------- */\n\n/* c = a/b where c, a, and b are complex.  The real and imaginary parts are\n * passed as separate arguments to this routine.  The NaN case is ignored\n * for the c_float relop br >= bi.  Returns 1 if the denominator is zero,\n * 0 otherwise.\n *\n * This uses ACM Algo 116, by R. L. Smith, 1962, which tries to avoid\n * underflow and overflow.\n *\n * c can be the same variable as a or b.\n *\n * Default value of the SuiteSparse_config.divcomplex_func pointer is\n * SuiteSparse_divcomplex.\n */\n\nint SuiteSparse_divcomplex\n(\n    c_float ar, c_float ai,       /* real and imaginary parts of a */\n    c_float br, c_float bi,       /* real and imaginary parts of b */\n    c_float *cr, c_float *ci      /* real and imaginary parts of c */\n)\n{\n    c_float tr, ti, r, den ;\n    if (fabs (br) >= fabs (bi))\n    {\n        r = bi / br ;\n        den = br + r * bi ;\n        tr = (ar + ai * r) / den ;\n        ti = (ai - ar * r) / den ;\n    }\n    else\n    {\n        r = br / bi ;\n        den = r * br + bi ;\n        tr = (ar * r + ai) / den ;\n        ti = (ai * r - ar) / den ;\n    }\n    *cr = tr ;\n    *ci = ti ;\n    return (den == 0.) ;\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/amd/src/amd_1.c",
    "content": "/* ========================================================================= */\n/* === AMD_1 =============================================================== */\n/* ========================================================================= */\n\n/* ------------------------------------------------------------------------- */\n/* AMD, Copyright (c) Timothy A. Davis,\t\t\t\t\t     */\n/* Patrick R. Amestoy, and Iain S. Duff.  See ../README.txt for License.     */\n/* email: DrTimothyAldenDavis@gmail.com                                      */\n/* ------------------------------------------------------------------------- */\n\n/* AMD_1: Construct A+A' for a sparse matrix A and perform the AMD ordering.\n *\n * The n-by-n sparse matrix A can be unsymmetric.  It is stored in MATLAB-style\n * compressed-column form, with sorted row indices in each column, and no\n * duplicate entries.  Diagonal entries may be present, but they are ignored.\n * Row indices of column j of A are stored in Ai [Ap [j] ... Ap [j+1]-1].\n * Ap [0] must be zero, and nz = Ap [n] is the number of entries in A.  The\n * size of the matrix, n, must be greater than or equal to zero.\n *\n * This routine must be preceded by a call to AMD_aat, which computes the\n * number of entries in each row/column in A+A', excluding the diagonal.\n * Len [j], on input, is the number of entries in row/column j of A+A'.  This\n * routine constructs the matrix A+A' and then calls AMD_2.  No error checking\n * is performed (this was done in AMD_valid).\n */\n\n#include \"amd_internal.h\"\n\nGLOBAL void AMD_1\n(\n    Int n,\t\t/* n > 0 */\n    const Int Ap [ ],\t/* input of size n+1, not modified */\n    const Int Ai [ ],\t/* input of size nz = Ap [n], not modified */\n    Int P [ ],\t\t/* size n output permutation */\n    Int Pinv [ ],\t/* size n output inverse permutation */\n    Int Len [ ],\t/* size n input, undefined on output */\n    Int slen,\t\t/* slen >= sum (Len [0..n-1]) + 7n,\n\t\t\t * ideally slen = 1.2 * sum (Len) + 8n */\n    Int S [ ],\t\t/* size slen workspace */\n    c_float Control [ ],\t/* input array of size AMD_CONTROL */\n    c_float Info [ ]\t/* output array of size AMD_INFO */\n)\n{\n    Int i, j, k, p, pfree, iwlen, pj, p1, p2, pj2, *Iw, *Pe, *Nv, *Head,\n\t*Elen, *Degree, *s, *W, *Sp, *Tp ;\n\n    /* --------------------------------------------------------------------- */\n    /* construct the matrix for AMD_2 */\n    /* --------------------------------------------------------------------- */\n\n    ASSERT (n > 0) ;\n\n    iwlen = slen - 6*n ;\n    s = S ;\n    Pe = s ;\t    s += n ;\n    Nv = s ;\t    s += n ;\n    Head = s ;\t    s += n ;\n    Elen = s ;\t    s += n ;\n    Degree = s ;    s += n ;\n    W = s ;\t    s += n ;\n    Iw = s ;\t    s += iwlen ;\n\n    ASSERT (AMD_valid (n, n, Ap, Ai) == AMD_OK) ;\n\n    /* construct the pointers for A+A' */\n    Sp = Nv ;\t\t\t/* use Nv and W as workspace for Sp and Tp [ */\n    Tp = W ;\n    pfree = 0 ;\n    for (j = 0 ; j < n ; j++)\n    {\n\tPe [j] = pfree ;\n\tSp [j] = pfree ;\n\tpfree += Len [j] ;\n    }\n\n    /* Note that this restriction on iwlen is slightly more restrictive than\n     * what is strictly required in AMD_2.  AMD_2 can operate with no elbow\n     * room at all, but it will be very slow.  For better performance, at\n     * least size-n elbow room is enforced. */\n    ASSERT (iwlen >= pfree + n) ;\n\n#ifndef NDEBUG\n    for (p = 0 ; p < iwlen ; p++) Iw [p] = EMPTY ;\n#endif\n\n    for (k = 0 ; k < n ; k++)\n    {\n\tAMD_DEBUG1 ((\"Construct row/column k= \"ID\" of A+A'\\n\", k))  ;\n\tp1 = Ap [k] ;\n\tp2 = Ap [k+1] ;\n\n\t/* construct A+A' */\n\tfor (p = p1 ; p < p2 ; )\n\t{\n\t    /* scan the upper triangular part of A */\n\t    j = Ai [p] ;\n\t    ASSERT (j >= 0 && j < n) ;\n\t    if (j < k)\n\t    {\n\t\t/* entry A (j,k) in the strictly upper triangular part */\n\t\tASSERT (Sp [j] < (j == n-1 ? pfree : Pe [j+1])) ;\n\t\tASSERT (Sp [k] < (k == n-1 ? pfree : Pe [k+1])) ;\n\t\tIw [Sp [j]++] = k ;\n\t\tIw [Sp [k]++] = j ;\n\t\tp++ ;\n\t    }\n\t    else if (j == k)\n\t    {\n\t\t/* skip the diagonal */\n\t\tp++ ;\n\t\tbreak ;\n\t    }\n\t    else /* j > k */\n\t    {\n\t\t/* first entry below the diagonal */\n\t\tbreak ;\n\t    }\n\t    /* scan lower triangular part of A, in column j until reaching\n\t     * row k.  Start where last scan left off. */\n\t    ASSERT (Ap [j] <= Tp [j] && Tp [j] <= Ap [j+1]) ;\n\t    pj2 = Ap [j+1] ;\n\t    for (pj = Tp [j] ; pj < pj2 ; )\n\t    {\n\t\ti = Ai [pj] ;\n\t\tASSERT (i >= 0 && i < n) ;\n\t\tif (i < k)\n\t\t{\n\t\t    /* A (i,j) is only in the lower part, not in upper */\n\t\t    ASSERT (Sp [i] < (i == n-1 ? pfree : Pe [i+1])) ;\n\t\t    ASSERT (Sp [j] < (j == n-1 ? pfree : Pe [j+1])) ;\n\t\t    Iw [Sp [i]++] = j ;\n\t\t    Iw [Sp [j]++] = i ;\n\t\t    pj++ ;\n\t\t}\n\t\telse if (i == k)\n\t\t{\n\t\t    /* entry A (k,j) in lower part and A (j,k) in upper */\n\t\t    pj++ ;\n\t\t    break ;\n\t\t}\n\t\telse /* i > k */\n\t\t{\n\t\t    /* consider this entry later, when k advances to i */\n\t\t    break ;\n\t\t}\n\t    }\n\t    Tp [j] = pj ;\n\t}\n\tTp [k] = p ;\n    }\n\n    /* clean up, for remaining mismatched entries */\n    for (j = 0 ; j < n ; j++)\n    {\n\tfor (pj = Tp [j] ; pj < Ap [j+1] ; pj++)\n\t{\n\t    i = Ai [pj] ;\n\t    ASSERT (i >= 0 && i < n) ;\n\t    /* A (i,j) is only in the lower part, not in upper */\n\t    ASSERT (Sp [i] < (i == n-1 ? pfree : Pe [i+1])) ;\n\t    ASSERT (Sp [j] < (j == n-1 ? pfree : Pe [j+1])) ;\n\t    Iw [Sp [i]++] = j ;\n\t    Iw [Sp [j]++] = i ;\n\t}\n    }\n\n#ifndef NDEBUG\n    for (j = 0 ; j < n-1 ; j++) ASSERT (Sp [j] == Pe [j+1]) ;\n    ASSERT (Sp [n-1] == pfree) ;\n#endif\n\n    /* Tp and Sp no longer needed ] */\n\n    /* --------------------------------------------------------------------- */\n    /* order the matrix */\n    /* --------------------------------------------------------------------- */\n\n    AMD_2 (n, Pe, Iw, Len, iwlen, pfree,\n\tNv, Pinv, P, Head, Elen, Degree, W, Control, Info) ;\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/amd/src/amd_2.c",
    "content": "/* ========================================================================= */\n/* === AMD_2 =============================================================== */\n/* ========================================================================= */\n\n/* ------------------------------------------------------------------------- */\n/* AMD, Copyright (c) Timothy A. Davis,\t\t\t\t\t     */\n/* Patrick R. Amestoy, and Iain S. Duff.  See ../README.txt for License.     */\n/* email: DrTimothyAldenDavis@gmail.com                                      */\n/* ------------------------------------------------------------------------- */\n\n/* AMD_2:  performs the AMD ordering on a symmetric sparse matrix A, followed\n * by a postordering (via depth-first search) of the assembly tree using the\n * AMD_postorder routine.\n */\n\n#include \"amd_internal.h\"\n\n/* ========================================================================= */\n/* === clear_flag ========================================================== */\n/* ========================================================================= */\n\nstatic Int clear_flag (Int wflg, Int wbig, Int W [ ], Int n)\n{\n    Int x ;\n    if (wflg < 2 || wflg >= wbig)\n    {\n\tfor (x = 0 ; x < n ; x++)\n\t{\n\t    if (W [x] != 0) W [x] = 1 ;\n\t}\n\twflg = 2 ;\n    }\n    /*  at this point, W [0..n-1] < wflg holds */\n    return (wflg) ;\n}\n\n\n/* ========================================================================= */\n/* === AMD_2 =============================================================== */\n/* ========================================================================= */\n\nGLOBAL void AMD_2\n(\n    Int n,\t\t/* A is n-by-n, where n > 0 */\n    Int Pe [ ],\t\t/* Pe [0..n-1]: index in Iw of row i on input */\n    Int Iw [ ],\t\t/* workspace of size iwlen. Iw [0..pfree-1]\n\t\t\t * holds the matrix on input */\n    Int Len [ ],\t/* Len [0..n-1]: length for row/column i on input */\n    Int iwlen,\t\t/* length of Iw. iwlen >= pfree + n */\n    Int pfree,\t\t/* Iw [pfree ... iwlen-1] is empty on input */\n\n    /* 7 size-n workspaces, not defined on input: */\n    Int Nv [ ],\t\t/* the size of each supernode on output */\n    Int Next [ ],\t/* the output inverse permutation */\n    Int Last [ ],\t/* the output permutation */\n    Int Head [ ],\n    Int Elen [ ],\t/* the size columns of L for each supernode */\n    Int Degree [ ],\n    Int W [ ],\n\n    /* control parameters and output statistics */\n    c_float Control [ ],\t/* array of size AMD_CONTROL */\n    c_float Info [ ]\t/* array of size AMD_INFO */\n)\n{\n\n/*\n * Given a representation of the nonzero pattern of a symmetric matrix, A,\n * (excluding the diagonal) perform an approximate minimum (UMFPACK/MA38-style)\n * degree ordering to compute a pivot order such that the introduction of\n * nonzeros (fill-in) in the Cholesky factors A = LL' is kept low.  At each\n * step, the pivot selected is the one with the minimum UMFAPACK/MA38-style\n * upper-bound on the external degree.  This routine can optionally perform\n * aggresive absorption (as done by MC47B in the Harwell Subroutine\n * Library).\n *\n * The approximate degree algorithm implemented here is the symmetric analog of\n * the degree update algorithm in MA38 and UMFPACK (the Unsymmetric-pattern\n * MultiFrontal PACKage, both by Davis and Duff).  The routine is based on the\n * MA27 minimum degree ordering algorithm by Iain Duff and John Reid.\n *\n * This routine is a translation of the original AMDBAR and MC47B routines,\n * in Fortran, with the following modifications:\n *\n * (1) dense rows/columns are removed prior to ordering the matrix, and placed\n *\tlast in the output order.  The presence of a dense row/column can\n *\tincrease the ordering time by up to O(n^2), unless they are removed\n *\tprior to ordering.\n *\n * (2) the minimum degree ordering is followed by a postordering (depth-first\n *\tsearch) of the assembly tree.  Note that mass elimination (discussed\n *\tbelow) combined with the approximate degree update can lead to the mass\n *\telimination of nodes with lower exact degree than the current pivot\n *\telement.  No additional fill-in is caused in the representation of the\n *\tSchur complement.  The mass-eliminated nodes merge with the current\n *\tpivot element.  They are ordered prior to the current pivot element.\n *\tBecause they can have lower exact degree than the current element, the\n *\tmerger of two or more of these nodes in the current pivot element can\n *\tlead to a single element that is not a \"fundamental supernode\".  The\n *\tdiagonal block can have zeros in it.  Thus, the assembly tree used here\n *\tis not guaranteed to be the precise supernodal elemination tree (with\n *\t\"funadmental\" supernodes), and the postordering performed by this\n *\troutine is not guaranteed to be a precise postordering of the\n *\telimination tree.\n *\n * (3) input parameters are added, to control aggressive absorption and the\n *\tdetection of \"dense\" rows/columns of A.\n *\n * (4) additional statistical information is returned, such as the number of\n *\tnonzeros in L, and the flop counts for subsequent LDL' and LU\n *\tfactorizations.  These are slight upper bounds, because of the mass\n *\telimination issue discussed above.\n *\n * (5) additional routines are added to interface this routine to MATLAB\n *\tto provide a simple C-callable user-interface, to check inputs for\n *\terrors, compute the symmetry of the pattern of A and the number of\n *\tnonzeros in each row/column of A+A', to compute the pattern of A+A',\n *\tto perform the assembly tree postordering, and to provide debugging\n *\touput.  Many of these functions are also provided by the Fortran\n *\tHarwell Subroutine Library routine MC47A.\n *\n * (6) both int and SuiteSparse_long versions are provided.  In the\n *      descriptions below and integer is and int or SuiteSparse_long depending\n *      on which version is being used.\n\n **********************************************************************\n ***** CAUTION:  ARGUMENTS ARE NOT CHECKED FOR ERRORS ON INPUT.  ******\n **********************************************************************\n ** If you want error checking, a more versatile input format, and a **\n ** simpler user interface, use amd_order or amd_l_order instead.    **\n ** This routine is not meant to be user-callable.                   **\n **********************************************************************\n\n * ----------------------------------------------------------------------------\n * References:\n * ----------------------------------------------------------------------------\n *\n *  [1] Timothy A. Davis and Iain Duff, \"An unsymmetric-pattern multifrontal\n *\tmethod for sparse LU factorization\", SIAM J. Matrix Analysis and\n *\tApplications, vol. 18, no. 1, pp. 140-158.  Discusses UMFPACK / MA38,\n *\twhich first introduced the approximate minimum degree used by this\n *\troutine.\n *\n *  [2] Patrick Amestoy, Timothy A. Davis, and Iain S. Duff, \"An approximate\n *\tminimum degree ordering algorithm,\" SIAM J. Matrix Analysis and\n *\tApplications, vol. 17, no. 4, pp. 886-905, 1996.  Discusses AMDBAR and\n *\tMC47B, which are the Fortran versions of this routine.\n *\n *  [3] Alan George and Joseph Liu, \"The evolution of the minimum degree\n *\tordering algorithm,\" SIAM Review, vol. 31, no. 1, pp. 1-19, 1989.\n *\tWe list below the features mentioned in that paper that this code\n *\tincludes:\n *\n *\tmass elimination:\n *\t    Yes.  MA27 relied on supervariable detection for mass elimination.\n *\n *\tindistinguishable nodes:\n *\t    Yes (we call these \"supervariables\").  This was also in the MA27\n *\t    code - although we modified the method of detecting them (the\n *\t    previous hash was the true degree, which we no longer keep track\n *\t    of).  A supervariable is a set of rows with identical nonzero\n *\t    pattern.  All variables in a supervariable are eliminated together.\n *\t    Each supervariable has as its numerical name that of one of its\n *\t    variables (its principal variable).\n *\n *\tquotient graph representation:\n *\t    Yes.  We use the term \"element\" for the cliques formed during\n *\t    elimination.  This was also in the MA27 code.  The algorithm can\n *\t    operate in place, but it will work more efficiently if given some\n *\t    \"elbow room.\"\n *\n *\telement absorption:\n *\t    Yes.  This was also in the MA27 code.\n *\n *\texternal degree:\n *\t    Yes.  The MA27 code was based on the true degree.\n *\n *\tincomplete degree update and multiple elimination:\n *\t    No.  This was not in MA27, either.  Our method of degree update\n *\t    within MC47B is element-based, not variable-based.  It is thus\n *\t    not well-suited for use with incomplete degree update or multiple\n *\t    elimination.\n *\n * Authors, and Copyright (C) 2004 by:\n * Timothy A. Davis, Patrick Amestoy, Iain S. Duff, John K. Reid.\n *\n * Acknowledgements: This work (and the UMFPACK package) was supported by the\n * National Science Foundation (ASC-9111263, DMS-9223088, and CCR-0203270).\n * The UMFPACK/MA38 approximate degree update algorithm, the unsymmetric analog\n * which forms the basis of AMD, was developed while Tim Davis was supported by\n * CERFACS (Toulouse, France) in a post-doctoral position.  This C version, and\n * the etree postorder, were written while Tim Davis was on sabbatical at\n * Stanford University and Lawrence Berkeley National Laboratory.\n\n * ----------------------------------------------------------------------------\n * INPUT ARGUMENTS (unaltered):\n * ----------------------------------------------------------------------------\n\n * n:  The matrix order.  Restriction:  n >= 1.\n *\n * iwlen:  The size of the Iw array.  On input, the matrix is stored in\n *\tIw [0..pfree-1].  However, Iw [0..iwlen-1] should be slightly larger\n *\tthan what is required to hold the matrix, at least iwlen >= pfree + n.\n *\tOtherwise, excessive compressions will take place.  The recommended\n *\tvalue of iwlen is 1.2 * pfree + n, which is the value used in the\n *\tuser-callable interface to this routine (amd_order.c).  The algorithm\n *\twill not run at all if iwlen < pfree.  Restriction: iwlen >= pfree + n.\n *\tNote that this is slightly more restrictive than the actual minimum\n *\t(iwlen >= pfree), but AMD_2 will be very slow with no elbow room.\n *\tThus, this routine enforces a bare minimum elbow room of size n.\n *\n * pfree: On input the tail end of the array, Iw [pfree..iwlen-1], is empty,\n *\tand the matrix is stored in Iw [0..pfree-1].  During execution,\n *\tadditional data is placed in Iw, and pfree is modified so that\n *\tIw [pfree..iwlen-1] is always the unused part of Iw.\n *\n * Control:  A c_float array of size AMD_CONTROL containing input parameters\n *\tthat affect how the ordering is computed.  If NULL, then default\n *\tsettings are used.\n *\n *\tControl [AMD_DENSE] is used to determine whether or not a given input\n *\trow is \"dense\".  A row is \"dense\" if the number of entries in the row\n *\texceeds Control [AMD_DENSE] times sqrt (n), except that rows with 16 or\n *\tfewer entries are never considered \"dense\".  To turn off the detection\n *\tof dense rows, set Control [AMD_DENSE] to a negative number, or to a\n *\tnumber larger than sqrt (n).  The default value of Control [AMD_DENSE]\n *\tis AMD_DEFAULT_DENSE, which is defined in amd.h as 10.\n *\n *\tControl [AMD_AGGRESSIVE] is used to determine whether or not aggressive\n *\tabsorption is to be performed.  If nonzero, then aggressive absorption\n *\tis performed (this is the default).\n\n * ----------------------------------------------------------------------------\n * INPUT/OUPUT ARGUMENTS:\n * ----------------------------------------------------------------------------\n *\n * Pe:  An integer array of size n.  On input, Pe [i] is the index in Iw of\n *\tthe start of row i.  Pe [i] is ignored if row i has no off-diagonal\n *\tentries.  Thus Pe [i] must be in the range 0 to pfree-1 for non-empty\n *\trows.\n *\n *\tDuring execution, it is used for both supervariables and elements:\n *\n *\tPrincipal supervariable i:  index into Iw of the description of\n *\t    supervariable i.  A supervariable represents one or more rows of\n *\t    the matrix with identical nonzero pattern.  In this case,\n *\t    Pe [i] >= 0.\n *\n *\tNon-principal supervariable i:  if i has been absorbed into another\n *\t    supervariable j, then Pe [i] = FLIP (j), where FLIP (j) is defined\n *\t    as (-(j)-2).  Row j has the same pattern as row i.  Note that j\n *\t    might later be absorbed into another supervariable j2, in which\n *\t    case Pe [i] is still FLIP (j), and Pe [j] = FLIP (j2) which is\n *\t    < EMPTY, where EMPTY is defined as (-1) in amd_internal.h.\n *\n *\tUnabsorbed element e:  the index into Iw of the description of element\n *\t    e, if e has not yet been absorbed by a subsequent element.  Element\n *\t    e is created when the supervariable of the same name is selected as\n *\t    the pivot.  In this case, Pe [i] >= 0.\n *\n *\tAbsorbed element e:  if element e is absorbed into element e2, then\n *\t    Pe [e] = FLIP (e2).  This occurs when the pattern of e (which we\n *\t    refer to as Le) is found to be a subset of the pattern of e2 (that\n *\t    is, Le2).  In this case, Pe [i] < EMPTY.  If element e is \"null\"\n *\t    (it has no nonzeros outside its pivot block), then Pe [e] = EMPTY,\n *\t    and e is the root of an assembly subtree (or the whole tree if\n *\t    there is just one such root).\n *\n *\tDense variable i:  if i is \"dense\", then Pe [i] = EMPTY.\n *\n *\tOn output, Pe holds the assembly tree/forest, which implicitly\n *\trepresents a pivot order with identical fill-in as the actual order\n *\t(via a depth-first search of the tree), as follows.  If Nv [i] > 0,\n *\tthen i represents a node in the assembly tree, and the parent of i is\n *\tPe [i], or EMPTY if i is a root.  If Nv [i] = 0, then (i, Pe [i])\n *\trepresents an edge in a subtree, the root of which is a node in the\n *\tassembly tree.  Note that i refers to a row/column in the original\n *\tmatrix, not the permuted matrix.\n *\n * Info:  A c_float array of size AMD_INFO.  If present, (that is, not NULL),\n *\tthen statistics about the ordering are returned in the Info array.\n *\tSee amd.h for a description.\n\n * ----------------------------------------------------------------------------\n * INPUT/MODIFIED (undefined on output):\n * ----------------------------------------------------------------------------\n *\n * Len:  An integer array of size n.  On input, Len [i] holds the number of\n *\tentries in row i of the matrix, excluding the diagonal.  The contents\n *\tof Len are undefined on output.\n *\n * Iw:  An integer array of size iwlen.  On input, Iw [0..pfree-1] holds the\n *\tdescription of each row i in the matrix.  The matrix must be symmetric,\n *\tand both upper and lower triangular parts must be present.  The\n *\tdiagonal must not be present.  Row i is held as follows:\n *\n *\t    Len [i]:  the length of the row i data structure in the Iw array.\n *\t    Iw [Pe [i] ... Pe [i] + Len [i] - 1]:\n *\t\tthe list of column indices for nonzeros in row i (simple\n *\t\tsupervariables), excluding the diagonal.  All supervariables\n *\t\tstart with one row/column each (supervariable i is just row i).\n *\t\tIf Len [i] is zero on input, then Pe [i] is ignored on input.\n *\n *\t    Note that the rows need not be in any particular order, and there\n *\t    may be empty space between the rows.\n *\n *\tDuring execution, the supervariable i experiences fill-in.  This is\n *\trepresented by placing in i a list of the elements that cause fill-in\n *\tin supervariable i:\n *\n *\t    Len [i]:  the length of supervariable i in the Iw array.\n *\t    Iw [Pe [i] ... Pe [i] + Elen [i] - 1]:\n *\t\tthe list of elements that contain i.  This list is kept short\n *\t\tby removing absorbed elements.\n *\t    Iw [Pe [i] + Elen [i] ... Pe [i] + Len [i] - 1]:\n *\t\tthe list of supervariables in i.  This list is kept short by\n *\t\tremoving nonprincipal variables, and any entry j that is also\n *\t\tcontained in at least one of the elements (j in Le) in the list\n *\t\tfor i (e in row i).\n *\n *\tWhen supervariable i is selected as pivot, we create an element e of\n *\tthe same name (e=i):\n *\n *\t    Len [e]:  the length of element e in the Iw array.\n *\t    Iw [Pe [e] ... Pe [e] + Len [e] - 1]:\n *\t\tthe list of supervariables in element e.\n *\n *\tAn element represents the fill-in that occurs when supervariable i is\n *\tselected as pivot (which represents the selection of row i and all\n *\tnon-principal variables whose principal variable is i).  We use the\n *\tterm Le to denote the set of all supervariables in element e.  Absorbed\n *\tsupervariables and elements are pruned from these lists when\n *\tcomputationally convenient.\n *\n *  CAUTION:  THE INPUT MATRIX IS OVERWRITTEN DURING COMPUTATION.\n *  The contents of Iw are undefined on output.\n\n * ----------------------------------------------------------------------------\n * OUTPUT (need not be set on input):\n * ----------------------------------------------------------------------------\n *\n * Nv:  An integer array of size n.  During execution, ABS (Nv [i]) is equal to\n *\tthe number of rows that are represented by the principal supervariable\n *\ti.  If i is a nonprincipal or dense variable, then Nv [i] = 0.\n *\tInitially, Nv [i] = 1 for all i.  Nv [i] < 0 signifies that i is a\n *\tprincipal variable in the pattern Lme of the current pivot element me.\n *\tAfter element me is constructed, Nv [i] is set back to a positive\n *\tvalue.\n *\n *\tOn output, Nv [i] holds the number of pivots represented by super\n *\trow/column i of the original matrix, or Nv [i] = 0 for non-principal\n *\trows/columns.  Note that i refers to a row/column in the original\n *\tmatrix, not the permuted matrix.\n *\n * Elen:  An integer array of size n.  See the description of Iw above.  At the\n *\tstart of execution, Elen [i] is set to zero for all rows i.  During\n *\texecution, Elen [i] is the number of elements in the list for\n *\tsupervariable i.  When e becomes an element, Elen [e] = FLIP (esize) is\n *\tset, where esize is the size of the element (the number of pivots, plus\n *\tthe number of nonpivotal entries).  Thus Elen [e] < EMPTY.\n *\tElen (i) = EMPTY set when variable i becomes nonprincipal.\n *\n *\tFor variables, Elen (i) >= EMPTY holds until just before the\n *\tpostordering and permutation vectors are computed.  For elements,\n *\tElen [e] < EMPTY holds.\n *\n *\tOn output, Elen [i] is the degree of the row/column in the Cholesky\n *\tfactorization of the permuted matrix, corresponding to the original row\n *\ti, if i is a super row/column.  It is equal to EMPTY if i is\n *\tnon-principal.  Note that i refers to a row/column in the original\n *\tmatrix, not the permuted matrix.\n *\n *\tNote that the contents of Elen on output differ from the Fortran\n *\tversion (Elen holds the inverse permutation in the Fortran version,\n *\twhich is instead returned in the Next array in this C version,\n *\tdescribed below).\n *\n * Last: In a degree list, Last [i] is the supervariable preceding i, or EMPTY\n *\tif i is the head of the list.  In a hash bucket, Last [i] is the hash\n *\tkey for i.\n *\n *\tLast [Head [hash]] is also used as the head of a hash bucket if\n *\tHead [hash] contains a degree list (see the description of Head,\n *\tbelow).\n *\n *\tOn output, Last [0..n-1] holds the permutation.  That is, if\n *\ti = Last [k], then row i is the kth pivot row (where k ranges from 0 to\n *\tn-1).  Row Last [k] of A is the kth row in the permuted matrix, PAP'.\n *\n * Next: Next [i] is the supervariable following i in a link list, or EMPTY if\n *\ti is the last in the list.  Used for two kinds of lists:  degree lists\n *\tand hash buckets (a supervariable can be in only one kind of list at a\n *\ttime).\n *\n *\tOn output Next [0..n-1] holds the inverse permutation. \tThat is, if\n *\tk = Next [i], then row i is the kth pivot row. Row i of A appears as\n *\tthe (Next[i])-th row in the permuted matrix, PAP'.\n *\n *\tNote that the contents of Next on output differ from the Fortran\n *\tversion (Next is undefined on output in the Fortran version).\n\n * ----------------------------------------------------------------------------\n * LOCAL WORKSPACE (not input or output - used only during execution):\n * ----------------------------------------------------------------------------\n *\n * Degree:  An integer array of size n.  If i is a supervariable, then\n *\tDegree [i] holds the current approximation of the external degree of\n *\trow i (an upper bound).  The external degree is the number of nonzeros\n *\tin row i, minus ABS (Nv [i]), the diagonal part.  The bound is equal to\n *\tthe exact external degree if Elen [i] is less than or equal to two.\n *\n *\tWe also use the term \"external degree\" for elements e to refer to\n *\t|Le \\ Lme|.  If e is an element, then Degree [e] is |Le|, which is the\n *\tdegree of the off-diagonal part of the element e (not including the\n *\tdiagonal part).\n *\n * Head:   An integer array of size n.  Head is used for degree lists.\n *\tHead [deg] is the first supervariable in a degree list.  All\n *\tsupervariables i in a degree list Head [deg] have the same approximate\n *\tdegree, namely, deg = Degree [i].  If the list Head [deg] is empty then\n *\tHead [deg] = EMPTY.\n *\n *\tDuring supervariable detection Head [hash] also serves as a pointer to\n *\ta hash bucket.  If Head [hash] >= 0, there is a degree list of degree\n *\thash.  The hash bucket head pointer is Last [Head [hash]].  If\n *\tHead [hash] = EMPTY, then the degree list and hash bucket are both\n *\tempty.  If Head [hash] < EMPTY, then the degree list is empty, and\n *\tFLIP (Head [hash]) is the head of the hash bucket.  After supervariable\n *\tdetection is complete, all hash buckets are empty, and the\n *\t(Last [Head [hash]] = EMPTY) condition is restored for the non-empty\n *\tdegree lists.\n *\n * W:  An integer array of size n.  The flag array W determines the status of\n *\telements and variables, and the external degree of elements.\n *\n *\tfor elements:\n *\t    if W [e] = 0, then the element e is absorbed.\n *\t    if W [e] >= wflg, then W [e] - wflg is the size of the set\n *\t\t|Le \\ Lme|, in terms of nonzeros (the sum of ABS (Nv [i]) for\n *\t\teach principal variable i that is both in the pattern of\n *\t\telement e and NOT in the pattern of the current pivot element,\n *\t\tme).\n *\t    if wflg > W [e] > 0, then e is not absorbed and has not yet been\n *\t\tseen in the scan of the element lists in the computation of\n *\t\t|Le\\Lme| in Scan 1 below.\n *\n *\tfor variables:\n *\t    during supervariable detection, if W [j] != wflg then j is\n *\t    not in the pattern of variable i.\n *\n *\tThe W array is initialized by setting W [i] = 1 for all i, and by\n *\tsetting wflg = 2.  It is reinitialized if wflg becomes too large (to\n *\tensure that wflg+n does not cause integer overflow).\n\n * ----------------------------------------------------------------------------\n * LOCAL INTEGERS:\n * ----------------------------------------------------------------------------\n */\n\n    Int deg, degme, dext, lemax, e, elenme, eln, i, ilast, inext, j,\n\tjlast, jnext, k, knt1, knt2, knt3, lenj, ln, me, mindeg, nel, nleft,\n\tnvi, nvj, nvpiv, slenme, wbig, we, wflg, wnvi, ok, ndense, ncmpa,\n\tdense, aggressive ;\n\n    unsigned Int hash ;\t    /* unsigned, so that hash % n is well defined.*/\n\n/*\n * deg:\t\tthe degree of a variable or element\n * degme:\tsize, |Lme|, of the current element, me (= Degree [me])\n * dext:\texternal degree, |Le \\ Lme|, of some element e\n * lemax:\tlargest |Le| seen so far (called dmax in Fortran version)\n * e:\t\tan element\n * elenme:\tthe length, Elen [me], of element list of pivotal variable\n * eln:\t\tthe length, Elen [...], of an element list\n * hash:\tthe computed value of the hash function\n * i:\t\ta supervariable\n * ilast:\tthe entry in a link list preceding i\n * inext:\tthe entry in a link list following i\n * j:\t\ta supervariable\n * jlast:\tthe entry in a link list preceding j\n * jnext:\tthe entry in a link list, or path, following j\n * k:\t\tthe pivot order of an element or variable\n * knt1:\tloop counter used during element construction\n * knt2:\tloop counter used during element construction\n * knt3:\tloop counter used during compression\n * lenj:\tLen [j]\n * ln:\t\tlength of a supervariable list\n * me:\t\tcurrent supervariable being eliminated, and the current\n *\t\t    element created by eliminating that supervariable\n * mindeg:\tcurrent minimum degree\n * nel:\t\tnumber of pivots selected so far\n * nleft:\tn - nel, the number of nonpivotal rows/columns remaining\n * nvi:\t\tthe number of variables in a supervariable i (= Nv [i])\n * nvj:\t\tthe number of variables in a supervariable j (= Nv [j])\n * nvpiv:\tnumber of pivots in current element\n * slenme:\tnumber of variables in variable list of pivotal variable\n * wbig:\t= (INT_MAX - n) for the int version, (SuiteSparse_long_max - n)\n *                  for the SuiteSparse_long version.  wflg is not allowed to\n *                  be >= wbig.\n * we:\t\tW [e]\n * wflg:\tused for flagging the W array.  See description of Iw.\n * wnvi:\twflg - Nv [i]\n * x:\t\teither a supervariable or an element\n *\n * ok:\t\ttrue if supervariable j can be absorbed into i\n * ndense:\tnumber of \"dense\" rows/columns\n * dense:\trows/columns with initial degree > dense are considered \"dense\"\n * aggressive:\ttrue if aggressive absorption is being performed\n * ncmpa:\tnumber of garbage collections\n\n * ----------------------------------------------------------------------------\n * LOCAL DOUBLES, used for statistical output only (except for alpha):\n * ----------------------------------------------------------------------------\n */\n\n    c_float f, r, ndiv, s, nms_lu, nms_ldl, dmax, alpha, lnz, lnzme ;\n\n/*\n * f:\t\tnvpiv\n * r:\t\tdegme + nvpiv\n * ndiv:\tnumber of divisions for LU or LDL' factorizations\n * s:\t\tnumber of multiply-subtract pairs for LU factorization, for the\n *\t\t    current element me\n * nms_lu\tnumber of multiply-subtract pairs for LU factorization\n * nms_ldl\tnumber of multiply-subtract pairs for LDL' factorization\n * dmax:\tthe largest number of entries in any column of L, including the\n *\t\t    diagonal\n * alpha:\t\"dense\" degree ratio\n * lnz:\t\tthe number of nonzeros in L (excluding the diagonal)\n * lnzme:\tthe number of nonzeros in L (excl. the diagonal) for the\n *\t\t    current element me\n\n * ----------------------------------------------------------------------------\n * LOCAL \"POINTERS\" (indices into the Iw array)\n * ----------------------------------------------------------------------------\n*/\n\n    Int p, p1, p2, p3, p4, pdst, pend, pj, pme, pme1, pme2, pn, psrc ;\n\n/*\n * Any parameter (Pe [...] or pfree) or local variable starting with \"p\" (for\n * Pointer) is an index into Iw, and all indices into Iw use variables starting\n * with \"p.\"  The only exception to this rule is the iwlen input argument.\n *\n * p:           pointer into lots of things\n * p1:          Pe [i] for some variable i (start of element list)\n * p2:          Pe [i] + Elen [i] -  1 for some variable i\n * p3:          index of first supervariable in clean list\n * p4:\n * pdst:        destination pointer, for compression\n * pend:        end of memory to compress\n * pj:          pointer into an element or variable\n * pme:         pointer into the current element (pme1...pme2)\n * pme1:        the current element, me, is stored in Iw [pme1...pme2]\n * pme2:        the end of the current element\n * pn:          pointer into a \"clean\" variable, also used to compress\n * psrc:        source pointer, for compression\n*/\n\n/* ========================================================================= */\n/*  INITIALIZATIONS */\n/* ========================================================================= */\n\n    /* Note that this restriction on iwlen is slightly more restrictive than\n     * what is actually required in AMD_2.  AMD_2 can operate with no elbow\n     * room at all, but it will be slow.  For better performance, at least\n     * size-n elbow room is enforced. */\n    ASSERT (iwlen >= pfree + n) ;\n    ASSERT (n > 0) ;\n\n    /* initialize output statistics */\n    lnz = 0 ;\n    ndiv = 0 ;\n    nms_lu = 0 ;\n    nms_ldl = 0 ;\n    dmax = 1 ;\n    me = EMPTY ;\n\n    mindeg = 0 ;\n    ncmpa = 0 ;\n    nel = 0 ;\n    lemax = 0 ;\n\n    /* get control parameters */\n    if (Control != (c_float *) NULL)\n    {\n\talpha = Control [AMD_DENSE] ;\n\taggressive = (Control [AMD_AGGRESSIVE] != 0) ;\n    }\n    else\n    {\n\talpha = AMD_DEFAULT_DENSE ;\n\taggressive = AMD_DEFAULT_AGGRESSIVE ;\n    }\n    /* Note: if alpha is NaN, this is undefined: */\n    if (alpha < 0)\n    {\n\t/* only remove completely dense rows/columns */\n\tdense = n-2 ;\n    }\n    else\n    {\n\tdense = (Int) (alpha * sqrt ((c_float) n)) ;\n    }\n    dense = MAX (16, dense) ;\n    dense = MIN (n,  dense) ;\n    AMD_DEBUG1 ((\"\\n\\nAMD (debug), alpha %g, aggr. \"ID\"\\n\",\n\talpha, aggressive)) ;\n\n    for (i = 0 ; i < n ; i++)\n    {\n\tLast [i] = EMPTY ;\n\tHead [i] = EMPTY ;\n\tNext [i] = EMPTY ;\n\t/* if separate Hhead array is used for hash buckets: *\n\tHhead [i] = EMPTY ;\n\t*/\n\tNv [i] = 1 ;\n\tW [i] = 1 ;\n\tElen [i] = 0 ;\n\tDegree [i] = Len [i] ;\n    }\n\n#ifndef NDEBUG\n    AMD_DEBUG1 ((\"\\n======Nel \"ID\" initial\\n\", nel)) ;\n    AMD_dump (n, Pe, Iw, Len, iwlen, pfree, Nv, Next, Last,\n\t\tHead, Elen, Degree, W, -1) ;\n#endif\n\n    /* initialize wflg */\n    wbig = Int_MAX - n ;\n    wflg = clear_flag (0, wbig, W, n) ;\n\n    /* --------------------------------------------------------------------- */\n    /* initialize degree lists and eliminate dense and empty rows */\n    /* --------------------------------------------------------------------- */\n\n    ndense = 0 ;\n\n    for (i = 0 ; i < n ; i++)\n    {\n\tdeg = Degree [i] ;\n\tASSERT (deg >= 0 && deg < n) ;\n\tif (deg == 0)\n\t{\n\n\t    /* -------------------------------------------------------------\n\t     * we have a variable that can be eliminated at once because\n\t     * there is no off-diagonal non-zero in its row.  Note that\n\t     * Nv [i] = 1 for an empty variable i.  It is treated just\n\t     * the same as an eliminated element i.\n\t     * ------------------------------------------------------------- */\n\n\t    Elen [i] = FLIP (1) ;\n\t    nel++ ;\n\t    Pe [i] = EMPTY ;\n\t    W [i] = 0 ;\n\n\t}\n\telse if (deg > dense)\n\t{\n\n\t    /* -------------------------------------------------------------\n\t     * Dense variables are not treated as elements, but as unordered,\n\t     * non-principal variables that have no parent.  They do not take\n\t     * part in the postorder, since Nv [i] = 0.  Note that the Fortran\n\t     * version does not have this option.\n\t     * ------------------------------------------------------------- */\n\n\t    AMD_DEBUG1 ((\"Dense node \"ID\" degree \"ID\"\\n\", i, deg)) ;\n\t    ndense++ ;\n\t    Nv [i] = 0 ;\t\t/* do not postorder this node */\n\t    Elen [i] = EMPTY ;\n\t    nel++ ;\n\t    Pe [i] = EMPTY ;\n\n\t}\n\telse\n\t{\n\n\t    /* -------------------------------------------------------------\n\t     * place i in the degree list corresponding to its degree\n\t     * ------------------------------------------------------------- */\n\n\t    inext = Head [deg] ;\n\t    ASSERT (inext >= EMPTY && inext < n) ;\n\t    if (inext != EMPTY) Last [inext] = i ;\n\t    Next [i] = inext ;\n\t    Head [deg] = i ;\n\n\t}\n    }\n\n/* ========================================================================= */\n/* WHILE (selecting pivots) DO */\n/* ========================================================================= */\n\n    while (nel < n)\n    {\n\n#ifndef NDEBUG\n\tAMD_DEBUG1 ((\"\\n======Nel \"ID\"\\n\", nel)) ;\n\tif (AMD_debug >= 2)\n\t{\n\t    AMD_dump (n, Pe, Iw, Len, iwlen, pfree, Nv, Next,\n\t\t    Last, Head, Elen, Degree, W, nel) ;\n\t}\n#endif\n\n/* ========================================================================= */\n/* GET PIVOT OF MINIMUM DEGREE */\n/* ========================================================================= */\n\n\t/* ----------------------------------------------------------------- */\n\t/* find next supervariable for elimination */\n\t/* ----------------------------------------------------------------- */\n\n\tASSERT (mindeg >= 0 && mindeg < n) ;\n\tfor (deg = mindeg ; deg < n ; deg++)\n\t{\n\t    me = Head [deg] ;\n\t    if (me != EMPTY) break ;\n\t}\n\tmindeg = deg ;\n\tASSERT (me >= 0 && me < n) ;\n\tAMD_DEBUG1 ((\"=================me: \"ID\"\\n\", me)) ;\n\n\t/* ----------------------------------------------------------------- */\n\t/* remove chosen variable from link list */\n\t/* ----------------------------------------------------------------- */\n\n\tinext = Next [me] ;\n\tASSERT (inext >= EMPTY && inext < n) ;\n\tif (inext != EMPTY) Last [inext] = EMPTY ;\n\tHead [deg] = inext ;\n\n\t/* ----------------------------------------------------------------- */\n\t/* me represents the elimination of pivots nel to nel+Nv[me]-1. */\n\t/* place me itself as the first in this set. */\n\t/* ----------------------------------------------------------------- */\n\n\telenme = Elen [me] ;\n\tnvpiv = Nv [me] ;\n\tASSERT (nvpiv > 0) ;\n\tnel += nvpiv ;\n\n/* ========================================================================= */\n/* CONSTRUCT NEW ELEMENT */\n/* ========================================================================= */\n\n\t/* -----------------------------------------------------------------\n\t * At this point, me is the pivotal supervariable.  It will be\n\t * converted into the current element.  Scan list of the pivotal\n\t * supervariable, me, setting tree pointers and constructing new list\n\t * of supervariables for the new element, me.  p is a pointer to the\n\t * current position in the old list.\n\t * ----------------------------------------------------------------- */\n\n\t/* flag the variable \"me\" as being in Lme by negating Nv [me] */\n\tNv [me] = -nvpiv ;\n\tdegme = 0 ;\n\tASSERT (Pe [me] >= 0 && Pe [me] < iwlen) ;\n\n\tif (elenme == 0)\n\t{\n\n\t    /* ------------------------------------------------------------- */\n\t    /* construct the new element in place */\n\t    /* ------------------------------------------------------------- */\n\n\t    pme1 = Pe [me] ;\n\t    pme2 = pme1 - 1 ;\n\n\t    for (p = pme1 ; p <= pme1 + Len [me] - 1 ; p++)\n\t    {\n\t\ti = Iw [p] ;\n\t\tASSERT (i >= 0 && i < n && Nv [i] >= 0) ;\n\t\tnvi = Nv [i] ;\n\t\tif (nvi > 0)\n\t\t{\n\n\t\t    /* ----------------------------------------------------- */\n\t\t    /* i is a principal variable not yet placed in Lme. */\n\t\t    /* store i in new list */\n\t\t    /* ----------------------------------------------------- */\n\n\t\t    /* flag i as being in Lme by negating Nv [i] */\n\t\t    degme += nvi ;\n\t\t    Nv [i] = -nvi ;\n\t\t    Iw [++pme2] = i ;\n\n\t\t    /* ----------------------------------------------------- */\n\t\t    /* remove variable i from degree list. */\n\t\t    /* ----------------------------------------------------- */\n\n\t\t    ilast = Last [i] ;\n\t\t    inext = Next [i] ;\n\t\t    ASSERT (ilast >= EMPTY && ilast < n) ;\n\t\t    ASSERT (inext >= EMPTY && inext < n) ;\n\t\t    if (inext != EMPTY) Last [inext] = ilast ;\n\t\t    if (ilast != EMPTY)\n\t\t    {\n\t\t\tNext [ilast] = inext ;\n\t\t    }\n\t\t    else\n\t\t    {\n\t\t\t/* i is at the head of the degree list */\n\t\t\tASSERT (Degree [i] >= 0 && Degree [i] < n) ;\n\t\t\tHead [Degree [i]] = inext ;\n\t\t    }\n\t\t}\n\t    }\n\t}\n\telse\n\t{\n\n\t    /* ------------------------------------------------------------- */\n\t    /* construct the new element in empty space, Iw [pfree ...] */\n\t    /* ------------------------------------------------------------- */\n\n\t    p = Pe [me] ;\n\t    pme1 = pfree ;\n\t    slenme = Len [me] - elenme ;\n\n\t    for (knt1 = 1 ; knt1 <= elenme + 1 ; knt1++)\n\t    {\n\n\t\tif (knt1 > elenme)\n\t\t{\n\t\t    /* search the supervariables in me. */\n\t\t    e = me ;\n\t\t    pj = p ;\n\t\t    ln = slenme ;\n\t\t    AMD_DEBUG2 ((\"Search sv: \"ID\" \"ID\" \"ID\"\\n\", me,pj,ln)) ;\n\t\t}\n\t\telse\n\t\t{\n\t\t    /* search the elements in me. */\n\t\t    e = Iw [p++] ;\n\t\t    ASSERT (e >= 0 && e < n) ;\n\t\t    pj = Pe [e] ;\n\t\t    ln = Len [e] ;\n\t\t    AMD_DEBUG2 ((\"Search element e \"ID\" in me \"ID\"\\n\", e,me)) ;\n\t\t    ASSERT (Elen [e] < EMPTY && W [e] > 0 && pj >= 0) ;\n\t\t}\n\t\tASSERT (ln >= 0 && (ln == 0 || (pj >= 0 && pj < iwlen))) ;\n\n\t\t/* ---------------------------------------------------------\n\t\t * search for different supervariables and add them to the\n\t\t * new list, compressing when necessary. this loop is\n\t\t * executed once for each element in the list and once for\n\t\t * all the supervariables in the list.\n\t\t * --------------------------------------------------------- */\n\n\t\tfor (knt2 = 1 ; knt2 <= ln ; knt2++)\n\t\t{\n\t\t    i = Iw [pj++] ;\n\t\t    ASSERT (i >= 0 && i < n && (i == me || Elen [i] >= EMPTY));\n\t\t    nvi = Nv [i] ;\n\t\t    AMD_DEBUG2 ((\": \"ID\" \"ID\" \"ID\" \"ID\"\\n\",\n\t\t\t\ti, Elen [i], Nv [i], wflg)) ;\n\n\t\t    if (nvi > 0)\n\t\t    {\n\n\t\t\t/* ------------------------------------------------- */\n\t\t\t/* compress Iw, if necessary */\n\t\t\t/* ------------------------------------------------- */\n\n\t\t\tif (pfree >= iwlen)\n\t\t\t{\n\n\t\t\t    AMD_DEBUG1 ((\"GARBAGE COLLECTION\\n\")) ;\n\n\t\t\t    /* prepare for compressing Iw by adjusting pointers\n\t\t\t     * and lengths so that the lists being searched in\n\t\t\t     * the inner and outer loops contain only the\n\t\t\t     * remaining entries. */\n\n\t\t\t    Pe [me] = p ;\n\t\t\t    Len [me] -= knt1 ;\n\t\t\t    /* check if nothing left of supervariable me */\n\t\t\t    if (Len [me] == 0) Pe [me] = EMPTY ;\n\t\t\t    Pe [e] = pj ;\n\t\t\t    Len [e] = ln - knt2 ;\n\t\t\t    /* nothing left of element e */\n\t\t\t    if (Len [e] == 0) Pe [e] = EMPTY ;\n\n\t\t\t    ncmpa++ ;\t/* one more garbage collection */\n\n\t\t\t    /* store first entry of each object in Pe */\n\t\t\t    /* FLIP the first entry in each object */\n\t\t\t    for (j = 0 ; j < n ; j++)\n\t\t\t    {\n\t\t\t\tpn = Pe [j] ;\n\t\t\t\tif (pn >= 0)\n\t\t\t\t{\n\t\t\t\t    ASSERT (pn >= 0 && pn < iwlen) ;\n\t\t\t\t    Pe [j] = Iw [pn] ;\n\t\t\t\t    Iw [pn] = FLIP (j) ;\n\t\t\t\t}\n\t\t\t    }\n\n\t\t\t    /* psrc/pdst point to source/destination */\n\t\t\t    psrc = 0 ;\n\t\t\t    pdst = 0 ;\n\t\t\t    pend = pme1 - 1 ;\n\n\t\t\t    while (psrc <= pend)\n\t\t\t    {\n\t\t\t\t/* search for next FLIP'd entry */\n\t\t\t\tj = FLIP (Iw [psrc++]) ;\n\t\t\t\tif (j >= 0)\n\t\t\t\t{\n\t\t\t\t    AMD_DEBUG2 ((\"Got object j: \"ID\"\\n\", j)) ;\n\t\t\t\t    Iw [pdst] = Pe [j] ;\n\t\t\t\t    Pe [j] = pdst++ ;\n\t\t\t\t    lenj = Len [j] ;\n\t\t\t\t    /* copy from source to destination */\n\t\t\t\t    for (knt3 = 0 ; knt3 <= lenj - 2 ; knt3++)\n\t\t\t\t    {\n\t\t\t\t\tIw [pdst++] = Iw [psrc++] ;\n\t\t\t\t    }\n\t\t\t\t}\n\t\t\t    }\n\n\t\t\t    /* move the new partially-constructed element */\n\t\t\t    p1 = pdst ;\n\t\t\t    for (psrc = pme1 ; psrc <= pfree-1 ; psrc++)\n\t\t\t    {\n\t\t\t\tIw [pdst++] = Iw [psrc] ;\n\t\t\t    }\n\t\t\t    pme1 = p1 ;\n\t\t\t    pfree = pdst ;\n\t\t\t    pj = Pe [e] ;\n\t\t\t    p = Pe [me] ;\n\n\t\t\t}\n\n\t\t\t/* ------------------------------------------------- */\n\t\t\t/* i is a principal variable not yet placed in Lme */\n\t\t\t/* store i in new list */\n\t\t\t/* ------------------------------------------------- */\n\n\t\t\t/* flag i as being in Lme by negating Nv [i] */\n\t\t\tdegme += nvi ;\n\t\t\tNv [i] = -nvi ;\n\t\t\tIw [pfree++] = i ;\n\t\t\tAMD_DEBUG2 ((\"     s: \"ID\"     nv \"ID\"\\n\", i, Nv [i]));\n\n\t\t\t/* ------------------------------------------------- */\n\t\t\t/* remove variable i from degree link list */\n\t\t\t/* ------------------------------------------------- */\n\n\t\t\tilast = Last [i] ;\n\t\t\tinext = Next [i] ;\n\t\t\tASSERT (ilast >= EMPTY && ilast < n) ;\n\t\t\tASSERT (inext >= EMPTY && inext < n) ;\n\t\t\tif (inext != EMPTY) Last [inext] = ilast ;\n\t\t\tif (ilast != EMPTY)\n\t\t\t{\n\t\t\t    Next [ilast] = inext ;\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t    /* i is at the head of the degree list */\n\t\t\t    ASSERT (Degree [i] >= 0 && Degree [i] < n) ;\n\t\t\t    Head [Degree [i]] = inext ;\n\t\t\t}\n\t\t    }\n\t\t}\n\n\t\tif (e != me)\n\t\t{\n\t\t    /* set tree pointer and flag to indicate element e is\n\t\t     * absorbed into new element me (the parent of e is me) */\n\t\t    AMD_DEBUG1 ((\" Element \"ID\" => \"ID\"\\n\", e, me)) ;\n\t\t    Pe [e] = FLIP (me) ;\n\t\t    W [e] = 0 ;\n\t\t}\n\t    }\n\n\t    pme2 = pfree - 1 ;\n\t}\n\n\t/* ----------------------------------------------------------------- */\n\t/* me has now been converted into an element in Iw [pme1..pme2] */\n\t/* ----------------------------------------------------------------- */\n\n\t/* degme holds the external degree of new element */\n\tDegree [me] = degme ;\n\tPe [me] = pme1 ;\n\tLen [me] = pme2 - pme1 + 1 ;\n\tASSERT (Pe [me] >= 0 && Pe [me] < iwlen) ;\n\n\tElen [me] = FLIP (nvpiv + degme) ;\n\t/* FLIP (Elen (me)) is now the degree of pivot (including\n\t * diagonal part). */\n\n#ifndef NDEBUG\n\tAMD_DEBUG2 ((\"New element structure: length= \"ID\"\\n\", pme2-pme1+1)) ;\n\tfor (pme = pme1 ; pme <= pme2 ; pme++) AMD_DEBUG3 ((\" \"ID\"\", Iw[pme]));\n\tAMD_DEBUG3 ((\"\\n\")) ;\n#endif\n\n\t/* ----------------------------------------------------------------- */\n\t/* make sure that wflg is not too large. */\n\t/* ----------------------------------------------------------------- */\n\n\t/* With the current value of wflg, wflg+n must not cause integer\n\t * overflow */\n\n\twflg = clear_flag (wflg, wbig, W, n) ;\n\n/* ========================================================================= */\n/* COMPUTE (W [e] - wflg) = |Le\\Lme| FOR ALL ELEMENTS */\n/* ========================================================================= */\n\n\t/* -----------------------------------------------------------------\n\t * Scan 1:  compute the external degrees of previous elements with\n\t * respect to the current element.  That is:\n\t *       (W [e] - wflg) = |Le \\ Lme|\n\t * for each element e that appears in any supervariable in Lme.  The\n\t * notation Le refers to the pattern (list of supervariables) of a\n\t * previous element e, where e is not yet absorbed, stored in\n\t * Iw [Pe [e] + 1 ... Pe [e] + Len [e]].  The notation Lme\n\t * refers to the pattern of the current element (stored in\n\t * Iw [pme1..pme2]).   If aggressive absorption is enabled, and\n\t * (W [e] - wflg) becomes zero, then the element e will be absorbed\n\t * in Scan 2.\n\t * ----------------------------------------------------------------- */\n\n\tAMD_DEBUG2 ((\"me: \")) ;\n\tfor (pme = pme1 ; pme <= pme2 ; pme++)\n\t{\n\t    i = Iw [pme] ;\n\t    ASSERT (i >= 0 && i < n) ;\n\t    eln = Elen [i] ;\n\t    AMD_DEBUG3 ((\"\"ID\" Elen \"ID\": \\n\", i, eln)) ;\n\t    if (eln > 0)\n\t    {\n\t\t/* note that Nv [i] has been negated to denote i in Lme: */\n\t\tnvi = -Nv [i] ;\n\t\tASSERT (nvi > 0 && Pe [i] >= 0 && Pe [i] < iwlen) ;\n\t\twnvi = wflg - nvi ;\n\t\tfor (p = Pe [i] ; p <= Pe [i] + eln - 1 ; p++)\n\t\t{\n\t\t    e = Iw [p] ;\n\t\t    ASSERT (e >= 0 && e < n) ;\n\t\t    we = W [e] ;\n\t\t    AMD_DEBUG4 ((\"    e \"ID\" we \"ID\" \", e, we)) ;\n\t\t    if (we >= wflg)\n\t\t    {\n\t\t\t/* unabsorbed element e has been seen in this loop */\n\t\t\tAMD_DEBUG4 ((\"    unabsorbed, first time seen\")) ;\n\t\t\twe -= nvi ;\n\t\t    }\n\t\t    else if (we != 0)\n\t\t    {\n\t\t\t/* e is an unabsorbed element */\n\t\t\t/* this is the first we have seen e in all of Scan 1 */\n\t\t\tAMD_DEBUG4 ((\"    unabsorbed\")) ;\n\t\t\twe = Degree [e] + wnvi ;\n\t\t    }\n\t\t    AMD_DEBUG4 ((\"\\n\")) ;\n\t\t    W [e] = we ;\n\t\t}\n\t    }\n\t}\n\tAMD_DEBUG2 ((\"\\n\")) ;\n\n/* ========================================================================= */\n/* DEGREE UPDATE AND ELEMENT ABSORPTION */\n/* ========================================================================= */\n\n\t/* -----------------------------------------------------------------\n\t * Scan 2:  for each i in Lme, sum up the degree of Lme (which is\n\t * degme), plus the sum of the external degrees of each Le for the\n\t * elements e appearing within i, plus the supervariables in i.\n\t * Place i in hash list.\n\t * ----------------------------------------------------------------- */\n\n\tfor (pme = pme1 ; pme <= pme2 ; pme++)\n\t{\n\t    i = Iw [pme] ;\n\t    ASSERT (i >= 0 && i < n && Nv [i] < 0 && Elen [i] >= 0) ;\n\t    AMD_DEBUG2 ((\"Updating: i \"ID\" \"ID\" \"ID\"\\n\", i, Elen[i], Len [i]));\n\t    p1 = Pe [i] ;\n\t    p2 = p1 + Elen [i] - 1 ;\n\t    pn = p1 ;\n\t    hash = 0 ;\n\t    deg = 0 ;\n\t    ASSERT (p1 >= 0 && p1 < iwlen && p2 >= -1 && p2 < iwlen) ;\n\n\t    /* ------------------------------------------------------------- */\n\t    /* scan the element list associated with supervariable i */\n\t    /* ------------------------------------------------------------- */\n\n\t    /* UMFPACK/MA38-style approximate degree: */\n\t    if (aggressive)\n\t    {\n\t\tfor (p = p1 ; p <= p2 ; p++)\n\t\t{\n\t\t    e = Iw [p] ;\n\t\t    ASSERT (e >= 0 && e < n) ;\n\t\t    we = W [e] ;\n\t\t    if (we != 0)\n\t\t    {\n\t\t\t/* e is an unabsorbed element */\n\t\t\t/* dext = | Le \\ Lme | */\n\t\t\tdext = we - wflg ;\n\t\t\tif (dext > 0)\n\t\t\t{\n\t\t\t    deg += dext ;\n\t\t\t    Iw [pn++] = e ;\n\t\t\t    hash += e ;\n\t\t\t    AMD_DEBUG4 ((\" e: \"ID\" hash = \"ID\"\\n\",e,hash)) ;\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t    /* external degree of e is zero, absorb e into me*/\n\t\t\t    AMD_DEBUG1 ((\" Element \"ID\" =>\"ID\" (aggressive)\\n\",\n\t\t\t\te, me)) ;\n\t\t\t    ASSERT (dext == 0) ;\n\t\t\t    Pe [e] = FLIP (me) ;\n\t\t\t    W [e] = 0 ;\n\t\t\t}\n\t\t    }\n\t\t}\n\t    }\n\t    else\n\t    {\n\t\tfor (p = p1 ; p <= p2 ; p++)\n\t\t{\n\t\t    e = Iw [p] ;\n\t\t    ASSERT (e >= 0 && e < n) ;\n\t\t    we = W [e] ;\n\t\t    if (we != 0)\n\t\t    {\n\t\t\t/* e is an unabsorbed element */\n\t\t\tdext = we - wflg ;\n\t\t\tASSERT (dext >= 0) ;\n\t\t\tdeg += dext ;\n\t\t\tIw [pn++] = e ;\n\t\t\thash += e ;\n\t\t\tAMD_DEBUG4 ((\"\te: \"ID\" hash = \"ID\"\\n\",e,hash)) ;\n\t\t    }\n\t\t}\n\t    }\n\n\t    /* count the number of elements in i (including me): */\n\t    Elen [i] = pn - p1 + 1 ;\n\n\t    /* ------------------------------------------------------------- */\n\t    /* scan the supervariables in the list associated with i */\n\t    /* ------------------------------------------------------------- */\n\n\t    /* The bulk of the AMD run time is typically spent in this loop,\n\t     * particularly if the matrix has many dense rows that are not\n\t     * removed prior to ordering. */\n\t    p3 = pn ;\n\t    p4 = p1 + Len [i] ;\n\t    for (p = p2 + 1 ; p < p4 ; p++)\n\t    {\n\t\tj = Iw [p] ;\n\t\tASSERT (j >= 0 && j < n) ;\n\t\tnvj = Nv [j] ;\n\t\tif (nvj > 0)\n\t\t{\n\t\t    /* j is unabsorbed, and not in Lme. */\n\t\t    /* add to degree and add to new list */\n\t\t    deg += nvj ;\n\t\t    Iw [pn++] = j ;\n\t\t    hash += j ;\n\t\t    AMD_DEBUG4 ((\"  s: \"ID\" hash \"ID\" Nv[j]= \"ID\"\\n\",\n\t\t\t\tj, hash, nvj)) ;\n\t\t}\n\t    }\n\n\t    /* ------------------------------------------------------------- */\n\t    /* update the degree and check for mass elimination */\n\t    /* ------------------------------------------------------------- */\n\n\t    /* with aggressive absorption, deg==0 is identical to the\n\t     * Elen [i] == 1 && p3 == pn test, below. */\n\t    ASSERT (IMPLIES (aggressive, (deg==0) == (Elen[i]==1 && p3==pn))) ;\n\n\t    if (Elen [i] == 1 && p3 == pn)\n\t    {\n\n\t\t/* --------------------------------------------------------- */\n\t\t/* mass elimination */\n\t\t/* --------------------------------------------------------- */\n\n\t\t/* There is nothing left of this node except for an edge to\n\t\t * the current pivot element.  Elen [i] is 1, and there are\n\t\t * no variables adjacent to node i.  Absorb i into the\n\t\t * current pivot element, me.  Note that if there are two or\n\t\t * more mass eliminations, fillin due to mass elimination is\n\t\t * possible within the nvpiv-by-nvpiv pivot block.  It is this\n\t\t * step that causes AMD's analysis to be an upper bound.\n\t\t *\n\t\t * The reason is that the selected pivot has a lower\n\t\t * approximate degree than the true degree of the two mass\n\t\t * eliminated nodes.  There is no edge between the two mass\n\t\t * eliminated nodes.  They are merged with the current pivot\n\t\t * anyway.\n\t\t *\n\t\t * No fillin occurs in the Schur complement, in any case,\n\t\t * and this effect does not decrease the quality of the\n\t\t * ordering itself, just the quality of the nonzero and\n\t\t * flop count analysis.  It also means that the post-ordering\n\t\t * is not an exact elimination tree post-ordering. */\n\n\t\tAMD_DEBUG1 ((\"  MASS i \"ID\" => parent e \"ID\"\\n\", i, me)) ;\n\t\tPe [i] = FLIP (me) ;\n\t\tnvi = -Nv [i] ;\n\t\tdegme -= nvi ;\n\t\tnvpiv += nvi ;\n\t\tnel += nvi ;\n\t\tNv [i] = 0 ;\n\t\tElen [i] = EMPTY ;\n\n\t    }\n\t    else\n\t    {\n\n\t\t/* --------------------------------------------------------- */\n\t\t/* update the upper-bound degree of i */\n\t\t/* --------------------------------------------------------- */\n\n\t\t/* the following degree does not yet include the size\n\t\t * of the current element, which is added later: */\n\n\t\tDegree [i] = MIN (Degree [i], deg) ;\n\n\t\t/* --------------------------------------------------------- */\n\t\t/* add me to the list for i */\n\t\t/* --------------------------------------------------------- */\n\n\t\t/* move first supervariable to end of list */\n\t\tIw [pn] = Iw [p3] ;\n\t\t/* move first element to end of element part of list */\n\t\tIw [p3] = Iw [p1] ;\n\t\t/* add new element, me, to front of list. */\n\t\tIw [p1] = me ;\n\t\t/* store the new length of the list in Len [i] */\n\t\tLen [i] = pn - p1 + 1 ;\n\n\t\t/* --------------------------------------------------------- */\n\t\t/* place in hash bucket.  Save hash key of i in Last [i]. */\n\t\t/* --------------------------------------------------------- */\n\n\t\t/* NOTE: this can fail if hash is negative, because the ANSI C\n\t\t * standard does not define a % b when a and/or b are negative.\n\t\t * That's why hash is defined as an unsigned Int, to avoid this\n\t\t * problem. */\n\t\thash = hash % n ;\n\t\tASSERT (((Int) hash) >= 0 && ((Int) hash) < n) ;\n\n\t\t/* if the Hhead array is not used: */\n\t\tj = Head [hash] ;\n\t\tif (j <= EMPTY)\n\t\t{\n\t\t    /* degree list is empty, hash head is FLIP (j) */\n\t\t    Next [i] = FLIP (j) ;\n\t\t    Head [hash] = FLIP (i) ;\n\t\t}\n\t\telse\n\t\t{\n\t\t    /* degree list is not empty, use Last [Head [hash]] as\n\t\t     * hash head. */\n\t\t    Next [i] = Last [j] ;\n\t\t    Last [j] = i ;\n\t\t}\n\n\t\t/* if a separate Hhead array is used: *\n\t\tNext [i] = Hhead [hash] ;\n\t\tHhead [hash] = i ;\n\t\t*/\n\n\t\tLast [i] = hash ;\n\t    }\n\t}\n\n\tDegree [me] = degme ;\n\n\t/* ----------------------------------------------------------------- */\n\t/* Clear the counter array, W [...], by incrementing wflg. */\n\t/* ----------------------------------------------------------------- */\n\n\t/* make sure that wflg+n does not cause integer overflow */\n\tlemax =  MAX (lemax, degme) ;\n\twflg += lemax ;\n\twflg = clear_flag (wflg, wbig, W, n) ;\n\t/*  at this point, W [0..n-1] < wflg holds */\n\n/* ========================================================================= */\n/* SUPERVARIABLE DETECTION */\n/* ========================================================================= */\n\n\tAMD_DEBUG1 ((\"Detecting supervariables:\\n\")) ;\n\tfor (pme = pme1 ; pme <= pme2 ; pme++)\n\t{\n\t    i = Iw [pme] ;\n\t    ASSERT (i >= 0 && i < n) ;\n\t    AMD_DEBUG2 ((\"Consider i \"ID\" nv \"ID\"\\n\", i, Nv [i])) ;\n\t    if (Nv [i] < 0)\n\t    {\n\t\t/* i is a principal variable in Lme */\n\n\t\t/* ---------------------------------------------------------\n\t\t * examine all hash buckets with 2 or more variables.  We do\n\t\t * this by examing all unique hash keys for supervariables in\n\t\t * the pattern Lme of the current element, me\n\t\t * --------------------------------------------------------- */\n\n\t\t/* let i = head of hash bucket, and empty the hash bucket */\n\t\tASSERT (Last [i] >= 0 && Last [i] < n) ;\n\t\thash = Last [i] ;\n\n\t\t/* if Hhead array is not used: */\n\t\tj = Head [hash] ;\n\t\tif (j == EMPTY)\n\t\t{\n\t\t    /* hash bucket and degree list are both empty */\n\t\t    i = EMPTY ;\n\t\t}\n\t\telse if (j < EMPTY)\n\t\t{\n\t\t    /* degree list is empty */\n\t\t    i = FLIP (j) ;\n\t\t    Head [hash] = EMPTY ;\n\t\t}\n\t\telse\n\t\t{\n\t\t    /* degree list is not empty, restore Last [j] of head j */\n\t\t    i = Last [j] ;\n\t\t    Last [j] = EMPTY ;\n\t\t}\n\n\t\t/* if separate Hhead array is used: *\n\t\ti = Hhead [hash] ;\n\t\tHhead [hash] = EMPTY ;\n\t\t*/\n\n\t\tASSERT (i >= EMPTY && i < n) ;\n\t\tAMD_DEBUG2 ((\"----i \"ID\" hash \"ID\"\\n\", i, hash)) ;\n\n\t\twhile (i != EMPTY && Next [i] != EMPTY)\n\t\t{\n\n\t\t    /* -----------------------------------------------------\n\t\t     * this bucket has one or more variables following i.\n\t\t     * scan all of them to see if i can absorb any entries\n\t\t     * that follow i in hash bucket.  Scatter i into w.\n\t\t     * ----------------------------------------------------- */\n\n\t\t    ln = Len [i] ;\n\t\t    eln = Elen [i] ;\n\t\t    ASSERT (ln >= 0 && eln >= 0) ;\n\t\t    ASSERT (Pe [i] >= 0 && Pe [i] < iwlen) ;\n\t\t    /* do not flag the first element in the list (me) */\n\t\t    for (p = Pe [i] + 1 ; p <= Pe [i] + ln - 1 ; p++)\n\t\t    {\n\t\t\tASSERT (Iw [p] >= 0 && Iw [p] < n) ;\n\t\t\tW [Iw [p]] = wflg ;\n\t\t    }\n\n\t\t    /* ----------------------------------------------------- */\n\t\t    /* scan every other entry j following i in bucket */\n\t\t    /* ----------------------------------------------------- */\n\n\t\t    jlast = i ;\n\t\t    j = Next [i] ;\n\t\t    ASSERT (j >= EMPTY && j < n) ;\n\n\t\t    while (j != EMPTY)\n\t\t    {\n\t\t\t/* ------------------------------------------------- */\n\t\t\t/* check if j and i have identical nonzero pattern */\n\t\t\t/* ------------------------------------------------- */\n\n\t\t\tAMD_DEBUG3 ((\"compare i \"ID\" and j \"ID\"\\n\", i,j)) ;\n\n\t\t\t/* check if i and j have the same Len and Elen */\n\t\t\tASSERT (Len [j] >= 0 && Elen [j] >= 0) ;\n\t\t\tASSERT (Pe [j] >= 0 && Pe [j] < iwlen) ;\n\t\t\tok = (Len [j] == ln) && (Elen [j] == eln) ;\n\t\t\t/* skip the first element in the list (me) */\n\t\t\tfor (p = Pe [j] + 1 ; ok && p <= Pe [j] + ln - 1 ; p++)\n\t\t\t{\n\t\t\t    ASSERT (Iw [p] >= 0 && Iw [p] < n) ;\n\t\t\t    if (W [Iw [p]] != wflg) ok = 0 ;\n\t\t\t}\n\t\t\tif (ok)\n\t\t\t{\n\t\t\t    /* --------------------------------------------- */\n\t\t\t    /* found it!  j can be absorbed into i */\n\t\t\t    /* --------------------------------------------- */\n\n\t\t\t    AMD_DEBUG1 ((\"found it! j \"ID\" => i \"ID\"\\n\", j,i));\n\t\t\t    Pe [j] = FLIP (i) ;\n\t\t\t    /* both Nv [i] and Nv [j] are negated since they */\n\t\t\t    /* are in Lme, and the absolute values of each */\n\t\t\t    /* are the number of variables in i and j: */\n\t\t\t    Nv [i] += Nv [j] ;\n\t\t\t    Nv [j] = 0 ;\n\t\t\t    Elen [j] = EMPTY ;\n\t\t\t    /* delete j from hash bucket */\n\t\t\t    ASSERT (j != Next [j]) ;\n\t\t\t    j = Next [j] ;\n\t\t\t    Next [jlast] = j ;\n\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t    /* j cannot be absorbed into i */\n\t\t\t    jlast = j ;\n\t\t\t    ASSERT (j != Next [j]) ;\n\t\t\t    j = Next [j] ;\n\t\t\t}\n\t\t\tASSERT (j >= EMPTY && j < n) ;\n\t\t    }\n\n\t\t    /* -----------------------------------------------------\n\t\t     * no more variables can be absorbed into i\n\t\t     * go to next i in bucket and clear flag array\n\t\t     * ----------------------------------------------------- */\n\n\t\t    wflg++ ;\n\t\t    i = Next [i] ;\n\t\t    ASSERT (i >= EMPTY && i < n) ;\n\n\t\t}\n\t    }\n\t}\n\tAMD_DEBUG2 ((\"detect done\\n\")) ;\n\n/* ========================================================================= */\n/* RESTORE DEGREE LISTS AND REMOVE NONPRINCIPAL SUPERVARIABLES FROM ELEMENT */\n/* ========================================================================= */\n\n\tp = pme1 ;\n\tnleft = n - nel ;\n\tfor (pme = pme1 ; pme <= pme2 ; pme++)\n\t{\n\t    i = Iw [pme] ;\n\t    ASSERT (i >= 0 && i < n) ;\n\t    nvi = -Nv [i] ;\n\t    AMD_DEBUG3 ((\"Restore i \"ID\" \"ID\"\\n\", i, nvi)) ;\n\t    if (nvi > 0)\n\t    {\n\t\t/* i is a principal variable in Lme */\n\t\t/* restore Nv [i] to signify that i is principal */\n\t\tNv [i] = nvi ;\n\n\t\t/* --------------------------------------------------------- */\n\t\t/* compute the external degree (add size of current element) */\n\t\t/* --------------------------------------------------------- */\n\n\t\tdeg = Degree [i] + degme - nvi ;\n\t\tdeg = MIN (deg, nleft - nvi) ;\n\t\tASSERT (IMPLIES (aggressive, deg > 0) && deg >= 0 && deg < n) ;\n\n\t\t/* --------------------------------------------------------- */\n\t\t/* place the supervariable at the head of the degree list */\n\t\t/* --------------------------------------------------------- */\n\n\t\tinext = Head [deg] ;\n\t\tASSERT (inext >= EMPTY && inext < n) ;\n\t\tif (inext != EMPTY) Last [inext] = i ;\n\t\tNext [i] = inext ;\n\t\tLast [i] = EMPTY ;\n\t\tHead [deg] = i ;\n\n\t\t/* --------------------------------------------------------- */\n\t\t/* save the new degree, and find the minimum degree */\n\t\t/* --------------------------------------------------------- */\n\n\t\tmindeg = MIN (mindeg, deg) ;\n\t\tDegree [i] = deg ;\n\n\t\t/* --------------------------------------------------------- */\n\t\t/* place the supervariable in the element pattern */\n\t\t/* --------------------------------------------------------- */\n\n\t\tIw [p++] = i ;\n\n\t    }\n\t}\n\tAMD_DEBUG2 ((\"restore done\\n\")) ;\n\n/* ========================================================================= */\n/* FINALIZE THE NEW ELEMENT */\n/* ========================================================================= */\n\n\tAMD_DEBUG2 ((\"ME = \"ID\" DONE\\n\", me)) ;\n\tNv [me] = nvpiv ;\n\t/* save the length of the list for the new element me */\n\tLen [me] = p - pme1 ;\n\tif (Len [me] == 0)\n\t{\n\t    /* there is nothing left of the current pivot element */\n\t    /* it is a root of the assembly tree */\n\t    Pe [me] = EMPTY ;\n\t    W [me] = 0 ;\n\t}\n\tif (elenme != 0)\n\t{\n\t    /* element was not constructed in place: deallocate part of */\n\t    /* it since newly nonprincipal variables may have been removed */\n\t    pfree = p ;\n\t}\n\n\t/* The new element has nvpiv pivots and the size of the contribution\n\t * block for a multifrontal method is degme-by-degme, not including\n\t * the \"dense\" rows/columns.  If the \"dense\" rows/columns are included,\n\t * the frontal matrix is no larger than\n\t * (degme+ndense)-by-(degme+ndense).\n\t */\n\n\tif (Info != (c_float *) NULL)\n\t{\n\t    f = nvpiv ;\n\t    r = degme + ndense ;\n\t    dmax = MAX (dmax, f + r) ;\n\n\t    /* number of nonzeros in L (excluding the diagonal) */\n\t    lnzme = f*r + (f-1)*f/2 ;\n\t    lnz += lnzme ;\n\n\t    /* number of divide operations for LDL' and for LU */\n\t    ndiv += lnzme ;\n\n\t    /* number of multiply-subtract pairs for LU */\n\t    s = f*r*r + r*(f-1)*f + (f-1)*f*(2*f-1)/6 ;\n\t    nms_lu += s ;\n\n\t    /* number of multiply-subtract pairs for LDL' */\n\t    nms_ldl += (s + lnzme)/2 ;\n\t}\n\n#ifndef NDEBUG\n\tAMD_DEBUG2 ((\"finalize done nel \"ID\" n \"ID\"\\n   ::::\\n\", nel, n)) ;\n\tfor (pme = Pe [me] ; pme <= Pe [me] + Len [me] - 1 ; pme++)\n\t{\n\t      AMD_DEBUG3 ((\" \"ID\"\", Iw [pme])) ;\n\t}\n\tAMD_DEBUG3 ((\"\\n\")) ;\n#endif\n\n    }\n\n/* ========================================================================= */\n/* DONE SELECTING PIVOTS */\n/* ========================================================================= */\n\n    if (Info != (c_float *) NULL)\n    {\n\n\t/* count the work to factorize the ndense-by-ndense submatrix */\n\tf = ndense ;\n\tdmax = MAX (dmax, (c_float) ndense) ;\n\n\t/* number of nonzeros in L (excluding the diagonal) */\n\tlnzme = (f-1)*f/2 ;\n\tlnz += lnzme ;\n\n\t/* number of divide operations for LDL' and for LU */\n\tndiv += lnzme ;\n\n\t/* number of multiply-subtract pairs for LU */\n\ts = (f-1)*f*(2*f-1)/6 ;\n\tnms_lu += s ;\n\n\t/* number of multiply-subtract pairs for LDL' */\n\tnms_ldl += (s + lnzme)/2 ;\n\n\t/* number of nz's in L (excl. diagonal) */\n\tInfo [AMD_LNZ] = lnz ;\n\n\t/* number of divide ops for LU and LDL' */\n\tInfo [AMD_NDIV] = ndiv ;\n\n\t/* number of multiply-subtract pairs for LDL' */\n\tInfo [AMD_NMULTSUBS_LDL] = nms_ldl ;\n\n\t/* number of multiply-subtract pairs for LU */\n\tInfo [AMD_NMULTSUBS_LU] = nms_lu ;\n\n\t/* number of \"dense\" rows/columns */\n\tInfo [AMD_NDENSE] = ndense ;\n\n\t/* largest front is dmax-by-dmax */\n\tInfo [AMD_DMAX] = dmax ;\n\n\t/* number of garbage collections in AMD */\n\tInfo [AMD_NCMPA] = ncmpa ;\n\n\t/* successful ordering */\n\tInfo [AMD_STATUS] = AMD_OK ;\n    }\n\n/* ========================================================================= */\n/* POST-ORDERING */\n/* ========================================================================= */\n\n/* -------------------------------------------------------------------------\n * Variables at this point:\n *\n * Pe: holds the elimination tree.  The parent of j is FLIP (Pe [j]),\n *\tor EMPTY if j is a root.  The tree holds both elements and\n *\tnon-principal (unordered) variables absorbed into them.\n *\tDense variables are non-principal and unordered.\n *\n * Elen: holds the size of each element, including the diagonal part.\n *\tFLIP (Elen [e]) > 0 if e is an element.  For unordered\n *\tvariables i, Elen [i] is EMPTY.\n *\n * Nv: Nv [e] > 0 is the number of pivots represented by the element e.\n *\tFor unordered variables i, Nv [i] is zero.\n *\n * Contents no longer needed:\n *\tW, Iw, Len, Degree, Head, Next, Last.\n *\n * The matrix itself has been destroyed.\n *\n * n: the size of the matrix.\n * No other scalars needed (pfree, iwlen, etc.)\n * ------------------------------------------------------------------------- */\n\n    /* restore Pe */\n    for (i = 0 ; i < n ; i++)\n    {\n\tPe [i] = FLIP (Pe [i]) ;\n    }\n\n    /* restore Elen, for output information, and for postordering */\n    for (i = 0 ; i < n ; i++)\n    {\n\tElen [i] = FLIP (Elen [i]) ;\n    }\n\n/* Now the parent of j is Pe [j], or EMPTY if j is a root.  Elen [e] > 0\n * is the size of element e.  Elen [i] is EMPTY for unordered variable i. */\n\n#ifndef NDEBUG\n    AMD_DEBUG2 ((\"\\nTree:\\n\")) ;\n    for (i = 0 ; i < n ; i++)\n    {\n\tAMD_DEBUG2 ((\" \"ID\" parent: \"ID\"   \", i, Pe [i])) ;\n\tASSERT (Pe [i] >= EMPTY && Pe [i] < n) ;\n\tif (Nv [i] > 0)\n\t{\n\t    /* this is an element */\n\t    e = i ;\n\t    AMD_DEBUG2 ((\" element, size is \"ID\"\\n\", Elen [i])) ;\n\t    ASSERT (Elen [e] > 0) ;\n\t}\n\tAMD_DEBUG2 ((\"\\n\")) ;\n    }\n    AMD_DEBUG2 ((\"\\nelements:\\n\")) ;\n    for (e = 0 ; e < n ; e++)\n    {\n\tif (Nv [e] > 0)\n\t{\n\t    AMD_DEBUG3 ((\"Element e= \"ID\" size \"ID\" nv \"ID\" \\n\", e,\n\t\tElen [e], Nv [e])) ;\n\t}\n    }\n    AMD_DEBUG2 ((\"\\nvariables:\\n\")) ;\n    for (i = 0 ; i < n ; i++)\n    {\n\tInt cnt ;\n\tif (Nv [i] == 0)\n\t{\n\t    AMD_DEBUG3 ((\"i unordered: \"ID\"\\n\", i)) ;\n\t    j = Pe [i] ;\n\t    cnt = 0 ;\n\t    AMD_DEBUG3 ((\"  j: \"ID\"\\n\", j)) ;\n\t    if (j == EMPTY)\n\t    {\n\t\tAMD_DEBUG3 ((\"\ti is a dense variable\\n\")) ;\n\t    }\n\t    else\n\t    {\n\t\tASSERT (j >= 0 && j < n) ;\n\t\twhile (Nv [j] == 0)\n\t\t{\n\t\t    AMD_DEBUG3 ((\"\tj : \"ID\"\\n\", j)) ;\n\t\t    j = Pe [j] ;\n\t\t    AMD_DEBUG3 ((\"\tj:: \"ID\"\\n\", j)) ;\n\t\t    cnt++ ;\n\t\t    if (cnt > n) break ;\n\t\t}\n\t\te = j ;\n\t\tAMD_DEBUG3 ((\"\tgot to e: \"ID\"\\n\", e)) ;\n\t    }\n\t}\n    }\n#endif\n\n/* ========================================================================= */\n/* compress the paths of the variables */\n/* ========================================================================= */\n\n    for (i = 0 ; i < n ; i++)\n    {\n\tif (Nv [i] == 0)\n\t{\n\n\t    /* -------------------------------------------------------------\n\t     * i is an un-ordered row.  Traverse the tree from i until\n\t     * reaching an element, e.  The element, e, was the principal\n\t     * supervariable of i and all nodes in the path from i to when e\n\t     * was selected as pivot.\n\t     * ------------------------------------------------------------- */\n\n\t    AMD_DEBUG1 ((\"Path compression, i unordered: \"ID\"\\n\", i)) ;\n\t    j = Pe [i] ;\n\t    ASSERT (j >= EMPTY && j < n) ;\n\t    AMD_DEBUG3 ((\"\tj: \"ID\"\\n\", j)) ;\n\t    if (j == EMPTY)\n\t    {\n\t\t/* Skip a dense variable.  It has no parent. */\n\t\tAMD_DEBUG3 ((\"      i is a dense variable\\n\")) ;\n\t\tcontinue ;\n\t    }\n\n\t    /* while (j is a variable) */\n\t    while (Nv [j] == 0)\n\t    {\n\t\tAMD_DEBUG3 ((\"\t\tj : \"ID\"\\n\", j)) ;\n\t\tj = Pe [j] ;\n\t\tAMD_DEBUG3 ((\"\t\tj:: \"ID\"\\n\", j)) ;\n\t\tASSERT (j >= 0 && j < n) ;\n\t    }\n\t    /* got to an element e */\n\t    e = j ;\n\t    AMD_DEBUG3 ((\"got to e: \"ID\"\\n\", e)) ;\n\n\t    /* -------------------------------------------------------------\n\t     * traverse the path again from i to e, and compress the path\n\t     * (all nodes point to e).  Path compression allows this code to\n\t     * compute in O(n) time.\n\t     * ------------------------------------------------------------- */\n\n\t    j = i ;\n\t    /* while (j is a variable) */\n\t    while (Nv [j] == 0)\n\t    {\n\t\tjnext = Pe [j] ;\n\t\tAMD_DEBUG3 ((\"j \"ID\" jnext \"ID\"\\n\", j, jnext)) ;\n\t\tPe [j] = e ;\n\t\tj = jnext ;\n\t\tASSERT (j >= 0 && j < n) ;\n\t    }\n\t}\n    }\n\n/* ========================================================================= */\n/* postorder the assembly tree */\n/* ========================================================================= */\n\n    AMD_postorder (n, Pe, Nv, Elen,\n\tW,\t\t\t/* output order */\n\tHead, Next, Last) ;\t/* workspace */\n\n/* ========================================================================= */\n/* compute output permutation and inverse permutation */\n/* ========================================================================= */\n\n    /* W [e] = k means that element e is the kth element in the new\n     * order.  e is in the range 0 to n-1, and k is in the range 0 to\n     * the number of elements.  Use Head for inverse order. */\n\n    for (k = 0 ; k < n ; k++)\n    {\n\tHead [k] = EMPTY ;\n\tNext [k] = EMPTY ;\n    }\n    for (e = 0 ; e < n ; e++)\n    {\n\tk = W [e] ;\n\tASSERT ((k == EMPTY) == (Nv [e] == 0)) ;\n\tif (k != EMPTY)\n\t{\n\t    ASSERT (k >= 0 && k < n) ;\n\t    Head [k] = e ;\n\t}\n    }\n\n    /* construct output inverse permutation in Next,\n     * and permutation in Last */\n    nel = 0 ;\n    for (k = 0 ; k < n ; k++)\n    {\n\te = Head [k] ;\n\tif (e == EMPTY) break ;\n\tASSERT (e >= 0 && e < n && Nv [e] > 0) ;\n\tNext [e] = nel ;\n\tnel += Nv [e] ;\n    }\n    ASSERT (nel == n - ndense) ;\n\n    /* order non-principal variables (dense, & those merged into supervar's) */\n    for (i = 0 ; i < n ; i++)\n    {\n\tif (Nv [i] == 0)\n\t{\n\t    e = Pe [i] ;\n\t    ASSERT (e >= EMPTY && e < n) ;\n\t    if (e != EMPTY)\n\t    {\n\t\t/* This is an unordered variable that was merged\n\t\t * into element e via supernode detection or mass\n\t\t * elimination of i when e became the pivot element.\n\t\t * Place i in order just before e. */\n\t\tASSERT (Next [i] == EMPTY && Nv [e] > 0) ;\n\t\tNext [i] = Next [e] ;\n\t\tNext [e]++ ;\n\t    }\n\t    else\n\t    {\n\t\t/* This is a dense unordered variable, with no parent.\n\t\t * Place it last in the output order. */\n\t\tNext [i] = nel++ ;\n\t    }\n\t}\n    }\n    ASSERT (nel == n) ;\n\n    AMD_DEBUG2 ((\"\\n\\nPerm:\\n\")) ;\n    for (i = 0 ; i < n ; i++)\n    {\n\tk = Next [i] ;\n\tASSERT (k >= 0 && k < n) ;\n\tLast [k] = i ;\n\tAMD_DEBUG2 ((\"   perm [\"ID\"] = \"ID\"\\n\", k, i)) ;\n    }\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/amd/src/amd_aat.c",
    "content": "/* ========================================================================= */\n/* === AMD_aat ============================================================= */\n/* ========================================================================= */\n\n/* ------------------------------------------------------------------------- */\n/* AMD, Copyright (c) Timothy A. Davis,\t\t\t\t\t     */\n/* Patrick R. Amestoy, and Iain S. Duff.  See ../README.txt for License.     */\n/* email: DrTimothyAldenDavis@gmail.com                                      */\n/* ------------------------------------------------------------------------- */\n\n/* AMD_aat:  compute the symmetry of the pattern of A, and count the number of\n * nonzeros each column of A+A' (excluding the diagonal).  Assumes the input\n * matrix has no errors, with sorted columns and no duplicates\n * (AMD_valid (n, n, Ap, Ai) must be AMD_OK, but this condition is not\n * checked).\n */\n\n#include \"amd_internal.h\"\n\nGLOBAL size_t AMD_aat\t/* returns nz in A+A' */\n(\n    Int n,\n    const Int Ap [ ],\n    const Int Ai [ ],\n    Int Len [ ],\t/* Len [j]: length of column j of A+A', excl diagonal*/\n    Int Tp [ ],\t\t/* workspace of size n */\n    c_float Info [ ]\n)\n{\n    Int p1, p2, p, i, j, pj, pj2, k, nzdiag, nzboth, nz ;\n    c_float sym ;\n    size_t nzaat ;\n\n#ifndef NDEBUG\n    AMD_debug_init (\"AMD AAT\") ;\n    for (k = 0 ; k < n ; k++) Tp [k] = EMPTY ;\n    ASSERT (AMD_valid (n, n, Ap, Ai) == AMD_OK) ;\n#endif\n\n    if (Info != (c_float *) NULL)\n    {\n\t/* clear the Info array, if it exists */\n\tfor (i = 0 ; i < AMD_INFO ; i++)\n\t{\n\t    Info [i] = EMPTY ;\n\t}\n\tInfo [AMD_STATUS] = AMD_OK ;\n    }\n\n    for (k = 0 ; k < n ; k++)\n    {\n\tLen [k] = 0 ;\n    }\n\n    nzdiag = 0 ;\n    nzboth = 0 ;\n    nz = Ap [n] ;\n\n    for (k = 0 ; k < n ; k++)\n    {\n\tp1 = Ap [k] ;\n\tp2 = Ap [k+1] ;\n\tAMD_DEBUG2 ((\"\\nAAT Column: \"ID\" p1: \"ID\" p2: \"ID\"\\n\", k, p1, p2)) ;\n\n\t/* construct A+A' */\n\tfor (p = p1 ; p < p2 ; )\n\t{\n\t    /* scan the upper triangular part of A */\n\t    j = Ai [p] ;\n\t    if (j < k)\n\t    {\n\t\t/* entry A (j,k) is in the strictly upper triangular part,\n\t\t * add both A (j,k) and A (k,j) to the matrix A+A' */\n\t\tLen [j]++ ;\n\t\tLen [k]++ ;\n\t\tAMD_DEBUG3 ((\"    upper (\"ID\",\"ID\") (\"ID\",\"ID\")\\n\", j,k, k,j));\n\t\tp++ ;\n\t    }\n\t    else if (j == k)\n\t    {\n\t\t/* skip the diagonal */\n\t\tp++ ;\n\t\tnzdiag++ ;\n\t\tbreak ;\n\t    }\n\t    else /* j > k */\n\t    {\n\t\t/* first entry below the diagonal */\n\t\tbreak ;\n\t    }\n\t    /* scan lower triangular part of A, in column j until reaching\n\t     * row k.  Start where last scan left off. */\n\t    ASSERT (Tp [j] != EMPTY) ;\n\t    ASSERT (Ap [j] <= Tp [j] && Tp [j] <= Ap [j+1]) ;\n\t    pj2 = Ap [j+1] ;\n\t    for (pj = Tp [j] ; pj < pj2 ; )\n\t    {\n\t\ti = Ai [pj] ;\n\t\tif (i < k)\n\t\t{\n\t\t    /* A (i,j) is only in the lower part, not in upper.\n\t\t     * add both A (i,j) and A (j,i) to the matrix A+A' */\n\t\t    Len [i]++ ;\n\t\t    Len [j]++ ;\n\t\t    AMD_DEBUG3 ((\"    lower (\"ID\",\"ID\") (\"ID\",\"ID\")\\n\",\n\t\t\ti,j, j,i)) ;\n\t\t    pj++ ;\n\t\t}\n\t\telse if (i == k)\n\t\t{\n\t\t    /* entry A (k,j) in lower part and A (j,k) in upper */\n\t\t    pj++ ;\n\t\t    nzboth++ ;\n\t\t    break ;\n\t\t}\n\t\telse /* i > k */\n\t\t{\n\t\t    /* consider this entry later, when k advances to i */\n\t\t    break ;\n\t\t}\n\t    }\n\t    Tp [j] = pj ;\n\t}\n\t/* Tp [k] points to the entry just below the diagonal in column k */\n\tTp [k] = p ;\n    }\n\n    /* clean up, for remaining mismatched entries */\n    for (j = 0 ; j < n ; j++)\n    {\n\tfor (pj = Tp [j] ; pj < Ap [j+1] ; pj++)\n\t{\n\t    i = Ai [pj] ;\n\t    /* A (i,j) is only in the lower part, not in upper.\n\t     * add both A (i,j) and A (j,i) to the matrix A+A' */\n\t    Len [i]++ ;\n\t    Len [j]++ ;\n\t    AMD_DEBUG3 ((\"    lower cleanup (\"ID\",\"ID\") (\"ID\",\"ID\")\\n\",\n\t\ti,j, j,i)) ;\n\t}\n    }\n\n    /* --------------------------------------------------------------------- */\n    /* compute the symmetry of the nonzero pattern of A */\n    /* --------------------------------------------------------------------- */\n\n    /* Given a matrix A, the symmetry of A is:\n     *\tB = tril (spones (A), -1) + triu (spones (A), 1) ;\n     *  sym = nnz (B & B') / nnz (B) ;\n     *  or 1 if nnz (B) is zero.\n     */\n\n    if (nz == nzdiag)\n    {\n\tsym = 1 ;\n    }\n    else\n    {\n\tsym = (2 * (c_float) nzboth) / ((c_float) (nz - nzdiag)) ;\n    }\n\n    nzaat = 0 ;\n    for (k = 0 ; k < n ; k++)\n    {\n\tnzaat += Len [k] ;\n    }\n\n    AMD_DEBUG1 ((\"AMD nz in A+A', excluding diagonal (nzaat) = %g\\n\",\n\t(c_float) nzaat)) ;\n    AMD_DEBUG1 ((\"   nzboth: \"ID\" nz: \"ID\" nzdiag: \"ID\" symmetry: %g\\n\",\n\t\tnzboth, nz, nzdiag, sym)) ;\n\n    if (Info != (c_float *) NULL)\n    {\n\tInfo [AMD_STATUS] = AMD_OK ;\n\tInfo [AMD_N] = n ;\n\tInfo [AMD_NZ] = nz ;\n\tInfo [AMD_SYMMETRY] = sym ;\t    /* symmetry of pattern of A */\n\tInfo [AMD_NZDIAG] = nzdiag ;\t    /* nonzeros on diagonal of A */\n\tInfo [AMD_NZ_A_PLUS_AT] = nzaat ;   /* nonzeros in A+A' */\n    }\n\n    return (nzaat) ;\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/amd/src/amd_control.c",
    "content": "/* ========================================================================= */\n/* === AMD_control ========================================================= */\n/* ========================================================================= */\n\n/* ------------------------------------------------------------------------- */\n/* AMD, Copyright (c) Timothy A. Davis,\t\t\t\t\t     */\n/* Patrick R. Amestoy, and Iain S. Duff.  See ../README.txt for License.     */\n/* email: DrTimothyAldenDavis@gmail.com                                      */\n/* ------------------------------------------------------------------------- */\n\n/* User-callable.  Prints the control parameters for AMD.  See amd.h\n * for details.  If the Control array is not present, the defaults are\n * printed instead.\n */\n\n#include \"amd_internal.h\"\n\nGLOBAL void AMD_control\n(\n    c_float Control [ ]\n)\n{\n    c_float alpha ;\n    Int aggressive ;\n\n    if (Control != (c_float *) NULL)\n    {\n\talpha = Control [AMD_DENSE] ;\n\taggressive = Control [AMD_AGGRESSIVE] != 0 ;\n    }\n    else\n    {\n\talpha = AMD_DEFAULT_DENSE ;\n\taggressive = AMD_DEFAULT_AGGRESSIVE ;\n    }\n\n    SUITESPARSE_PRINTF ((\n        \"\\nAMD version %d.%d.%d, %s: approximate minimum degree ordering\\n\"\n\t\"    dense row parameter: %g\\n\", AMD_MAIN_VERSION, AMD_SUB_VERSION,\n\tAMD_SUBSUB_VERSION, AMD_DATE, alpha)) ;\n\n    if (alpha < 0)\n    {\n\tSUITESPARSE_PRINTF ((\"    no rows treated as dense\\n\")) ;\n    }\n    else\n    {\n\tSUITESPARSE_PRINTF ((\n\t\"    (rows with more than max (%g * sqrt (n), 16) entries are\\n\"\n\t\"    considered \\\"dense\\\", and placed last in output permutation)\\n\",\n\talpha)) ;\n    }\n\n    if (aggressive)\n    {\n\tSUITESPARSE_PRINTF ((\"    aggressive absorption:  yes\\n\")) ;\n    }\n    else\n    {\n\tSUITESPARSE_PRINTF ((\"    aggressive absorption:  no\\n\")) ;\n    }\n\n    SUITESPARSE_PRINTF ((\"    size of AMD integer: %d\\n\\n\", sizeof (Int))) ;\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/amd/src/amd_defaults.c",
    "content": "/* ========================================================================= */\n/* === AMD_defaults ======================================================== */\n/* ========================================================================= */\n\n/* ------------------------------------------------------------------------- */\n/* AMD, Copyright (c) Timothy A. Davis,\t\t\t\t\t     */\n/* Patrick R. Amestoy, and Iain S. Duff.  See ../README.txt for License.     */\n/* email: DrTimothyAldenDavis@gmail.com                                      */\n/* ------------------------------------------------------------------------- */\n\n/* User-callable.  Sets default control parameters for AMD.  See amd.h\n * for details.\n */\n\n#include \"amd_internal.h\"\n\n/* ========================================================================= */\n/* === AMD defaults ======================================================== */\n/* ========================================================================= */\n\nGLOBAL void AMD_defaults\n(\n    c_float Control [ ]\n)\n{\n    Int i ;\n\n    if (Control != (c_float *) NULL)\n    {\n\tfor (i = 0 ; i < AMD_CONTROL ; i++)\n\t{\n\t    Control [i] = 0 ;\n\t}\n\tControl [AMD_DENSE] = AMD_DEFAULT_DENSE ;\n\tControl [AMD_AGGRESSIVE] = AMD_DEFAULT_AGGRESSIVE ;\n    }\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/amd/src/amd_info.c",
    "content": "/* ========================================================================= */\n/* === AMD_info ============================================================ */\n/* ========================================================================= */\n\n/* ------------------------------------------------------------------------- */\n/* AMD, Copyright (c) Timothy A. Davis,\t\t\t\t\t     */\n/* Patrick R. Amestoy, and Iain S. Duff.  See ../README.txt for License.     */\n/* email: DrTimothyAldenDavis@gmail.com                                      */\n/* ------------------------------------------------------------------------- */\n\n/* User-callable.  Prints the output statistics for AMD.  See amd.h\n * for details.  If the Info array is not present, nothing is printed.\n */\n\n#include \"amd_internal.h\"\n\n#define PRI(format,x) { if (x >= 0) { SUITESPARSE_PRINTF ((format, x)) ; }}\n\nGLOBAL void AMD_info\n(\n    c_float Info [ ]\n)\n{\n    c_float n, ndiv, nmultsubs_ldl, nmultsubs_lu, lnz, lnzd ;\n\n    SUITESPARSE_PRINTF ((\"\\nAMD version %d.%d.%d, %s, results:\\n\",\n\tAMD_MAIN_VERSION, AMD_SUB_VERSION, AMD_SUBSUB_VERSION, AMD_DATE)) ;\n\n    if (!Info)\n    {\n\treturn ;\n    }\n\n    n = Info [AMD_N] ;\n    ndiv = Info [AMD_NDIV] ;\n    nmultsubs_ldl = Info [AMD_NMULTSUBS_LDL] ;\n    nmultsubs_lu = Info [AMD_NMULTSUBS_LU] ;\n    lnz = Info [AMD_LNZ] ;\n    lnzd = (n >= 0 && lnz >= 0) ? (n + lnz) : (-1) ;\n\n    /* AMD return status */\n    SUITESPARSE_PRINTF ((\"    status: \")) ;\n    if (Info [AMD_STATUS] == AMD_OK)\n    {\n\tSUITESPARSE_PRINTF ((\"OK\\n\")) ;\n    }\n    else if (Info [AMD_STATUS] == AMD_OUT_OF_MEMORY)\n    {\n\tSUITESPARSE_PRINTF ((\"out of memory\\n\")) ;\n    }\n    else if (Info [AMD_STATUS] == AMD_INVALID)\n    {\n\tSUITESPARSE_PRINTF ((\"invalid matrix\\n\")) ;\n    }\n    else if (Info [AMD_STATUS] == AMD_OK_BUT_JUMBLED)\n    {\n\tSUITESPARSE_PRINTF ((\"OK, but jumbled\\n\")) ;\n    }\n    else\n    {\n\tSUITESPARSE_PRINTF ((\"unknown\\n\")) ;\n    }\n\n    /* statistics about the input matrix */\n    PRI (\"    n, dimension of A:                                  %.20g\\n\", n);\n    PRI (\"    nz, number of nonzeros in A:                        %.20g\\n\",\n\tInfo [AMD_NZ]) ;\n    PRI (\"    symmetry of A:                                      %.4f\\n\",\n\tInfo [AMD_SYMMETRY]) ;\n    PRI (\"    number of nonzeros on diagonal:                     %.20g\\n\",\n\tInfo [AMD_NZDIAG]) ;\n    PRI (\"    nonzeros in pattern of A+A' (excl. diagonal):       %.20g\\n\",\n\tInfo [AMD_NZ_A_PLUS_AT]) ;\n    PRI (\"    # dense rows/columns of A+A':                       %.20g\\n\",\n\tInfo [AMD_NDENSE]) ;\n\n    /* statistics about AMD's behavior  */\n    PRI (\"    memory used, in bytes:                              %.20g\\n\",\n\tInfo [AMD_MEMORY]) ;\n    PRI (\"    # of memory compactions:                            %.20g\\n\",\n\tInfo [AMD_NCMPA]) ;\n\n    /* statistics about the ordering quality */\n    SUITESPARSE_PRINTF ((\"\\n\"\n\t\"    The following approximate statistics are for a subsequent\\n\"\n\t\"    factorization of A(P,P) + A(P,P)'.  They are slight upper\\n\"\n\t\"    bounds if there are no dense rows/columns in A+A', and become\\n\"\n\t\"    looser if dense rows/columns exist.\\n\\n\")) ;\n\n    PRI (\"    nonzeros in L (excluding diagonal):                 %.20g\\n\",\n\tlnz) ;\n    PRI (\"    nonzeros in L (including diagonal):                 %.20g\\n\",\n\tlnzd) ;\n    PRI (\"    # divide operations for LDL' or LU:                 %.20g\\n\",\n\tndiv) ;\n    PRI (\"    # multiply-subtract operations for LDL':            %.20g\\n\",\n\tnmultsubs_ldl) ;\n    PRI (\"    # multiply-subtract operations for LU:              %.20g\\n\",\n\tnmultsubs_lu) ;\n    PRI (\"    max nz. in any column of L (incl. diagonal):        %.20g\\n\",\n\tInfo [AMD_DMAX]) ;\n\n    /* total flop counts for various factorizations */\n\n    if (n >= 0 && ndiv >= 0 && nmultsubs_ldl >= 0 && nmultsubs_lu >= 0)\n    {\n\tSUITESPARSE_PRINTF ((\"\\n\"\n\t\"    chol flop count for real A, sqrt counted as 1 flop: %.20g\\n\"\n\t\"    LDL' flop count for real A:                         %.20g\\n\"\n\t\"    LDL' flop count for complex A:                      %.20g\\n\"\n\t\"    LU flop count for real A (with no pivoting):        %.20g\\n\"\n\t\"    LU flop count for complex A (with no pivoting):     %.20g\\n\\n\",\n\tn + ndiv + 2*nmultsubs_ldl,\n\t    ndiv + 2*nmultsubs_ldl,\n\t  9*ndiv + 8*nmultsubs_ldl,\n\t    ndiv + 2*nmultsubs_lu,\n\t  9*ndiv + 8*nmultsubs_lu)) ;\n    }\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/amd/src/amd_order.c",
    "content": "/* ========================================================================= */\n/* === AMD_order =========================================================== */\n/* ========================================================================= */\n\n/* ------------------------------------------------------------------------- */\n/* AMD, Copyright (c) Timothy A. Davis,\t\t\t\t\t     */\n/* Patrick R. Amestoy, and Iain S. Duff.  See ../README.txt for License.     */\n/* email: DrTimothyAldenDavis@gmail.com                                      */\n/* ------------------------------------------------------------------------- */\n\n/* User-callable AMD minimum degree ordering routine.  See amd.h for\n * documentation.\n */\n\n#include \"amd_internal.h\"\n\n/* ========================================================================= */\n/* === AMD_order =========================================================== */\n/* ========================================================================= */\n\nGLOBAL Int AMD_order\n(\n    Int n,\n    const Int Ap [ ],\n    const Int Ai [ ],\n    Int P [ ],\n    c_float Control [ ],\n    c_float Info [ ]\n)\n{\n    Int *Len, *S, nz, i, *Pinv, info, status, *Rp, *Ri, *Cp, *Ci, ok ;\n    size_t nzaat, slen ;\n    c_float mem = 0 ;\n\n#ifndef NDEBUG\n    AMD_debug_init (\"amd\") ;\n#endif\n\n    /* clear the Info array, if it exists */\n    info = Info != (c_float *) NULL ;\n    if (info)\n    {\n\tfor (i = 0 ; i < AMD_INFO ; i++)\n\t{\n\t    Info [i] = EMPTY ;\n\t}\n\tInfo [AMD_N] = n ;\n\tInfo [AMD_STATUS] = AMD_OK ;\n    }\n\n    /* make sure inputs exist and n is >= 0 */\n    if (Ai == (Int *) NULL || Ap == (Int *) NULL || P == (Int *) NULL || n < 0)\n    {\n\tif (info) Info [AMD_STATUS] = AMD_INVALID ;\n\treturn (AMD_INVALID) ;\t    /* arguments are invalid */\n    }\n\n    if (n == 0)\n    {\n\treturn (AMD_OK) ;\t    /* n is 0 so there's nothing to do */\n    }\n\n    nz = Ap [n] ;\n    if (info)\n    {\n\tInfo [AMD_NZ] = nz ;\n    }\n    if (nz < 0)\n    {\n\tif (info) Info [AMD_STATUS] = AMD_INVALID ;\n\treturn (AMD_INVALID) ;\n    }\n\n    /* check if n or nz will cause size_t overflow */\n    if (((size_t) n) >= SIZE_T_MAX / sizeof (Int)\n     || ((size_t) nz) >= SIZE_T_MAX / sizeof (Int))\n    {\n\tif (info) Info [AMD_STATUS] = AMD_OUT_OF_MEMORY ;\n\treturn (AMD_OUT_OF_MEMORY) ;\t    /* problem too large */\n    }\n\n    /* check the input matrix:\tAMD_OK, AMD_INVALID, or AMD_OK_BUT_JUMBLED */\n    status = AMD_valid (n, n, Ap, Ai) ;\n\n    if (status == AMD_INVALID)\n    {\n\tif (info) Info [AMD_STATUS] = AMD_INVALID ;\n\treturn (AMD_INVALID) ;\t    /* matrix is invalid */\n    }\n\n    /* allocate two size-n integer workspaces */\n    Len  = SuiteSparse_malloc (n, sizeof (Int)) ;\n    Pinv = SuiteSparse_malloc (n, sizeof (Int)) ;\n    mem += n ;\n    mem += n ;\n    if (!Len || !Pinv)\n    {\n\t/* :: out of memory :: */\n\tSuiteSparse_free (Len) ;\n\tSuiteSparse_free (Pinv) ;\n\tif (info) Info [AMD_STATUS] = AMD_OUT_OF_MEMORY ;\n\treturn (AMD_OUT_OF_MEMORY) ;\n    }\n\n    if (status == AMD_OK_BUT_JUMBLED)\n    {\n\t/* sort the input matrix and remove duplicate entries */\n\tAMD_DEBUG1 ((\"Matrix is jumbled\\n\")) ;\n\tRp = SuiteSparse_malloc (n+1, sizeof (Int)) ;\n\tRi = SuiteSparse_malloc (nz,  sizeof (Int)) ;\n\tmem += (n+1) ;\n\tmem += MAX (nz,1) ;\n\tif (!Rp || !Ri)\n\t{\n\t    /* :: out of memory :: */\n\t    SuiteSparse_free (Rp) ;\n\t    SuiteSparse_free (Ri) ;\n\t    SuiteSparse_free (Len) ;\n\t    SuiteSparse_free (Pinv) ;\n\t    if (info) Info [AMD_STATUS] = AMD_OUT_OF_MEMORY ;\n\t    return (AMD_OUT_OF_MEMORY) ;\n\t}\n\t/* use Len and Pinv as workspace to create R = A' */\n\tAMD_preprocess (n, Ap, Ai, Rp, Ri, Len, Pinv) ;\n\tCp = Rp ;\n\tCi = Ri ;\n    }\n    else\n    {\n\t/* order the input matrix as-is.  No need to compute R = A' first */\n\tRp = NULL ;\n\tRi = NULL ;\n\tCp = (Int *) Ap ;\n\tCi = (Int *) Ai ;\n    }\n\n    /* --------------------------------------------------------------------- */\n    /* determine the symmetry and count off-diagonal nonzeros in A+A' */\n    /* --------------------------------------------------------------------- */\n\n    nzaat = AMD_aat (n, Cp, Ci, Len, P, Info) ;\n    AMD_DEBUG1 ((\"nzaat: %g\\n\", (c_float) nzaat)) ;\n    ASSERT ((MAX (nz-n, 0) <= nzaat) && (nzaat <= 2 * (size_t) nz)) ;\n\n    /* --------------------------------------------------------------------- */\n    /* allocate workspace for matrix, elbow room, and 6 size-n vectors */\n    /* --------------------------------------------------------------------- */\n\n    S = NULL ;\n    slen = nzaat ;\t\t\t/* space for matrix */\n    ok = ((slen + nzaat/5) >= slen) ;\t/* check for size_t overflow */\n    slen += nzaat/5 ;\t\t\t/* add elbow room */\n    for (i = 0 ; ok && i < 7 ; i++)\n    {\n\tok = ((slen + n) > slen) ;\t/* check for size_t overflow */\n\tslen += n ;\t\t\t/* size-n elbow room, 6 size-n work */\n    }\n    mem += slen ;\n    ok = ok && (slen < SIZE_T_MAX / sizeof (Int)) ; /* check for overflow */\n    ok = ok && (slen < Int_MAX) ;\t/* S[i] for Int i must be OK */\n    if (ok)\n    {\n\tS = SuiteSparse_malloc (slen, sizeof (Int)) ;\n    }\n    AMD_DEBUG1 ((\"slen %g\\n\", (c_float) slen)) ;\n    if (!S)\n    {\n\t/* :: out of memory :: (or problem too large) */\n\tSuiteSparse_free (Rp) ;\n\tSuiteSparse_free (Ri) ;\n\tSuiteSparse_free (Len) ;\n\tSuiteSparse_free (Pinv) ;\n\tif (info) Info [AMD_STATUS] = AMD_OUT_OF_MEMORY ;\n\treturn (AMD_OUT_OF_MEMORY) ;\n    }\n    if (info)\n    {\n\t/* memory usage, in bytes. */\n\tInfo [AMD_MEMORY] = mem * sizeof (Int) ;\n    }\n\n    /* --------------------------------------------------------------------- */\n    /* order the matrix */\n    /* --------------------------------------------------------------------- */\n\n    AMD_1 (n, Cp, Ci, P, Pinv, Len, slen, S, Control, Info) ;\n\n    /* --------------------------------------------------------------------- */\n    /* free the workspace */\n    /* --------------------------------------------------------------------- */\n\n    SuiteSparse_free (Rp) ;\n    SuiteSparse_free (Ri) ;\n    SuiteSparse_free (Len) ;\n    SuiteSparse_free (Pinv) ;\n    SuiteSparse_free (S) ;\n    if (info) Info [AMD_STATUS] = status ;\n    return (status) ;\t    /* successful ordering */\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/amd/src/amd_post_tree.c",
    "content": "/* ========================================================================= */\n/* === AMD_post_tree ======================================================= */\n/* ========================================================================= */\n\n/* ------------------------------------------------------------------------- */\n/* AMD, Copyright (c) Timothy A. Davis,\t\t\t\t\t     */\n/* Patrick R. Amestoy, and Iain S. Duff.  See ../README.txt for License.     */\n/* email: DrTimothyAldenDavis@gmail.com                                      */\n/* ------------------------------------------------------------------------- */\n\n/* Post-ordering of a supernodal elimination tree.  */\n\n#include \"amd_internal.h\"\n\nGLOBAL Int AMD_post_tree\n(\n    Int root,\t\t\t/* root of the tree */\n    Int k,\t\t\t/* start numbering at k */\n    Int Child [ ],\t\t/* input argument of size nn, undefined on\n\t\t\t\t * output.  Child [i] is the head of a link\n\t\t\t\t * list of all nodes that are children of node\n\t\t\t\t * i in the tree. */\n    const Int Sibling [ ],\t/* input argument of size nn, not modified.\n\t\t\t\t * If f is a node in the link list of the\n\t\t\t\t * children of node i, then Sibling [f] is the\n\t\t\t\t * next child of node i.\n\t\t\t\t */\n    Int Order [ ],\t\t/* output order, of size nn.  Order [i] = k\n\t\t\t\t * if node i is the kth node of the reordered\n\t\t\t\t * tree. */\n    Int Stack [ ]\t\t/* workspace of size nn */\n#ifndef NDEBUG\n    , Int nn\t\t\t/* nodes are in the range 0..nn-1. */\n#endif\n)\n{\n    Int f, head, h, i ;\n\n#if 0\n    /* --------------------------------------------------------------------- */\n    /* recursive version (Stack [ ] is not used): */\n    /* --------------------------------------------------------------------- */\n\n    /* this is simple, but can caouse stack overflow if nn is large */\n    i = root ;\n    for (f = Child [i] ; f != EMPTY ; f = Sibling [f])\n    {\n\tk = AMD_post_tree (f, k, Child, Sibling, Order, Stack, nn) ;\n    }\n    Order [i] = k++ ;\n    return (k) ;\n#endif\n\n    /* --------------------------------------------------------------------- */\n    /* non-recursive version, using an explicit stack */\n    /* --------------------------------------------------------------------- */\n\n    /* push root on the stack */\n    head = 0 ;\n    Stack [0] = root ;\n\n    while (head >= 0)\n    {\n\t/* get head of stack */\n\tASSERT (head < nn) ;\n\ti = Stack [head] ;\n\tAMD_DEBUG1 ((\"head of stack \"ID\" \\n\", i)) ;\n\tASSERT (i >= 0 && i < nn) ;\n\n\tif (Child [i] != EMPTY)\n\t{\n\t    /* the children of i are not yet ordered */\n\t    /* push each child onto the stack in reverse order */\n\t    /* so that small ones at the head of the list get popped first */\n\t    /* and the biggest one at the end of the list gets popped last */\n\t    for (f = Child [i] ; f != EMPTY ; f = Sibling [f])\n\t    {\n\t\thead++ ;\n\t\tASSERT (head < nn) ;\n\t\tASSERT (f >= 0 && f < nn) ;\n\t    }\n\t    h = head ;\n\t    ASSERT (head < nn) ;\n\t    for (f = Child [i] ; f != EMPTY ; f = Sibling [f])\n\t    {\n\t\tASSERT (h > 0) ;\n\t\tStack [h--] = f ;\n\t\tAMD_DEBUG1 ((\"push \"ID\" on stack\\n\", f)) ;\n\t\tASSERT (f >= 0 && f < nn) ;\n\t    }\n\t    ASSERT (Stack [h] == i) ;\n\n\t    /* delete child list so that i gets ordered next time we see it */\n\t    Child [i] = EMPTY ;\n\t}\n\telse\n\t{\n\t    /* the children of i (if there were any) are already ordered */\n\t    /* remove i from the stack and order it.  Front i is kth front */\n\t    head-- ;\n\t    AMD_DEBUG1 ((\"pop \"ID\" order \"ID\"\\n\", i, k)) ;\n\t    Order [i] = k++ ;\n\t    ASSERT (k <= nn) ;\n\t}\n\n#ifndef NDEBUG\n\tAMD_DEBUG1 ((\"\\nStack:\")) ;\n\tfor (h = head ; h >= 0 ; h--)\n\t{\n\t    Int j = Stack [h] ;\n\t    AMD_DEBUG1 ((\" \"ID, j)) ;\n\t    ASSERT (j >= 0 && j < nn) ;\n\t}\n\tAMD_DEBUG1 ((\"\\n\\n\")) ;\n\tASSERT (head < nn) ;\n#endif\n\n    }\n    return (k) ;\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/amd/src/amd_postorder.c",
    "content": "/* ========================================================================= */\n/* === AMD_postorder ======================================================= */\n/* ========================================================================= */\n\n/* ------------------------------------------------------------------------- */\n/* AMD, Copyright (c) Timothy A. Davis,\t\t\t\t\t     */\n/* Patrick R. Amestoy, and Iain S. Duff.  See ../README.txt for License.     */\n/* email: DrTimothyAldenDavis@gmail.com                                      */\n/* ------------------------------------------------------------------------- */\n\n/* Perform a postordering (via depth-first search) of an assembly tree. */\n\n#include \"amd_internal.h\"\n\nGLOBAL void AMD_postorder\n(\n    /* inputs, not modified on output: */\n    Int nn,\t\t/* nodes are in the range 0..nn-1 */\n    Int Parent [ ],\t/* Parent [j] is the parent of j, or EMPTY if root */\n    Int Nv [ ],\t\t/* Nv [j] > 0 number of pivots represented by node j,\n\t\t\t * or zero if j is not a node. */\n    Int Fsize [ ],\t/* Fsize [j]: size of node j */\n\n    /* output, not defined on input: */\n    Int Order [ ],\t/* output post-order */\n\n    /* workspaces of size nn: */\n    Int Child [ ],\n    Int Sibling [ ],\n    Int Stack [ ]\n)\n{\n    Int i, j, k, parent, frsize, f, fprev, maxfrsize, bigfprev, bigf, fnext ;\n\n    for (j = 0 ; j < nn ; j++)\n    {\n\tChild [j] = EMPTY ;\n\tSibling [j] = EMPTY ;\n    }\n\n    /* --------------------------------------------------------------------- */\n    /* place the children in link lists - bigger elements tend to be last */\n    /* --------------------------------------------------------------------- */\n\n    for (j = nn-1 ; j >= 0 ; j--)\n    {\n\tif (Nv [j] > 0)\n\t{\n\t    /* this is an element */\n\t    parent = Parent [j] ;\n\t    if (parent != EMPTY)\n\t    {\n\t\t/* place the element in link list of the children its parent */\n\t\t/* bigger elements will tend to be at the end of the list */\n\t\tSibling [j] = Child [parent] ;\n\t\tChild [parent] = j ;\n\t    }\n\t}\n    }\n\n#ifndef NDEBUG\n    {\n\tInt nels, ff, nchild ;\n\tAMD_DEBUG1 ((\"\\n\\n================================ AMD_postorder:\\n\"));\n\tnels = 0 ;\n\tfor (j = 0 ; j < nn ; j++)\n\t{\n\t    if (Nv [j] > 0)\n\t    {\n\t\tAMD_DEBUG1 (( \"\"ID\" :  nels \"ID\" npiv \"ID\" size \"ID\n\t\t    \" parent \"ID\" maxfr \"ID\"\\n\", j, nels,\n\t\t    Nv [j], Fsize [j], Parent [j], Fsize [j])) ;\n\t\t/* this is an element */\n\t\t/* dump the link list of children */\n\t\tnchild = 0 ;\n\t\tAMD_DEBUG1 ((\"    Children: \")) ;\n\t\tfor (ff = Child [j] ; ff != EMPTY ; ff = Sibling [ff])\n\t\t{\n\t\t    AMD_DEBUG1 ((ID\" \", ff)) ;\n\t\t    ASSERT (Parent [ff] == j) ;\n\t\t    nchild++ ;\n\t\t    ASSERT (nchild < nn) ;\n\t\t}\n\t\tAMD_DEBUG1 ((\"\\n\")) ;\n\t\tparent = Parent [j] ;\n\t\tif (parent != EMPTY)\n\t\t{\n\t\t    ASSERT (Nv [parent] > 0) ;\n\t\t}\n\t\tnels++ ;\n\t    }\n\t}\n    }\n    AMD_DEBUG1 ((\"\\n\\nGo through the children of each node, and put\\n\"\n\t\t \"the biggest child last in each list:\\n\")) ;\n#endif\n\n    /* --------------------------------------------------------------------- */\n    /* place the largest child last in the list of children for each node */\n    /* --------------------------------------------------------------------- */\n\n    for (i = 0 ; i < nn ; i++)\n    {\n\tif (Nv [i] > 0 && Child [i] != EMPTY)\n\t{\n\n#ifndef NDEBUG\n\t    Int nchild ;\n\t    AMD_DEBUG1 ((\"Before partial sort, element \"ID\"\\n\", i)) ;\n\t    nchild = 0 ;\n\t    for (f = Child [i] ; f != EMPTY ; f = Sibling [f])\n\t    {\n\t\tASSERT (f >= 0 && f < nn) ;\n\t\tAMD_DEBUG1 ((\"      f: \"ID\"  size: \"ID\"\\n\", f, Fsize [f])) ;\n\t\tnchild++ ;\n\t\tASSERT (nchild <= nn) ;\n\t    }\n#endif\n\n\t    /* find the biggest element in the child list */\n\t    fprev = EMPTY ;\n\t    maxfrsize = EMPTY ;\n\t    bigfprev = EMPTY ;\n\t    bigf = EMPTY ;\n\t    for (f = Child [i] ; f != EMPTY ; f = Sibling [f])\n\t    {\n\t\tASSERT (f >= 0 && f < nn) ;\n\t\tfrsize = Fsize [f] ;\n\t\tif (frsize >= maxfrsize)\n\t\t{\n\t\t    /* this is the biggest seen so far */\n\t\t    maxfrsize = frsize ;\n\t\t    bigfprev = fprev ;\n\t\t    bigf = f ;\n\t\t}\n\t\tfprev = f ;\n\t    }\n\t    ASSERT (bigf != EMPTY) ;\n\n\t    fnext = Sibling [bigf] ;\n\n\t    AMD_DEBUG1 ((\"bigf \"ID\" maxfrsize \"ID\" bigfprev \"ID\" fnext \"ID\n\t\t\" fprev \" ID\"\\n\", bigf, maxfrsize, bigfprev, fnext, fprev)) ;\n\n\t    if (fnext != EMPTY)\n\t    {\n\t\t/* if fnext is EMPTY then bigf is already at the end of list */\n\n\t\tif (bigfprev == EMPTY)\n\t\t{\n\t\t    /* delete bigf from the element of the list */\n\t\t    Child [i] = fnext ;\n\t\t}\n\t\telse\n\t\t{\n\t\t    /* delete bigf from the middle of the list */\n\t\t    Sibling [bigfprev] = fnext ;\n\t\t}\n\n\t\t/* put bigf at the end of the list */\n\t\tSibling [bigf] = EMPTY ;\n\t\tASSERT (Child [i] != EMPTY) ;\n\t\tASSERT (fprev != bigf) ;\n\t\tASSERT (fprev != EMPTY) ;\n\t\tSibling [fprev] = bigf ;\n\t    }\n\n#ifndef NDEBUG\n\t    AMD_DEBUG1 ((\"After partial sort, element \"ID\"\\n\", i)) ;\n\t    for (f = Child [i] ; f != EMPTY ; f = Sibling [f])\n\t    {\n\t\tASSERT (f >= 0 && f < nn) ;\n\t\tAMD_DEBUG1 ((\"        \"ID\"  \"ID\"\\n\", f, Fsize [f])) ;\n\t\tASSERT (Nv [f] > 0) ;\n\t\tnchild-- ;\n\t    }\n\t    ASSERT (nchild == 0) ;\n#endif\n\n\t}\n    }\n\n    /* --------------------------------------------------------------------- */\n    /* postorder the assembly tree */\n    /* --------------------------------------------------------------------- */\n\n    for (i = 0 ; i < nn ; i++)\n    {\n\tOrder [i] = EMPTY ;\n    }\n\n    k = 0 ;\n\n    for (i = 0 ; i < nn ; i++)\n    {\n\tif (Parent [i] == EMPTY && Nv [i] > 0)\n\t{\n\t    AMD_DEBUG1 ((\"Root of assembly tree \"ID\"\\n\", i)) ;\n\t    k = AMD_post_tree (i, k, Child, Sibling, Order, Stack\n#ifndef NDEBUG\n\t\t, nn\n#endif\n\t\t) ;\n\t}\n    }\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/amd/src/amd_preprocess.c",
    "content": "/* ========================================================================= */\n/* === AMD_preprocess ====================================================== */\n/* ========================================================================= */\n\n/* ------------------------------------------------------------------------- */\n/* AMD, Copyright (c) Timothy A. Davis,\t\t\t\t\t     */\n/* Patrick R. Amestoy, and Iain S. Duff.  See ../README.txt for License.     */\n/* email: DrTimothyAldenDavis@gmail.com                                      */\n/* ------------------------------------------------------------------------- */\n\n/* Sorts, removes duplicate entries, and transposes from the nonzero pattern of\n * a column-form matrix A, to obtain the matrix R.  The input matrix can have\n * duplicate entries and/or unsorted columns (AMD_valid (n,Ap,Ai) must not be\n * AMD_INVALID).\n *\n * This input condition is NOT checked.  This routine is not user-callable.\n */\n\n#include \"amd_internal.h\"\n\n/* ========================================================================= */\n/* === AMD_preprocess ====================================================== */\n/* ========================================================================= */\n\n/* AMD_preprocess does not check its input for errors or allocate workspace.\n * On input, the condition (AMD_valid (n,n,Ap,Ai) != AMD_INVALID) must hold.\n */\n\nGLOBAL void AMD_preprocess\n(\n    Int n,\t\t/* input matrix: A is n-by-n */\n    const Int Ap [ ],\t/* size n+1 */\n    const Int Ai [ ],\t/* size nz = Ap [n] */\n\n    /* output matrix R: */\n    Int Rp [ ],\t\t/* size n+1 */\n    Int Ri [ ],\t\t/* size nz (or less, if duplicates present) */\n\n    Int W [ ],\t\t/* workspace of size n */\n    Int Flag [ ]\t/* workspace of size n */\n)\n{\n\n    /* --------------------------------------------------------------------- */\n    /* local variables */\n    /* --------------------------------------------------------------------- */\n\n    Int i, j, p, p2 ;\n\n    ASSERT (AMD_valid (n, n, Ap, Ai) != AMD_INVALID) ;\n\n    /* --------------------------------------------------------------------- */\n    /* count the entries in each row of A (excluding duplicates) */\n    /* --------------------------------------------------------------------- */\n\n    for (i = 0 ; i < n ; i++)\n    {\n\tW [i] = 0 ;\t\t/* # of nonzeros in row i (excl duplicates) */\n\tFlag [i] = EMPTY ;\t/* Flag [i] = j if i appears in column j */\n    }\n    for (j = 0 ; j < n ; j++)\n    {\n\tp2 = Ap [j+1] ;\n\tfor (p = Ap [j] ; p < p2 ; p++)\n\t{\n\t    i = Ai [p] ;\n\t    if (Flag [i] != j)\n\t    {\n\t\t/* row index i has not yet appeared in column j */\n\t\tW [i]++ ;\t    /* one more entry in row i */\n\t\tFlag [i] = j ;\t    /* flag row index i as appearing in col j*/\n\t    }\n\t}\n    }\n\n    /* --------------------------------------------------------------------- */\n    /* compute the row pointers for R */\n    /* --------------------------------------------------------------------- */\n\n    Rp [0] = 0 ;\n    for (i = 0 ; i < n ; i++)\n    {\n\tRp [i+1] = Rp [i] + W [i] ;\n    }\n    for (i = 0 ; i < n ; i++)\n    {\n\tW [i] = Rp [i] ;\n\tFlag [i] = EMPTY ;\n    }\n\n    /* --------------------------------------------------------------------- */\n    /* construct the row form matrix R */\n    /* --------------------------------------------------------------------- */\n\n    /* R = row form of pattern of A */\n    for (j = 0 ; j < n ; j++)\n    {\n\tp2 = Ap [j+1] ;\n\tfor (p = Ap [j] ; p < p2 ; p++)\n\t{\n\t    i = Ai [p] ;\n\t    if (Flag [i] != j)\n\t    {\n\t\t/* row index i has not yet appeared in column j */\n\t\tRi [W [i]++] = j ;  /* put col j in row i */\n\t\tFlag [i] = j ;\t    /* flag row index i as appearing in col j*/\n\t    }\n\t}\n    }\n\n#ifndef NDEBUG\n    ASSERT (AMD_valid (n, n, Rp, Ri) == AMD_OK) ;\n    for (j = 0 ; j < n ; j++)\n    {\n\tASSERT (W [j] == Rp [j+1]) ;\n    }\n#endif\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/amd/src/amd_valid.c",
    "content": "/* ========================================================================= */\n/* === AMD_valid =========================================================== */\n/* ========================================================================= */\n\n/* ------------------------------------------------------------------------- */\n/* AMD, Copyright (c) Timothy A. Davis,\t\t\t\t\t     */\n/* Patrick R. Amestoy, and Iain S. Duff.  See ../README.txt for License.     */\n/* email: DrTimothyAldenDavis@gmail.com                                      */\n/* ------------------------------------------------------------------------- */\n\n/* Check if a column-form matrix is valid or not.  The matrix A is\n * n_row-by-n_col.  The row indices of entries in column j are in\n * Ai [Ap [j] ... Ap [j+1]-1].  Required conditions are:\n *\n *\tn_row >= 0\n *\tn_col >= 0\n *\tnz = Ap [n_col] >= 0\t    number of entries in the matrix\n *\tAp [0] == 0\n *\tAp [j] <= Ap [j+1] for all j in the range 0 to n_col.\n *      Ai [0 ... nz-1] must be in the range 0 to n_row-1.\n *\n * If any of the above conditions hold, AMD_INVALID is returned.  If the\n * following condition holds, AMD_OK_BUT_JUMBLED is returned (a warning,\n * not an error):\n *\n *\trow indices in Ai [Ap [j] ... Ap [j+1]-1] are not sorted in ascending\n *\t    order, and/or duplicate entries exist.\n *\n * Otherwise, AMD_OK is returned.\n *\n * In v1.2 and earlier, this function returned TRUE if the matrix was valid\n * (now returns AMD_OK), or FALSE otherwise (now returns AMD_INVALID or\n * AMD_OK_BUT_JUMBLED).\n */\n\n#include \"amd_internal.h\"\n\nGLOBAL Int AMD_valid\n(\n    /* inputs, not modified on output: */\n    Int n_row,\t\t/* A is n_row-by-n_col */\n    Int n_col,\n    const Int Ap [ ],\t/* column pointers of A, of size n_col+1 */\n    const Int Ai [ ]\t/* row indices of A, of size nz = Ap [n_col] */\n)\n{\n    Int nz, j, p1, p2, ilast, i, p, result = AMD_OK ;\n\n    if (n_row < 0 || n_col < 0 || Ap == NULL || Ai == NULL)\n    {\n\treturn (AMD_INVALID) ;\n    }\n    nz = Ap [n_col] ;\n    if (Ap [0] != 0 || nz < 0)\n    {\n\t/* column pointers must start at Ap [0] = 0, and Ap [n] must be >= 0 */\n\tAMD_DEBUG0 ((\"column 0 pointer bad or nz < 0\\n\")) ;\n\treturn (AMD_INVALID) ;\n    }\n    for (j = 0 ; j < n_col ; j++)\n    {\n\tp1 = Ap [j] ;\n\tp2 = Ap [j+1] ;\n\tAMD_DEBUG2 ((\"\\nColumn: \"ID\" p1: \"ID\" p2: \"ID\"\\n\", j, p1, p2)) ;\n\tif (p1 > p2)\n\t{\n\t    /* column pointers must be ascending */\n\t    AMD_DEBUG0 ((\"column \"ID\" pointer bad\\n\", j)) ;\n\t    return (AMD_INVALID) ;\n\t}\n\tilast = EMPTY ;\n\tfor (p = p1 ; p < p2 ; p++)\n\t{\n\t    i = Ai [p] ;\n\t    AMD_DEBUG3 ((\"row: \"ID\"\\n\", i)) ;\n\t    if (i < 0 || i >= n_row)\n\t    {\n\t\t/* row index out of range */\n\t\tAMD_DEBUG0 ((\"index out of range, col \"ID\" row \"ID\"\\n\", j, i));\n\t\treturn (AMD_INVALID) ;\n\t    }\n\t    if (i <= ilast)\n\t    {\n\t\t/* row index unsorted, or duplicate entry present */\n\t\tAMD_DEBUG1 ((\"index unsorted/dupl col \"ID\" row \"ID\"\\n\", j, i));\n\t\tresult = AMD_OK_BUT_JUMBLED ;\n\t    }\n\t    ilast = i ;\n\t}\n    }\n    return (result) ;\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_interface.c",
    "content": "#include \"glob_opts.h\"\n\n#include \"qdldl.h\"\n#include \"qdldl_interface.h\"\n\n#ifndef EMBEDDED\n#include \"amd.h\"\n#endif\n\n#if EMBEDDED != 1\n#include \"kkt.h\"\n#endif\n\n#ifndef EMBEDDED\n\n// Free LDL Factorization structure\nvoid free_linsys_solver_qdldl(qdldl_solver *s) {\n    if (s) {\n        if (s->L)           csc_spfree(s->L);\n        if (s->P)           c_free(s->P);\n        if (s->Dinv)        c_free(s->Dinv);\n        if (s->bp)          c_free(s->bp);\n        if (s->sol)         c_free(s->sol);\n        if (s->rho_inv_vec) c_free(s->rho_inv_vec);\n\n        // These are required for matrix updates\n        if (s->Pdiag_idx) c_free(s->Pdiag_idx);\n        if (s->KKT)       csc_spfree(s->KKT);\n        if (s->PtoKKT)    c_free(s->PtoKKT);\n        if (s->AtoKKT)    c_free(s->AtoKKT);\n        if (s->rhotoKKT)  c_free(s->rhotoKKT);\n\n        // QDLDL workspace\n        if (s->D)         c_free(s->D);\n        if (s->etree)     c_free(s->etree);\n        if (s->Lnz)       c_free(s->Lnz);\n        if (s->iwork)     c_free(s->iwork);\n        if (s->bwork)     c_free(s->bwork);\n        if (s->fwork)     c_free(s->fwork);\n        c_free(s);\n\n    }\n}\n\n\n/**\n * Compute LDL factorization of matrix A\n * @param  A    Matrix to be factorized\n * @param  p    Private workspace\n * @param  nvar Number of QP variables\n * @return      exitstatus (0 is good)\n */\nstatic c_int LDL_factor(csc *A,  qdldl_solver * p, c_int nvar){\n\n    c_int sum_Lnz;\n    c_int factor_status;\n\n    // Compute elimination tree\n    sum_Lnz = QDLDL_etree(A->n, A->p, A->i, p->iwork, p->Lnz, p->etree);\n\n    if (sum_Lnz < 0){\n      // Error\n#ifdef PRINTING\n      c_eprint(\"Error in KKT matrix LDL factorization when computing the elimination tree. A is not perfectly upper triangular\");\n#endif\n      return sum_Lnz;\n    }\n\n    // Allocate memory for Li and Lx\n    p->L->i = (c_int *)c_malloc(sizeof(c_int)*sum_Lnz);\n    p->L->x = (c_float *)c_malloc(sizeof(c_float)*sum_Lnz);\n\n    // Factor matrix\n    factor_status = QDLDL_factor(A->n, A->p, A->i, A->x,\n                                 p->L->p, p->L->i, p->L->x,\n                                 p->D, p->Dinv, p->Lnz,\n                                 p->etree, p->bwork, p->iwork, p->fwork);\n\n\n    if (factor_status < 0){\n      // Error\n#ifdef PRINTING\n      c_eprint(\"Error in KKT matrix LDL factorization when computing the nonzero elements. There are zeros in the diagonal matrix\");\n#endif\n      return factor_status;\n    } else if (factor_status < nvar) {\n      // Error: Number of positive elements of D should be equal to nvar\n#ifdef PRINTING\n      c_eprint(\"Error in KKT matrix LDL factorization when computing the nonzero elements. The problem seems to be non-convex\");\n#endif\n      return -2;\n    }\n\n    return 0;\n\n}\n\n\nstatic c_int permute_KKT(csc ** KKT, qdldl_solver * p, c_int Pnz, c_int Anz, c_int m, c_int * PtoKKT, c_int * AtoKKT, c_int * rhotoKKT){\n    c_float *info;\n    c_int amd_status;\n    c_int * Pinv;\n    csc *KKT_temp;\n    c_int * KtoPKPt;\n    c_int i; // Indexing\n\n    info = (c_float *)c_malloc(AMD_INFO * sizeof(c_float));\n\n    // Compute permutation matrix P using AMD\n#ifdef DLONG\n    amd_status = amd_l_order((*KKT)->n, (*KKT)->p, (*KKT)->i, p->P, (c_float *)OSQP_NULL, info);\n#else\n    amd_status = amd_order((*KKT)->n, (*KKT)->p, (*KKT)->i, p->P, (c_float *)OSQP_NULL, info);\n#endif\n    if (amd_status < 0) {\n        // Free Amd info and return an error\n        c_free(info);\n        return amd_status;\n    }\n\n\n    // Inverse of the permutation vector\n    Pinv = csc_pinv(p->P, (*KKT)->n);\n\n    // Permute KKT matrix\n    if (!PtoKKT && !AtoKKT && !rhotoKKT){  // No vectors to be stored\n        // Assign values of mapping\n        KKT_temp = csc_symperm((*KKT), Pinv, OSQP_NULL, 1);\n    }\n    else {\n        // Allocate vector of mappings from unpermuted to permuted\n        KtoPKPt = c_malloc((*KKT)->p[(*KKT)->n] * sizeof(c_int));\n        KKT_temp = csc_symperm((*KKT), Pinv, KtoPKPt, 1);\n\n        // Update vectors PtoKKT, AtoKKT and rhotoKKT\n        if (PtoKKT){\n            for (i = 0; i < Pnz; i++){\n                PtoKKT[i] = KtoPKPt[PtoKKT[i]];\n            }\n        }\n        if (AtoKKT){\n            for (i = 0; i < Anz; i++){\n                AtoKKT[i] = KtoPKPt[AtoKKT[i]];\n            }\n        }\n        if (rhotoKKT){\n            for (i = 0; i < m; i++){\n                rhotoKKT[i] = KtoPKPt[rhotoKKT[i]];\n            }\n        }\n\n        // Cleanup vector of mapping\n        c_free(KtoPKPt);\n    }\n\n    // Cleanup\n    // Free previous KKT matrix and assign pointer to new one\n    csc_spfree((*KKT));\n    (*KKT) = KKT_temp;\n    // Free Pinv\n    c_free(Pinv);\n    // Free Amd info\n    c_free(info);\n\n    return 0;\n}\n\n\n// Initialize LDL Factorization structure\nc_int init_linsys_solver_qdldl(qdldl_solver ** sp, const csc * P, const csc * A, c_float sigma, const c_float * rho_vec, c_int polish){\n\n    // Define Variables\n    csc * KKT_temp;     // Temporary KKT pointer\n    c_int i;            // Loop counter\n    c_int n_plus_m;     // Define n_plus_m dimension\n\n    // Allocate private structure to store KKT factorization\n    qdldl_solver *s;\n    s = c_calloc(1, sizeof(qdldl_solver));\n    *sp = s;\n\n    // Size of KKT\n    s->n = P->n;\n    s->m = A->m;\n    n_plus_m = s->n + s->m;\n\n    // Sigma parameter\n    s->sigma = sigma;\n\n    // Polishing flag\n    s->polish = polish;\n\n    // Link Functions\n    s->solve = &solve_linsys_qdldl;\n\n#ifndef EMBEDDED\n    s->free = &free_linsys_solver_qdldl;\n#endif\n\n#if EMBEDDED != 1\n    s->update_matrices = &update_linsys_solver_matrices_qdldl;\n    s->update_rho_vec = &update_linsys_solver_rho_vec_qdldl;\n#endif\n\n    // Assign type\n    s->type = QDLDL_SOLVER;\n\n    // Set number of threads to 1 (single threaded)\n    s->nthreads = 1;\n\n    // Sparse matrix L (lower triangular)\n    // NB: We don not allocate L completely (CSC elements)\n    //      L will be allocated during the factorization depending on the\n    //      resulting number of elements.\n    s->L = c_malloc(sizeof(csc));\n    s->L->m = n_plus_m;\n    s->L->n = n_plus_m;\n    s->L->nz = -1;\n\n    // Diagonal matrix stored as a vector D\n    s->Dinv = (QDLDL_float *)c_malloc(sizeof(QDLDL_float) * n_plus_m);\n    s->D    = (QDLDL_float *)c_malloc(sizeof(QDLDL_float) * n_plus_m);\n\n    // Permutation vector P\n    s->P    = (QDLDL_int *)c_malloc(sizeof(QDLDL_int) * n_plus_m);\n\n    // Working vector\n    s->bp   = (QDLDL_float *)c_malloc(sizeof(QDLDL_float) * n_plus_m);\n\n    // Solution vector\n    s->sol  = (QDLDL_float *)c_malloc(sizeof(QDLDL_float) * n_plus_m);\n\n    // Parameter vector\n    s->rho_inv_vec = (c_float *)c_malloc(sizeof(c_float) * s->m);\n\n    // Elimination tree workspace\n    s->etree = (QDLDL_int *)c_malloc(n_plus_m * sizeof(QDLDL_int));\n    s->Lnz   = (QDLDL_int *)c_malloc(n_plus_m * sizeof(QDLDL_int));\n\n    // Preallocate L matrix (Lx and Li are sparsity dependent)\n    s->L->p = (c_int *)c_malloc((n_plus_m+1) * sizeof(QDLDL_int));\n\n    // Lx and Li are sparsity dependent, so set them to\n    // null initially so we don't try to free them prematurely\n    s->L->i = OSQP_NULL;\n    s->L->x = OSQP_NULL;\n\n    // Preallocate workspace\n    s->iwork = (QDLDL_int *)c_malloc(sizeof(QDLDL_int)*(3*n_plus_m));\n    s->bwork = (QDLDL_bool *)c_malloc(sizeof(QDLDL_bool)*n_plus_m);\n    s->fwork = (QDLDL_float *)c_malloc(sizeof(QDLDL_float)*n_plus_m);\n\n    // Form and permute KKT matrix\n    if (polish){ // Called from polish()\n        // Use s->rho_inv_vec for storing param2 = vec(delta)\n        for (i = 0; i < A->m; i++){\n            s->rho_inv_vec[i] = sigma;\n        }\n\n        KKT_temp = form_KKT(P, A, 0, sigma, s->rho_inv_vec, OSQP_NULL, OSQP_NULL, OSQP_NULL, OSQP_NULL, OSQP_NULL);\n\n        // Permute matrix\n        if (KKT_temp)\n            permute_KKT(&KKT_temp, s, OSQP_NULL, OSQP_NULL, OSQP_NULL, OSQP_NULL, OSQP_NULL, OSQP_NULL);\n    }\n    else { // Called from ADMM algorithm\n\n        // Allocate vectors of indices\n        s->PtoKKT = c_malloc((P->p[P->n]) * sizeof(c_int));\n        s->AtoKKT = c_malloc((A->p[A->n]) * sizeof(c_int));\n        s->rhotoKKT = c_malloc((A->m) * sizeof(c_int));\n\n        // Use p->rho_inv_vec for storing param2 = rho_inv_vec\n        for (i = 0; i < A->m; i++){\n            s->rho_inv_vec[i] = 1. / rho_vec[i];\n        }\n\n        KKT_temp = form_KKT(P, A, 0, sigma, s->rho_inv_vec,\n                            s->PtoKKT, s->AtoKKT,\n                            &(s->Pdiag_idx), &(s->Pdiag_n), s->rhotoKKT);\n\n        // Permute matrix\n        if (KKT_temp)\n            permute_KKT(&KKT_temp, s, P->p[P->n], A->p[A->n], A->m, s->PtoKKT, s->AtoKKT, s->rhotoKKT);\n    }\n\n    // Check if matrix has been created\n    if (!KKT_temp){\n#ifdef PRINTING\n        c_eprint(\"Error forming and permuting KKT matrix\");\n#endif\n        free_linsys_solver_qdldl(s);\n        *sp = OSQP_NULL;\n        return OSQP_LINSYS_SOLVER_INIT_ERROR;\n    }\n\n    // Factorize the KKT matrix\n    if (LDL_factor(KKT_temp, s, P->n) < 0) {\n        csc_spfree(KKT_temp);\n        free_linsys_solver_qdldl(s);\n        *sp = OSQP_NULL;\n        return OSQP_NONCVX_ERROR;\n    }\n\n    if (polish){ // If KKT passed, assign it to KKT_temp\n        // Polish, no need for KKT_temp\n        csc_spfree(KKT_temp);\n    }\n    else { // If not embedded option 1 copy pointer to KKT_temp. Do not free it.\n        s->KKT = KKT_temp;\n    }\n\n\n    // No error\n    return 0;\n}\n\n#endif  // EMBEDDED\n\n\n// Permute x = P*b using P\nvoid permute_x(c_int n, c_float * x, const c_float * b, const c_int * P) {\n    c_int j;\n    for (j = 0 ; j < n ; j++) x[j] = b[P[j]];\n}\n\n// Permute x = P'*b using P\nvoid permutet_x(c_int n, c_float * x, const c_float * b, const c_int * P) {\n    c_int j;\n    for (j = 0 ; j < n ; j++) x[P[j]] = b[j];\n}\n\n\nstatic void LDLSolve(c_float *x, c_float *b, const csc *L, const c_float *Dinv, const c_int *P, c_float *bp) {\n    /* solves P'LDL'P x = b for x */\n    permute_x(L->n, bp, b, P);\n    QDLDL_solve(L->n, L->p, L->i, L->x, Dinv, bp);\n    permutet_x(L->n, x, bp, P);\n\n}\n\n\nc_int solve_linsys_qdldl(qdldl_solver * s, c_float * b) {\n    c_int j;\n\n#ifndef EMBEDDED\n    if (s->polish) {\n        /* stores solution to the KKT system in b */\n        LDLSolve(b, b, s->L, s->Dinv, s->P, s->bp);\n    } else {\n#endif\n        /* stores solution to the KKT system in s->sol */\n        LDLSolve(s->sol, b, s->L, s->Dinv, s->P, s->bp);\n\n        /* copy x_tilde from s->sol */\n        for (j = 0 ; j < s->n ; j++) {\n            b[j] = s->sol[j];\n        }\n\n        /* compute z_tilde from b and s->sol */\n        for (j = 0 ; j < s->m ; j++) {\n            b[j + s->n] += s->rho_inv_vec[j] * s->sol[j + s->n];\n        }\n#ifndef EMBEDDED\n    }\n#endif\n\n    return 0;\n}\n\n\n#if EMBEDDED != 1\n// Update private structure with new P and A\nc_int update_linsys_solver_matrices_qdldl(qdldl_solver * s, const csc *P, const csc *A) {\n\n    // Update KKT matrix with new P\n    update_KKT_P(s->KKT, P, s->PtoKKT, s->sigma, s->Pdiag_idx, s->Pdiag_n);\n\n    // Update KKT matrix with new A\n    update_KKT_A(s->KKT, A, s->AtoKKT);\n\n    return (QDLDL_factor(s->KKT->n, s->KKT->p, s->KKT->i, s->KKT->x,\n        s->L->p, s->L->i, s->L->x, s->D, s->Dinv, s->Lnz,\n        s->etree, s->bwork, s->iwork, s->fwork) < 0);\n\n}\n\n\nc_int update_linsys_solver_rho_vec_qdldl(qdldl_solver * s, const c_float * rho_vec){\n    c_int i;\n\n    // Update internal rho_inv_vec\n    for (i = 0; i < s->m; i++){\n        s->rho_inv_vec[i] = 1. / rho_vec[i];\n    }\n\n    // Update KKT matrix with new rho_vec\n    update_KKT_param2(s->KKT, s->rho_inv_vec, s->rhotoKKT, s->m);\n\n    return (QDLDL_factor(s->KKT->n, s->KKT->p, s->KKT->i, s->KKT->x,\n        s->L->p, s->L->i, s->L->x, s->D, s->Dinv, s->Lnz,\n        s->etree, s->bwork, s->iwork, s->fwork) < 0);\n}\n\n\n#endif\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_interface.h",
    "content": "#ifndef QDLDL_INTERFACE_H\n#define QDLDL_INTERFACE_H\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\n#include \"types.h\"\n#include \"qdldl_types.h\"\n\n/**\n * QDLDL solver structure\n */\ntypedef struct qdldl qdldl_solver;\n\nstruct qdldl {\n    enum linsys_solver_type type;\n\n    /**\n     * @name Functions\n     * @{\n     */\n    c_int (*solve)(struct qdldl * self, c_float * b);\n\n#ifndef EMBEDDED\n    void (*free)(struct qdldl * self); ///< Free workspace (only if desktop)\n#endif\n\n    // This used only in non embedded or embedded 2 version\n#if EMBEDDED != 1\n    c_int (*update_matrices)(struct qdldl * self, const csc *P, const csc *A);  ///< Update solver matrices\n    c_int (*update_rho_vec)(struct qdldl * self, const c_float * rho_vec);      ///< Update rho_vec parameter\n#endif\n\n#ifndef EMBEDDED\n    c_int nthreads;\n#endif\n    /** @} */\n\n    /**\n     * @name Attributes\n     * @{\n     */\n    csc *L;                 ///< lower triangular matrix in LDL factorization\n    c_float *Dinv;          ///< inverse of diag matrix in LDL (as a vector)\n    c_int   *P;             ///< permutation of KKT matrix for factorization\n    c_float *bp;            ///< workspace memory for solves\n    c_float *sol;           ///< solution to the KKT system\n    c_float *rho_inv_vec;   ///< parameter vector\n    c_float sigma;          ///< scalar parameter\n#ifndef EMBEDDED\n    c_int polish;           ///< polishing flag\n#endif\n    c_int n;                ///< number of QP variables\n    c_int m;                ///< number of QP constraints\n\n\n#if EMBEDDED != 1\n    // These are required for matrix updates\n    c_int * Pdiag_idx, Pdiag_n;  ///< index and number of diagonal elements in P\n    csc   * KKT;                 ///< Permuted KKT matrix in sparse form (used to update P and A matrices)\n    c_int * PtoKKT, * AtoKKT;    ///< Index of elements from P and A to KKT matrix\n    c_int * rhotoKKT;            ///< Index of rho places in KKT matrix\n    // QDLDL Numeric workspace\n    QDLDL_float *D;\n    QDLDL_int   *etree;\n    QDLDL_int   *Lnz;\n    QDLDL_int   *iwork;\n    QDLDL_bool  *bwork;\n    QDLDL_float *fwork;\n#endif\n\n    /** @} */\n};\n\n\n\n/**\n * Initialize QDLDL Solver\n *\n * @param  s         Pointer to a private structure\n * @param  P         Cost function matrix (upper triangular form)\n * @param  A         Constraints matrix\n * @param  sigma     Algorithm parameter. If polish, then sigma = delta.\n * @param  rho_vec   Algorithm parameter. If polish, then rho_vec = OSQP_NULL.\n * @param  polish    Flag whether we are initializing for polish or not\n * @return           Exitflag for error (0 if no errors)\n */\nc_int init_linsys_solver_qdldl(qdldl_solver ** sp, const csc * P, const csc * A, c_float sigma, const c_float * rho_vec, c_int polish);\n\n/**\n * Solve linear system and store result in b\n * @param  s        Linear system solver structure\n * @param  b        Right-hand side\n * @return          Exitflag\n */\nc_int solve_linsys_qdldl(qdldl_solver * s, c_float * b);\n\n\n#if EMBEDDED != 1\n/**\n * Update linear system solver matrices\n * @param  s        Linear system solver structure\n * @param  P        Matrix P\n * @param  A        Matrix A\n * @return          Exitflag\n */\nc_int update_linsys_solver_matrices_qdldl(qdldl_solver * s, const csc *P, const csc *A);\n\n\n\n\n/**\n * Update rho_vec parameter in linear system solver structure\n * @param  s        Linear system solver structure\n * @param  rho_vec  new rho_vec value\n * @return          exitflag\n */\nc_int update_linsys_solver_rho_vec_qdldl(qdldl_solver * s, const c_float * rho_vec);\n\n#endif\n\n#ifndef EMBEDDED\n/**\n * Free linear system solver\n * @param s linear system solver object\n */\nvoid free_linsys_solver_qdldl(qdldl_solver * s);\n#endif\n\n#ifdef __cplusplus\n}\n#endif\n\n#endif\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/.editorconfig",
    "content": "# https://editorconfig.org\n\nroot = true\n\n[*.{c, h, cpp, hpp}]\ncharset = utf-8\nindent_style = space\nindent_size = 2\nend_of_line = lf\ntrim_trailing_whitespace = true\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/.gitignore",
    "content": "# Prerequisites\n*.d\n\n# Object files\n*.o\n*.ko\n*.obj\n*.elf\n\n# Linker output\n*.ilk\n*.map\n*.exp\n\n# Precompiled Headers\n*.gch\n*.pch\n\n# Libraries\n*.lib\n*.a\n*.la\n*.lo\n\n# Shared objects (inc. Windows DLLs)\n*.dll\n*.so\n*.so.*\n*.dylib\n\n# Executables\n*.exe\n*.out\n*.app\n*.i*86\n*.x86_64\n*.hex\n\n# Debug files\n*.dSYM/\n*.su\n*.idb\n*.pdb\n\n# Kernel Module Compile Results\n*.mod*\n*.cmd\n.tmp_versions/\nmodules.order\nModule.symvers\nMkfile.old\ndkms.conf\n\n# OSX stuff\n.DS_Store\n\n# Ignore cmake build\nbuild/\n\n# Editors\n.vscode/\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/.travis.yml",
    "content": "language: C\n\nmatrix:\n  include:\n    - os: linux\n    - os: osx\n      # Specify version to avoid bug (https://github.com/travis-ci/travis-ci/issues/6522)\n      osx_image: xcode9.3\n\nsudo: required\n\ninstall:\n- |\n  # Install lcov only on linux\n  if [[ \"$TRAVIS_OS_NAME\" == \"linux\" ]]; then\n      sudo apt-get install -y lcov\n      # Install coveralls uploader\n      gem install coveralls-lcov\n  fi\n\n\n# Build main and tests\nscript:\n- |\n  cd ${TRAVIS_BUILD_DIR}\n  mkdir -p build\n  cd build\n  cmake --warn-uninitialized -DUNITTESTS=ON -DCOVERAGE=ON ..\n  make\n  out/qdldl_tester\n\n\n# Pefrorm code coverage (only in Linux case)\nafter_success:\n- |\n  cd ${TRAVIS_BUILD_DIR}\n  if [[ $TRAVIS_OS_NAME == \"linux\" ]]; then\n      cd ${TRAVIS_BUILD_DIR}/build\n      lcov --directory . --capture -o coverage.info # capture coverage info\n      lcov --remove coverage.info \\\n          \"${TRAVIS_BUILD_DIR}/tests/*\" \\\n          \"${TRAVIS_BUILD_DIR}/examples/*\" \\\n          \"/usr/include/x86_64-linux-gnu/bits/*\" \\\n          -o coverage.info # filter out tests and unnecessary files\n      lcov --list coverage.info # debug before upload\n      coveralls-lcov coverage.info # uploads to coveralls\n  fi\n\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/CHANGELOG.md",
    "content": "Version 0.1.4 (6 September 2019)\n---------------------------------\n*   Fix to cmake when building `qdldl::qdldlstatic` \n*   Fix to overflow issue when factoring very large matrices \n\nVersion 0.1.3 (11 September 2018)\n----------------------------------\n*   Julia implementation supports logical factorisation.\n*   Changed `QDLDL_bool` to be unsigned char everywhere, except for the Julia examples where it is now treated as `Uint8`.\n\n\nVersion 0.1.2 (23 July 2018)\n-----------------------------\n*   Various cmake improvements.\n*   Added pure Julia implementation.\n\n\nVersion 0.1.1 (19 July 2018)\n-----------------------------\n*   Fixed behaviour when data in A does not appear\n    sequentially within each column.\n*   Additional unit tests for non-sequential columns.\n*   Types can be defined through cmake.\n\n\nVersion 0.1.0 (16 July 2018)\n-----------------------------\n*   Initial release\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/CMakeLists.txt",
    "content": "# Minimum version required\ncmake_minimum_required (VERSION 3.2)\n\n# Project name\nproject (qdldl)\n\n# Set the output folder where your program will be created\nset(EXECUTABLE_OUTPUT_PATH ${PROJECT_BINARY_DIR}/out)\nset(LIBRARY_OUTPUT_PATH ${PROJECT_BINARY_DIR}/out)\n\n# Some non-standard CMake modules\nLIST(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/configure/cmake)\n\n# Export compilation commands for IDEs and autocompletion\nset(CMAKE_EXPORT_COMPILE_COMMANDS ON)\n\n# Options\n# ----------------------------------------------\n# Use floats instead of doubles\nset(DFLOAT OFF CACHE BOOL \"Use float numbers instead of doubles\")\nmessage(STATUS \"Floats are ${DFLOAT}\")\n\n# Use long integers for indexing\nset(DLONG ON CACHE BOOL \"Use long integers (64bit) for indexing\")\nif (NOT (CMAKE_SIZEOF_VOID_P EQUAL 8))\n\tmessage(STATUS \"Disabling long integers (64bit) on 32bit machine\")\n\tset(DLONG OFF)\nendif()\nmessage(STATUS \"Long integers (64bit) are ${DLONG}\")\n\n\n# Set Compiler flags\n# ----------------------------------------------\nset(CMAKE_POSITION_INDEPENDENT_CODE ON)  # -fPIC\n\n\n# Add compiler options if we are not on windows\nif (NOT MSVC)\n\n    if (COVERAGE)\n        set(CMAKE_C_FLAGS \"${CMAKE_C_FLAGS} --coverage\")\n\tif(FORTRAN)\n\t\tset(CMAKE_FORTRAN_FLAGS \"${CMAKE_FORTRAN_FLAGS} --coverage\")\n\tendif(FORTRAN)\n    endif()\n\n    set(CMAKE_C_FLAGS \"${CMAKE_C_FLAGS} -O3\")\n    set(CMAKE_C_FLAGS_DEBUG \"${CMAKE_C_FLAGS_DEBUG} -O0 -g\")\nendif (NOT MSVC)\n\n# Generate header file with the global options\n# ---------------------------------------------\n\n# numeric types\nif(DFLOAT)\n  set(QDLDL_FLOAT_TYPE \"float\")\nelse()\n\tset(QDLDL_FLOAT_TYPE \"double\")\nendif()\n\nif(DLONG)\n  set(QDLDL_INT_TYPE \"long long\")\n\tset(QDLDL_INT_TYPE_MAX \"LLONG_MAX\")\nelse()\n\tset(QDLDL_INT_TYPE \"int\")\n\tset(QDLDL_INT_TYPE_MAX \"INT_MAX\")\nendif()\n\n#boolean type is always unsigned char\n#for now, since _Bool does not exist in\n#C89 and we want to avoid interoperability\n#problems when calling QDLDL from C++\nset(QDLDL_BOOL_TYPE \"unsigned char\")\n\nconfigure_file(${CMAKE_CURRENT_SOURCE_DIR}/configure/qdldl_types.h.in\n               ${CMAKE_CURRENT_SOURCE_DIR}/include/qdldl_types.h\n               NEWLINE_STYLE LF)\n\n\n# Set sources\n# ----------------------------------------------\nset(\n\tqdldl_src\n\tsrc/qdldl.c\n\t)\n\nset(\n\tqdldl_headers\n\tinclude/qdldl.h\n\tinclude/qdldl_types.h\n\t)\n\n# Create object library\n# ----------------------------------------------\nadd_library (qdldlobject OBJECT ${qdldl_src} ${qdldl_headers})\ntarget_include_directories(qdldlobject PRIVATE ${PROJECT_SOURCE_DIR}/include)\n\n\n# Create Static Library\n# ----------------------------------------------\n\ninclude(GNUInstallDirs)\n\n# Static library\nadd_library (qdldlstatic STATIC ${qdldl_src} ${qdldl_headers})\n# Give same name to static library output\nset_target_properties(qdldlstatic PROPERTIES OUTPUT_NAME qdldl)\n\n# Declare include directories for the cmake exported target\ntarget_include_directories(qdldlstatic\n                           PUBLIC \"$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>\"\n                                  \"$<INSTALL_INTERFACE:$<INSTALL_PREFIX>/${CMAKE_INSTALL_INCLUDEDIR}/qdldl>\")\n\n# Install Static Library\n# ----------------------------------------------\n\ninstall(TARGETS qdldlstatic\n        EXPORT  ${PROJECT_NAME}\n        ARCHIVE       DESTINATION \"${CMAKE_INSTALL_LIBDIR}\"\n        LIBRARY       DESTINATION \"${CMAKE_INSTALL_LIBDIR}\"\n        RUNTIME       DESTINATION \"${CMAKE_INSTALL_BINDIR}\")\n\n\n# Install Headers\n# ----------------------------------------------\ninstall(FILES ${qdldl_headers} DESTINATION \"${CMAKE_INSTALL_INCLUDEDIR}/qdldl\")\n\n\n# Install Shared Library\n# ----------------------------------------------\n# Create qdldl shared library\nadd_library (qdldl SHARED ${qdldl_src} ${qdldl_headers})\n\n# Declare include directories for the cmake exported target\ntarget_include_directories(qdldl\n\tPUBLIC \"$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>\"\n\t\"$<INSTALL_INTERFACE:$<INSTALL_PREFIX>/${CMAKE_INSTALL_INCLUDEDIR}/qdldl>\")\n\n# Install qdldl shared library\ninstall(TARGETS qdldl\n\tEXPORT  ${PROJECT_NAME}\n\tLIBRARY       DESTINATION \"${CMAKE_INSTALL_LIBDIR}\"\n\tARCHIVE       DESTINATION \"${CMAKE_INSTALL_LIBDIR}\"\n\tRUNTIME       DESTINATION \"${CMAKE_INSTALL_BINDIR}\")\n\n# Create demo executable (linked to static library)\nadd_executable (qdldl_example ${PROJECT_SOURCE_DIR}/examples/example.c)\ntarget_link_libraries (qdldl_example qdldlstatic)\n\n\n# Create CMake packages for the build directory\n# ----------------------------------------------\ninclude(CMakePackageConfigHelpers)\n\nexport(EXPORT ${PROJECT_NAME}\n  FILE \"${CMAKE_CURRENT_BINARY_DIR}/qdldl-targets.cmake\"\n  NAMESPACE qdldl::)\n\nif(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/qdldl-config.cmake)\n  file(WRITE ${CMAKE_CURRENT_BINARY_DIR}/qdldl-config.cmake \"include(\\\"\\${CMAKE_CURRENT_LIST_DIR}/qdldl-targets.cmake\\\")\\n\")\nendif()\n\n\n# Create CMake packages for the install directory\n# ----------------------------------------------\n\nset(ConfigPackageLocation lib/cmake/qdldl)\n\ninstall(EXPORT ${PROJECT_NAME}\n        FILE qdldl-targets.cmake\n        NAMESPACE qdldl::\n        DESTINATION ${ConfigPackageLocation})\n\ninstall(FILES ${CMAKE_CURRENT_BINARY_DIR}/qdldl-config.cmake\n        DESTINATION ${ConfigPackageLocation})\n\n\n\n# Add uninstall command\n# ----------------------------------------------\nif(NOT TARGET uninstall)\n    configure_file(\n        \"${CMAKE_CURRENT_SOURCE_DIR}/configure/cmake/cmake_uninstall.cmake.in\"\n        \"${CMAKE_CURRENT_BINARY_DIR}/cmake_uninstall.cmake\"\n        IMMEDIATE @ONLY)\n\n    add_custom_target(uninstall\n        COMMAND ${CMAKE_COMMAND} -P ${CMAKE_CURRENT_BINARY_DIR}/cmake_uninstall.cmake)\nendif()\n\n\n\n# Add testing\n# ----------------------------------------------\n# Add custom command to generate tests\nif (UNITTESTS)\n\n    # Add test_headers and codegen_test_headers\n    add_subdirectory(tests)\n\n    # Direct qdldl solver testing\n    add_executable(qdldl_tester\n                ${PROJECT_SOURCE_DIR}/tests/qdldl_tester.c ${PROJECT_SOURCE_DIR}/tests/minunit.h\n                ${test_headers})\n    target_link_libraries (qdldl_tester qdldlstatic)\n\n    # Add testing\n    include(CTest)\n    enable_testing()\n    add_test(NAME tester COMMAND $<TARGET_FILE:qdldl_tester>)\nendif()\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/LICENSE",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. 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The text should be enclosed in the appropriate\n      comment syntax for the file format. We also recommend that a\n      file or class name and description of purpose be included on the\n      same \"printed page\" as the copyright notice for easier\n      identification within third-party archives.\n\n   Copyright [yyyy] [name of copyright owner]\n\n   Licensed under the Apache License, Version 2.0 (the \"License\");\n   you may not use this file except in compliance with the License.\n   You may obtain a copy of the License at\n\n       http://www.apache.org/licenses/LICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an \"AS IS\" BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License.\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/README.md",
    "content": "# QDLDL\nA free LDL factorisation routine for quasi-definite linear systems: `Ax=b`\n\n[![Build Status](https://travis-ci.org/oxfordcontrol/qdldl.svg?branch=master)](https://travis-ci.org/oxfordcontrol/qdldl)\n[![Build status](https://ci.appveyor.com/api/projects/status/ns4br7v6y3i5stai/branch/master?svg=true)](https://ci.appveyor.com/project/bstellato/qdldl-8q1mv/branch/master)\n[![Coverage Status](https://coveralls.io/repos/github/oxfordcontrol/qdldl/badge.svg)](https://coveralls.io/github/oxfordcontrol/qdldl)\n\n\n## Getting started\nTo start using QDLDL, first clone the repository\n\n```bash\ngit clone https://github.com/oxfordcontrol/qdldl.git\n```\n\n### Build\n\nTo build QDLDL, you need to install [cmake](https://cmake.org/) and run\n\n```bash\nmkdir build\ncd build\ncmake ..\ncmake --build .\n```\n\nThis will generate an `out/` folder with contents:\n\n- `qdldl_example`: a **code example** from [`examples/example.c`](./examples/example.c)\n- `libqdldl`: a static and a dynamic versions of the library.\n\nYou can include an addition option `-DUNITTESTS=ON` when calling `cmake`, which will result in an additional executable `qdldl_tester` being built in the `out/` folder to test QDLDL on a variety of problems, including those with rank deficient or otherwise ill-formatted inputs.\n\n**N.B.** All files will have file extensions appropriate to your operating system.\n\n\n### Install/Uninstall\n\nTo install (uninstall) the libraries and headers you can simply run `make install` (`make uninstall`) after running the `cmake` command above.\n\n\n## Calling QDLDL\n\n### Main API\n\nThe QDLDL API consists of 5 functions documented in [`include/qdldl.h`](./include/qdldl.h).\nFor more details and a working example see [`examples/example.c`](./examples/example.c).\n\n**N.B.** There is **no memory allocation** performed in these routines. The user is assumed to have the working vectors already allocated.\n\nHere is a brief summary.\n\n* `QDLDL_etree`: compute the elimination tree for the quasidefinite matrix factorization `A = LDL'`\n* `QDLDL_factor`: return the factors `L`, `D` and `Dinv = 1./D`\n* `QDLDL_solve`: solves the linear system `LDL'x = b`\n* `QDLDL_Lsolve`: solves `Lx = b`\n* `QDLDL_Ltsolve`: solves `L'x = b`\n\nIn the above function calls the matrices `A` and `L` are stored in compressed sparse column (CSC) format.   The matrix `A` is assumed to be symmetric and only the upper triangular portion of A should be passed to the API.   The factor `L` is lower triangular with implicit ones on the diagonal (i.e. the diagonal of L is not stored as part of the CSC formatted data.)\n\nThe matrices `D` and `Dinv` are both diagonal matrices, with the diagonal values stored in an array.\n\nThe matrix input `A` should be quasidefinite.   The API provides some (non-comprehensive) error checking to protect against non-quasidefinite or non-upper triangular inputs.\n\n### Custom types for integer, floats and booleans\nQDLDL uses its own internal types for integers, floats and booleans (`QDLDL_int, QDLDL_float, QDLDL_bool`. They can be specified using the cmake options:\n\n- `DFLOAT` (default false): uses float numbers instead of doubles\n- `DLONG` (default true): uses long integers for indexing (for large matrices)\n\nThe `QDLDL_bool` is internally defined as `unsigned char`.\n\n\n## Linking QDLDL\n\n### Basic Example\nA basic example appears in [`examples/example.c`](./examples/example.c) and is compiled using cmake and the `CMakeLists.txt` file in the root folder.\n\n\n### Including in a cmake project\n\nYou can include QDLDL in a cmake project `foo` by adding the subdirectory as\n```\n# Add project\nadd_subdirectory(qdldl)\n```\n\nQDLDL can be linked using a static or dynamic linker\n```\n# Link static library\ntarget_link_libraries (foo qdldlstatic)\n\n# Link shared library\ntarget_link_libraries (foo qdldl)\n```\nfor dynamic linking the shared library should be available in your path.\n\nThere is also the option to include QDLDL as an object library in your project.\nThe current `CMakeLists.txt` file creates an object library called `qdldlobject`.\nThis can be added to your project by adding it after your sources.\nFor example, when creating a library `foo` you can add\n\n```\nadd_library(foo foo.c foo.h $<TARGET_OBJECTS:qdldlobject>)\n```\nfor more details see the [cmake documentation](https://cmake.org/cmake/help/latest/command/add_library.html#object-libraries).\n\n\n## The algorithm\n\nThe algorithm is an independent implementation of the elimination tree and factorisation procedures outlined in\n\n> T. A Davis. [Algorithm 849: a concise sparse cholesky factorization package](https://dl.acm.org/citation.cfm?id=1114277). ACM Trans. Math. Softw., 31(4):587–591, 2005.\n\n\n## Credits\n\n- [Paul Goulart](http://users.ox.ac.uk/~engs1373/): main development\n- [Bartolomeo Stellato](https://stellato.io/): code refactoring and testing\n- [Goran Banjac](https://people.ee.ethz.ch/~gbanjac/)\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/appveyor.yml",
    "content": "# Not a .NET project. We build it in the test script\nbuild: false\n\nplatform:\n  - x64\n  - x86\n\nenvironment:\n  CMAKE_PROJECT: \"Visual Studio 14 2015\"\n\ninit:\n# Uncomment for remote desktop debug\n# - ps: iex ((new-object net.webclient).DownloadString('https://raw.githubusercontent.com/appveyor/ci/master/scripts/enable-rdp.ps1'))\n# Setup Cmake project\n- cmd: >-\n    IF \"%PLATFORM%\"==\"x64\" (\n    call \"C:\\Program Files\\Microsoft SDKs\\Windows\\v7.1\\Bin\\SetEnv.cmd\" /x64\n    ) ELSE (\n    call \"C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\vcvarsall.bat\" x86\n    )\n\n\n# Perform tests\ntest_script:\n- cd \"%APPVEYOR_BUILD_FOLDER%\"\n- mkdir build\n- cd build\n- cmake -G \"%CMAKE_PROJECT%\" -DUNITTESTS=ON ..\n- cmake --build .\n- call \"%APPVEYOR_BUILD_FOLDER%\\build\\out\\Debug\\qdldl_tester.exe\"\n\n# on_finish:\n#   # Uncomment for remote desktop debug\n#   - ps: $blockRdp = $true; iex ((new-object net.webclient).DownloadString('https://raw.githubusercontent.com/appveyor/ci/master/scripts/enable-rdp.ps1'))\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/configure/cmake/cmake_uninstall.cmake.in",
    "content": "if(NOT EXISTS \"@CMAKE_BINARY_DIR@/install_manifest.txt\")\n  message(FATAL_ERROR \"Cannot find install manifest: @CMAKE_BINARY_DIR@/install_manifest.txt\")\nendif(NOT EXISTS \"@CMAKE_BINARY_DIR@/install_manifest.txt\")\n\nfile(READ \"@CMAKE_BINARY_DIR@/install_manifest.txt\" files)\nstring(REGEX REPLACE \"\\n\" \";\" files \"${files}\")\nforeach(file ${files})\n  message(STATUS \"Uninstalling $ENV{DESTDIR}${file}\")\n  if(IS_SYMLINK \"$ENV{DESTDIR}${file}\" OR EXISTS \"$ENV{DESTDIR}${file}\")\n    exec_program(\n      \"@CMAKE_COMMAND@\" ARGS \"-E remove \\\"$ENV{DESTDIR}${file}\\\"\"\n      OUTPUT_VARIABLE rm_out\n      RETURN_VALUE rm_retval\n      )\n    if(NOT \"${rm_retval}\" STREQUAL 0)\n      message(FATAL_ERROR \"Problem when removing $ENV{DESTDIR}${file}\")\n    endif(NOT \"${rm_retval}\" STREQUAL 0)\n  else(IS_SYMLINK \"$ENV{DESTDIR}${file}\" OR EXISTS \"$ENV{DESTDIR}${file}\")\n    message(STATUS \"File $ENV{DESTDIR}${file} does not exist.\")\n  endif(IS_SYMLINK \"$ENV{DESTDIR}${file}\" OR EXISTS \"$ENV{DESTDIR}${file}\")\nendforeach(file)\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/configure/qdldl_types.h.in",
    "content": "#ifndef QDLDL_TYPES_H\n# define QDLDL_TYPES_H\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif /* ifdef __cplusplus */\n\n#include <limits.h> //for the QDLDL_INT_TYPE_MAX\n\n// QDLDL integer and float types\n\ntypedef @QDLDL_INT_TYPE@    QDLDL_int;   /* for indices */\ntypedef @QDLDL_FLOAT_TYPE@  QDLDL_float; /* for numerical values  */\ntypedef @QDLDL_BOOL_TYPE@   QDLDL_bool;  /* for boolean values  */\n\n//Maximum value of the signed type QDLDL_int.\n#define QDLDL_INT_MAX @QDLDL_INT_TYPE_MAX@\n\n# ifdef __cplusplus\n}\n# endif /* ifdef __cplusplus */\n\n#endif /* ifndef QDLDL_TYPES_H */\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/examples/example.c",
    "content": "#include \"qdldl.h\"\n#include <stdlib.h>\n#include <stdio.h>\n\nvoid print_arrayi(const QDLDL_int* data, QDLDL_int n,char* varName);\nvoid print_arrayf(const QDLDL_float* data, QDLDL_int n, char* varName);\nvoid print_line(void);\n\nconst QDLDL_int   An   = 10;\nconst QDLDL_int   Ap[] = {0, 1, 2, 4, 5, 6, 8, 10, 12, 14, 17};\nconst QDLDL_int   Ai[] = {0, 1, 1, 2, 3, 4, 1, 5, 0, 6, 3, 7, 6, 8, 1, 2, 9};\nconst QDLDL_float Ax[] = {1.0, 0.460641, -0.121189, 0.417928, 0.177828, 0.1,\n                       -0.0290058, -1.0, 0.350321, -0.441092, -0.0845395,\n                       -0.316228, 0.178663, -0.299077, 0.182452, -1.56506, -0.1};\nconst QDLDL_float  b[] = {1,2,3,4,5,6,7,8,9,10};\n\nint main()\n\n{\n  QDLDL_int i; // Counter\n\n  //data for L and D factors\n  QDLDL_int Ln = An;\n  QDLDL_int *Lp;\n  QDLDL_int *Li;\n  QDLDL_float *Lx;\n  QDLDL_float *D;\n  QDLDL_float *Dinv;\n\n  //data for elim tree calculation\n  QDLDL_int *etree;\n  QDLDL_int *Lnz;\n  QDLDL_int  sumLnz;\n\n  //working data for factorisation\n  QDLDL_int   *iwork;\n  QDLDL_bool  *bwork;\n  QDLDL_float *fwork;\n\n  //Data for results of A\\b\n  QDLDL_float *x;\n\n\n  /*--------------------------------\n   * pre-factorisation memory allocations\n   *---------------------------------*/\n\n  //These can happen *before* the etree is calculated\n  //since the sizes are not sparsity pattern specific\n\n  //For the elimination tree\n  etree = (QDLDL_int*)malloc(sizeof(QDLDL_int)*An);\n  Lnz   = (QDLDL_int*)malloc(sizeof(QDLDL_int)*An);\n\n  //For the L factors.   Li and Lx are sparsity dependent\n  //so must be done after the etree is constructed\n  Lp    = (QDLDL_int*)malloc(sizeof(QDLDL_int)*(An+1));\n  D     = (QDLDL_float*)malloc(sizeof(QDLDL_float)*An);\n  Dinv  = (QDLDL_float*)malloc(sizeof(QDLDL_float)*An);\n\n  //Working memory.  Note that both the etree and factor\n  //calls requires a working vector of QDLDL_int, with\n  //the factor function requiring 3*An elements and the\n  //etree only An elements.   Just allocate the larger\n  //amount here and use it in both places\n  iwork = (QDLDL_int*)malloc(sizeof(QDLDL_int)*(3*An));\n  bwork = (QDLDL_bool*)malloc(sizeof(QDLDL_bool)*An);\n  fwork = (QDLDL_float*)malloc(sizeof(QDLDL_float)*An);\n\n  /*--------------------------------\n   * elimination tree calculation\n   *---------------------------------*/\n  sumLnz = QDLDL_etree(An,Ap,Ai,iwork,Lnz,etree);\n\n  /*--------------------------------\n   * LDL factorisation\n   *---------------------------------*/\n\n  //First allocate memory for Li and Lx\n  Li    = (QDLDL_int*)malloc(sizeof(QDLDL_int)*sumLnz);\n  Lx    = (QDLDL_float*)malloc(sizeof(QDLDL_float)*sumLnz);\n\n  //now factor\n  QDLDL_factor(An,Ap,Ai,Ax,Lp,Li,Lx,D,Dinv,Lnz,etree,bwork,iwork,fwork);\n\n  /*--------------------------------\n   * solve\n   *---------------------------------*/\n  x = (QDLDL_float*)malloc(sizeof(QDLDL_float)*An);\n\n  //when solving A\\b, start with x = b\n  for(i=0;i < Ln; i++) x[i] = b[i];\n  QDLDL_solve(Ln,Lp,Li,Lx,Dinv,x);\n\n  /*--------------------------------\n   * print factors and solution\n   *---------------------------------*/\n  printf(\"\\n\");\n  printf(\"A (CSC format):\\n\");\n  print_line();\n  print_arrayi(Ap, An + 1, \"A.p\");\n  print_arrayi(Ai, Ap[An], \"A.i\");\n  print_arrayf(Ax, Ap[An], \"A.x\");\n  printf(\"\\n\\n\");\n\n  printf(\"elimination tree:\\n\");\n  print_line();\n  print_arrayi(etree, Ln, \"etree\");\n  print_arrayi(Lnz, Ln, \"Lnz\");\n  printf(\"\\n\\n\");\n\n  printf(\"L (CSC format):\\n\");\n  print_line();\n  print_arrayi(Lp, Ln + 1, \"L.p\");\n  print_arrayi(Li, Lp[Ln], \"L.i\");\n  print_arrayf(Lx, Lp[Ln], \"L.x\");\n  printf(\"\\n\\n\");\n\n  printf(\"D:\\n\");\n  print_line();\n  print_arrayf(D, An,    \"diag(D)     \");\n  print_arrayf(Dinv, An, \"diag(D^{-1})\");\n  printf(\"\\n\\n\");\n\n  printf(\"solve results:\\n\");\n  print_line();\n  print_arrayf(b, An, \"b\");\n  print_arrayf(x, An, \"A\\\\b\");\n  printf(\"\\n\\n\");\n\n\n  /*--------------------------------\n   * clean up\n   *---------------------------------*/\n  free(Lp);\n  free(Li);\n  free(Lx);\n  free(D);\n  free(Dinv);\n  free(etree);\n  free(Lnz);\n  free(iwork);\n  free(bwork);\n  free(fwork);\n  free(x);\n\nreturn 0 ;\n\n\n}\n\n\nvoid print_line(void){\n  printf(\"--------------------------\\n\");\n}\n\nvoid print_arrayi(const QDLDL_int* data, QDLDL_int n,char* varName){\n\n  QDLDL_int i;\n  printf(\"%s = [\",varName);\n  for(i=0; i< n; i++){\n    printf(\"%i,\",(int)data[i]);\n  }\n  printf(\"]\\n\");\n\n}\n\nvoid print_arrayf(const QDLDL_float* data, QDLDL_int n, char* varName){\n\n  QDLDL_int i;\n  printf(\"%s = [\",varName);\n  for(i=0; i< n; i++){\n    printf(\"%.3g,\",data[i]);\n  }\n  printf(\"]\\n\");\n\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/include/.gitignore",
    "content": "qdldl_types.h\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/include/qdldl.h",
    "content": "#ifndef QDLDL_H\n#define QDLDL_H\n\n// Include qdldl type options\n#include \"qdldl_types.h\"\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif // ifdef __cplusplus\n\n/**\n  * Compute the elimination tree for a quasidefinite matrix\n  * in compressed sparse column form, where the input matrix is\n  * assumed to contain data for the upper triangular part of A only,\n  * and there are no duplicate indices.\n  *\n  * Returns an elimination tree for the factorization A = LDL^T and a\n  * count of the nonzeros in each column of L that are strictly below the\n  * diagonal.\n  *\n  * Does not use MALLOC.  It is assumed that the arrays work, Lnz, and\n  * etree will be allocated with a number of elements equal to n.\n  *\n  * The data in (n,Ap,Ai) are from a square matrix A in CSC format, and\n  * should include the upper triangular part of A only.\n  *\n  * This function is only intended for factorisation of QD matrices specified\n  * by their upper triangular part.  An error is returned if any column has\n  * data below the diagonal or s completely empty.\n  *\n  * For matrices with a non-empty column but a zero on the corresponding diagonal,\n  * this function will *not* return an error, as it may still be possible to factor\n  * such a matrix in LDL form.   No promises are made in this case though...\n  *\n  * @param  n      number of columns in CSC matrix A (assumed square)\n  * @param  Ap     column pointers (size n+1) for columns of A\n  * @param  Ai     row indices of A.  Has Ap[n] elements\n  * @param  work   work vector (size n) (no meaning on return)\n  * @param  Lnz    count of nonzeros in each column of L (size n) below diagonal\n  * @param  etree  elimination tree (size n)\n  * @return total  sum of Lnz (i.e. total nonzeros in L below diagonal).\n  *                Returns -1 if the input is not triu or has an empty column.\n  *                Returns -2 if the return value overflows QDLDL_int.\n  *\n*/\n QDLDL_int QDLDL_etree(const QDLDL_int   n,\n                       const QDLDL_int* Ap,\n                       const QDLDL_int* Ai,\n                       QDLDL_int* work,\n                       QDLDL_int* Lnz,\n                       QDLDL_int* etree);\n\n\n/**\n  * Compute an LDL decomposition for a quasidefinite matrix\n  * in compressed sparse column form, where the input matrix is\n  * assumed to contain data for the upper triangular part of A only,\n  * and there are no duplicate indices.\n  *\n  * Returns factors L, D and Dinv = 1./D.\n  *\n  * Does not use MALLOC.  It is assumed that L will be a compressed\n  * sparse column matrix with data (n,Lp,Li,Lx)  with sufficient space\n  * allocated, with a number of nonzeros equal to the count given\n  * as a return value by QDLDL_etree\n  *\n  * @param  n      number of columns in L and A (both square)\n  * @param  Ap     column pointers (size n+1) for columns of A (not modified)\n  * @param  Ai     row indices of A.  Has Ap[n] elements (not modified)\n  * @param  Ax     data of A.  Has Ap[n] elements (not modified)\n  * @param  Lp     column pointers (size n+1) for columns of L\n  * @param  Li     row indices of L.  Has Lp[n] elements\n  * @param  Lx     data of L.  Has Lp[n] elements\n  * @param  D      vectorized factor D.  Length is n\n  * @param  Dinv   reciprocal of D.  Length is n\n  * @param  Lnz    count of nonzeros in each column of L below diagonal,\n  *                as given by QDLDL_etree (not modified)\n  * @param  etree  elimination tree as as given by QDLDL_etree (not modified)\n  * @param  bwork  working array of bools. Length is n\n  * @param  iwork  working array of integers. Length is 3*n\n  * @param  fwork  working array of floats. Length is n\n  * @return        Returns a count of the number of positive elements\n  *                in D.  Returns -1 and exits immediately if any element\n  *                of D evaluates exactly to zero (matrix is not quasidefinite\n  *                or otherwise LDL factorisable)\n  *\n*/\nQDLDL_int QDLDL_factor(const QDLDL_int    n,\n                  const QDLDL_int*   Ap,\n                  const QDLDL_int*   Ai,\n                  const QDLDL_float* Ax,\n                  QDLDL_int*   Lp,\n                  QDLDL_int*   Li,\n                  QDLDL_float* Lx,\n                  QDLDL_float* D,\n                  QDLDL_float* Dinv,\n                  const QDLDL_int* Lnz,\n                  const QDLDL_int* etree,\n                  QDLDL_bool* bwork,\n                  QDLDL_int* iwork,\n                  QDLDL_float* fwork);\n\n\n/**\n  * Solves LDL'x = b\n  *\n  * It is assumed that L will be a compressed\n  * sparse column matrix with data (n,Lp,Li,Lx).\n  *\n  * @param  n      number of columns in L\n  * @param  Lp     column pointers (size n+1) for columns of L\n  * @param  Li     row indices of L.  Has Lp[n] elements\n  * @param  Lx     data of L.  Has Lp[n] elements\n  * @param  Dinv   reciprocal of D.  Length is n\n  * @param  x      initialized to b.  Equal to x on return\n  *\n*/\nvoid QDLDL_solve(const QDLDL_int    n,\n                 const QDLDL_int*   Lp,\n                 const QDLDL_int*   Li,\n                 const QDLDL_float* Lx,\n                 const QDLDL_float* Dinv,\n                 QDLDL_float* x);\n\n\n/**\n * Solves (L+I)x = b\n *\n * It is assumed that L will be a compressed\n * sparse column matrix with data (n,Lp,Li,Lx).\n *\n * @param  n      number of columns in L\n * @param  Lp     column pointers (size n+1) for columns of L\n * @param  Li     row indices of L.  Has Lp[n] elements\n * @param  Lx     data of L.  Has Lp[n] elements\n * @param  x      initialized to b.  Equal to x on return\n *\n*/\nvoid QDLDL_Lsolve(const QDLDL_int    n,\n                  const QDLDL_int*   Lp,\n                  const QDLDL_int*   Li,\n                  const QDLDL_float* Lx,\n                  QDLDL_float* x);\n\n\n/**\n * Solves (L+I)'x = b\n *\n * It is assumed that L will be a compressed\n * sparse column matrix with data (n,Lp,Li,Lx).\n *\n * @param  n      number of columns in L\n * @param  Lp     column pointers (size n+1) for columns of L\n * @param  Li     row indices of L.  Has Lp[n] elements\n * @param  Lx     data of L.  Has Lp[n] elements\n * @param  x      initialized to b.  Equal to x on return\n *\n*/\nvoid QDLDL_Ltsolve(const QDLDL_int    n,\n                   const QDLDL_int*   Lp,\n                   const QDLDL_int*   Li,\n                   const QDLDL_float* Lx,\n                   QDLDL_float* x);\n\n# ifdef __cplusplus\n}\n# endif // ifdef __cplusplus\n\n#endif // ifndef QDLDL_H\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/src/qdldl.c",
    "content": "#include \"qdldl.h\"\n\n#define QDLDL_UNKNOWN (-1)\n#define QDLDL_USED (1)\n#define QDLDL_UNUSED (0)\n\n/* Compute the elimination tree for a quasidefinite matrix\n   in compressed sparse column form.\n*/\n\nQDLDL_int QDLDL_etree(const QDLDL_int  n,\n                      const QDLDL_int* Ap,\n                      const QDLDL_int* Ai,\n                      QDLDL_int* work,\n                      QDLDL_int* Lnz,\n                      QDLDL_int* etree){\n\n  QDLDL_int sumLnz;\n  QDLDL_int i,j,p;\n\n\n  for(i = 0; i < n; i++){\n  // zero out Lnz and work.  Set all etree values to unknown\n    work[i]  = 0;\n    Lnz[i]   = 0;\n    etree[i] = QDLDL_UNKNOWN;\n\n    //Abort if A doesn't have at least one entry\n    //one entry in every column\n    if(Ap[i] == Ap[i+1]){\n      return -1;\n    }\n\n  }\n\n  for(j = 0; j < n; j++){\n    work[j] = j;\n    for(p = Ap[j]; p < Ap[j+1]; p++){\n      i = Ai[p];\n      if(i > j){return -1;}; //abort if entries on lower triangle\n      while(work[i] != j){\n        if(etree[i] == QDLDL_UNKNOWN){\n          etree[i] = j;\n        }\n        Lnz[i]++;         //nonzeros in this column\n        work[i] = j;\n        i = etree[i];\n      }\n    }\n  }\n\n  //compute the total nonzeros in L.  This much\n  //space is required to store Li and Lx.  Return\n  //error code -2 if the nonzero count will overflow\n  //its unteger type.\n  sumLnz  = 0;\n  for(i = 0; i < n; i++){\n    if(sumLnz > QDLDL_INT_MAX - Lnz[i]){\n      sumLnz = -2;\n      break;\n    }\n    else{\n      sumLnz += Lnz[i];\n    }\n  }\n\n  return sumLnz;\n}\n\n\n\nQDLDL_int QDLDL_factor(const QDLDL_int    n,\n                  const QDLDL_int*   Ap,\n                  const QDLDL_int*   Ai,\n                  const QDLDL_float* Ax,\n                  QDLDL_int*   Lp,\n                  QDLDL_int*   Li,\n                  QDLDL_float* Lx,\n                  QDLDL_float* D,\n                  QDLDL_float* Dinv,\n                  const QDLDL_int* Lnz,\n                  const QDLDL_int* etree,\n                  QDLDL_bool*  bwork,\n                  QDLDL_int*   iwork,\n                  QDLDL_float* fwork){\n\n  QDLDL_int i,j,k,nnzY, bidx, cidx, nextIdx, nnzE, tmpIdx;\n  QDLDL_int *yIdx, *elimBuffer, *LNextSpaceInCol;\n  QDLDL_float *yVals;\n  QDLDL_float yVals_cidx;\n  QDLDL_bool  *yMarkers;\n  QDLDL_int   positiveValuesInD = 0;\n\n  //partition working memory into pieces\n  yMarkers        = bwork;\n  yIdx            = iwork;\n  elimBuffer      = iwork + n;\n  LNextSpaceInCol = iwork + n*2;\n  yVals           = fwork;\n\n\n  Lp[0] = 0; //first column starts at index zero\n\n  for(i = 0; i < n; i++){\n\n    //compute L column indices\n    Lp[i+1] = Lp[i] + Lnz[i];   //cumsum, total at the end\n\n    // set all Yidx to be 'unused' initially\n    //in each column of L, the next available space\n    //to start is just the first space in the column\n    yMarkers[i]  = QDLDL_UNUSED;\n    yVals[i]     = 0.0;\n    D[i]         = 0.0;\n    LNextSpaceInCol[i] = Lp[i];\n  }\n\n  // First element of the diagonal D.\n  D[0]     = Ax[0];\n  if(D[0] == 0.0){return -1;}\n  if(D[0]  > 0.0){positiveValuesInD++;}\n  Dinv[0] = 1/D[0];\n\n  //Start from 1 here. The upper LH corner is trivially 0\n  //in L b/c we are only computing the subdiagonal elements\n  for(k = 1; k < n; k++){\n\n    //NB : For each k, we compute a solution to\n    //y = L(0:(k-1),0:k-1))\\b, where b is the kth\n    //column of A that sits above the diagonal.\n    //The solution y is then the kth row of L,\n    //with an implied '1' at the diagonal entry.\n\n    //number of nonzeros in this row of L\n    nnzY = 0;  //number of elements in this row\n\n    //This loop determines where nonzeros\n    //will go in the kth row of L, but doesn't\n    //compute the actual values\n    tmpIdx = Ap[k+1];\n\n    for(i = Ap[k]; i < tmpIdx; i++){\n\n      bidx = Ai[i];   // we are working on this element of b\n\n      //Initialize D[k] as the element of this column\n      //corresponding to the diagonal place.  Don't use\n      //this element as part of the elimination step\n      //that computes the k^th row of L\n      if(bidx == k){\n        D[k] = Ax[i];\n        continue;\n      }\n\n      yVals[bidx] = Ax[i];   // initialise y(bidx) = b(bidx)\n\n      // use the forward elimination tree to figure\n      // out which elements must be eliminated after\n      // this element of b\n      nextIdx = bidx;\n\n      if(yMarkers[nextIdx] == QDLDL_UNUSED){   //this y term not already visited\n\n        yMarkers[nextIdx] = QDLDL_USED;     //I touched this one\n        elimBuffer[0]     = nextIdx;  // It goes at the start of the current list\n        nnzE              = 1;         //length of unvisited elimination path from here\n\n        nextIdx = etree[bidx];\n\n        while(nextIdx != QDLDL_UNKNOWN && nextIdx < k){\n          if(yMarkers[nextIdx] == QDLDL_USED) break;\n\n          yMarkers[nextIdx] = QDLDL_USED;   //I touched this one\n          elimBuffer[nnzE] = nextIdx; //It goes in the current list\n          nnzE++;                     //the list is one longer than before\n          nextIdx = etree[nextIdx];   //one step further along tree\n\n        } //end while\n\n        // now I put the buffered elimination list into\n        // my current ordering in reverse order\n        while(nnzE){\n          yIdx[nnzY++] = elimBuffer[--nnzE];\n        } //end while\n      } //end if\n\n    } //end for i\n\n    //This for loop places nonzeros values in the k^th row\n    for(i = (nnzY-1); i >=0; i--){\n\n      //which column are we working on?\n      cidx = yIdx[i];\n\n      // loop along the elements in this\n      // column of L and subtract to solve to y\n      tmpIdx = LNextSpaceInCol[cidx];\n      yVals_cidx = yVals[cidx];\n      for(j = Lp[cidx]; j < tmpIdx; j++){\n        yVals[Li[j]] -= Lx[j]*yVals_cidx;\n      }\n\n      //Now I have the cidx^th element of y = L\\b.\n      //so compute the corresponding element of\n      //this row of L and put it into the right place\n      Li[tmpIdx] = k;\n      Lx[tmpIdx] = yVals_cidx *Dinv[cidx];\n\n      //D[k] -= yVals[cidx]*yVals[cidx]*Dinv[cidx];\n      D[k] -= yVals_cidx*Lx[tmpIdx];\n      LNextSpaceInCol[cidx]++;\n\n      //reset the yvalues and indices back to zero and QDLDL_UNUSED\n      //once I'm done with them\n      yVals[cidx]     = 0.0;\n      yMarkers[cidx]  = QDLDL_UNUSED;\n\n    } //end for i\n\n    //Maintain a count of the positive entries\n    //in D.  If we hit a zero, we can't factor\n    //this matrix, so abort\n    if(D[k] == 0.0){return -1;}\n    if(D[k]  > 0.0){positiveValuesInD++;}\n\n    //compute the inverse of the diagonal\n    Dinv[k]= 1/D[k];\n\n  } //end for k\n\n  return positiveValuesInD;\n\n}\n\n// Solves (L+I)x = b\nvoid QDLDL_Lsolve(const QDLDL_int    n,\n                  const QDLDL_int*   Lp,\n                  const QDLDL_int*   Li,\n                  const QDLDL_float* Lx,\n                  QDLDL_float* x){\n\nQDLDL_int i,j;\n  for(i = 0; i < n; i++){\n      for(j = Lp[i]; j < Lp[i+1]; j++){\n          x[Li[j]] -= Lx[j]*x[i];\n      }\n  }\n}\n\n// Solves (L+I)'x = b\nvoid QDLDL_Ltsolve(const QDLDL_int    n,\n                   const QDLDL_int*   Lp,\n                   const QDLDL_int*   Li,\n                   const QDLDL_float* Lx,\n                   QDLDL_float* x){\n\nQDLDL_int i,j;\n  for(i = n-1; i>=0; i--){\n      for(j = Lp[i]; j < Lp[i+1]; j++){\n          x[i] -= Lx[j]*x[Li[j]];\n      }\n  }\n}\n\n// Solves Ax = b where A has given LDL factors\nvoid QDLDL_solve(const QDLDL_int       n,\n                    const QDLDL_int*   Lp,\n                    const QDLDL_int*   Li,\n                    const QDLDL_float* Lx,\n                    const QDLDL_float* Dinv,\n                    QDLDL_float* x){\n\nQDLDL_int i;\n\nQDLDL_Lsolve(n,Lp,Li,Lx,x);\nfor(i = 0; i < n; i++) x[i] *= Dinv[i];\nQDLDL_Ltsolve(n,Lp,Li,Lx,x);\n\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/tests/CMakeLists.txt",
    "content": "set(test_headers\n\t${CMAKE_CURRENT_SOURCE_DIR}/test_basic.h\n\t${CMAKE_CURRENT_SOURCE_DIR}/test_identity.h\n\t${CMAKE_CURRENT_SOURCE_DIR}/test_rank_deficient.h\n\t${CMAKE_CURRENT_SOURCE_DIR}/test_singleton.h\n\t${CMAKE_CURRENT_SOURCE_DIR}/test_sym_structure.h\n\t${CMAKE_CURRENT_SOURCE_DIR}/test_tril_structure.h\n\t${CMAKE_CURRENT_SOURCE_DIR}/test_two_by_two.h\n\t${CMAKE_CURRENT_SOURCE_DIR}/test_zero_on_diag.h\n\t${CMAKE_CURRENT_SOURCE_DIR}/test_osqp_kkt.h\n\tPARENT_SCOPE)\n\n# Include this directory for test headers\ninclude_directories(${CMAKE_CURRENT_SOURCE_DIR})\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/tests/minunit.h",
    "content": "/* QDLDL TESTER MODULE */\n\n/* THE CODE FOR MINIMAL UNIT TESTING HAS BEEN TAKEN FROM\n   http://www.jera.com/techinfo/jtns/jtn002.html */\n\n#define mu_assert(message, test) \\\n  do { if (!(test)) return message; } while (0)\n#define mu_run_test(test)                   \\\n  do { char *message = test(); tests_run++; \\\n       if (message) return message; } while (0)\nextern int tests_run;\n\n#define QDLDL_TESTS_TOL 1e-4 // Define tests tolerance\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/tests/qdldl_tester.c",
    "content": "/* QDLDL TESTER MODULE */\n\n/* THE CODE FOR MINIMAL UNIT TESTING HAS BEEN TAKEN FROM\n   http://www.jera.com/techinfo/jtns/jtn002.html */\n\n#include <stdio.h>\n#include <stdlib.h>\n#include \"minunit.h\"\n#include \"qdldl.h\"\n\n//utility functions for solves\nQDLDL_float vec_diff_norm(QDLDL_float* x, QDLDL_float* y, QDLDL_int len);\nint ldl_factor_solve(QDLDL_int An, QDLDL_int* Ap,QDLDL_int* Ai,\n                     QDLDL_float* Ax,QDLDL_float* b);\n\n// Include tests\n#include \"test_basic.h\"\n#include \"test_identity.h\"\n#include \"test_rank_deficient.h\"\n#include \"test_singleton.h\"\n#include \"test_sym_structure.h\"\n#include \"test_tril_structure.h\"\n#include \"test_two_by_two.h\"\n#include \"test_zero_on_diag.h\"\n#include \"test_osqp_kkt.h\"\n\n\nint tests_run = 0;\n\n\nstatic char* all_tests() {\n  mu_run_test(test_basic);\n  mu_run_test(test_identity);\n  mu_run_test(test_rank_deficient);\n  mu_run_test(test_singleton);\n  mu_run_test(test_sym_structure);\n  mu_run_test(test_tril_structure);\n  mu_run_test(test_two_by_two);\n  mu_run_test(test_zero_on_diag);\n  mu_run_test(test_osqp_kkt);\n\n  return 0;\n}\n\n\n\n\n\nQDLDL_float vec_diff_norm(QDLDL_float* x, QDLDL_float* y, QDLDL_int len){\n\n  QDLDL_float maxDiff = 0.0;\n  QDLDL_float elDiff  = 0.0;\n  QDLDL_int i;\n\n  for(i = 0; i < len; i++){\n    elDiff  = x[i] - y[i];\n    maxDiff = (elDiff > maxDiff) ? elDiff : ((-elDiff > maxDiff) ? -elDiff : maxDiff);\n  }\n  return maxDiff;\n\n}\n\nint ldl_factor_solve(QDLDL_int An,\n                     QDLDL_int* Ap,\n                     QDLDL_int* Ai,\n                     QDLDL_float* Ax,\n                     QDLDL_float* b){\n\n  //data for L and D factors\n  QDLDL_int Ln = An;\n  QDLDL_int *Lp;\n  QDLDL_int *Li;\n  QDLDL_float *Lx;\n  QDLDL_float *D;\n  QDLDL_float *Dinv;\n\n  //data for elim tree calculation\n  QDLDL_int *etree;\n  QDLDL_int *Lnz;\n  QDLDL_int  sumLnz;\n\n  //data for factorisation\n  QDLDL_int   *iwork;\n  QDLDL_bool  *bwork;\n  QDLDL_float *fwork;\n  QDLDL_int   factorStatus;\n\n  /*--------------------------------\n   * pre-factorisation memory allocations\n   *---------------------------------*/\n\n  //These can happen *before* the etree is calculated\n  //since the sizes are not sparsity pattern specific\n\n  //For the elimination tree\n  etree = (QDLDL_int*)malloc(sizeof(QDLDL_int)*An);\n  Lnz   = (QDLDL_int*)malloc(sizeof(QDLDL_int)*An);\n\n  //For the L factors.   Li and Lx are sparsity dependent\n  //so must be done after the etree is constructed\n  Lp    = (QDLDL_int*)malloc(sizeof(QDLDL_int)*(An+1));\n  D     = (QDLDL_float*)malloc(sizeof(QDLDL_float)*An);\n  Dinv  = (QDLDL_float*)malloc(sizeof(QDLDL_float)*An);\n\n  //Working memory.  Note that both the etree and factor\n  //calls requires a working vector of QDLDL_int, with\n  //the factor function requiring 3*An elements and the\n  //etree only An elements.   Just allocate the larger\n  //amount here and use it in both places\n  iwork = (QDLDL_int*)malloc(sizeof(QDLDL_int)*(3*An));\n  bwork = (QDLDL_bool*)malloc(sizeof(QDLDL_bool)*An);\n  fwork = (QDLDL_float*)malloc(sizeof(QDLDL_float)*An);\n\n\n  /*--------------------------------\n   * elimination tree calculation\n   *---------------------------------*/\n  sumLnz = QDLDL_etree(An,Ap,Ai,iwork,Lnz,etree);\n\n  //not perfect triu A = bomb\n  if(sumLnz < 0){\n    free(Lp); free(D); free(Dinv); free(etree); free(Lnz);\n    free(iwork); free(bwork); free(fwork);\n    return sumLnz;\n  }\n\n  /*--------------------------------\n   * LDL factorisation\n   *---------------------------------*/\n\n  //First allocate memory for Li and Lx\n  Li    = (QDLDL_int*)malloc(sizeof(QDLDL_int)*sumLnz);\n  Lx    = (QDLDL_float*)malloc(sizeof(QDLDL_float)*sumLnz);\n\n  //now factor\n  factorStatus = QDLDL_factor(An,Ap,Ai,Ax,Lp,Li,Lx,D,Dinv,Lnz,etree,bwork,iwork,fwork);\n\n  //Zero on the diagonal = bomb\n  if(factorStatus < 0){\n    free(Lp); free(D); free(Dinv); free(etree); free(Lnz);\n    free(iwork); free(bwork); free(fwork); free(Li); free(Lx);\n    return factorStatus;\n  }\n\n  /*--------------------------------\n   * solve\n   *---------------------------------*/\n  QDLDL_solve(Ln,Lp,Li,Lx,Dinv,b);\n\n\n  /*--------------------------------\n   * clean up\n   *---------------------------------*/\n   free(Lp); free(D); free(Dinv); free(etree); free(Lnz);\n   free(iwork); free(bwork); free(fwork); free(Li); free(Lx);\n\n   return 0 ;\n\n}\n\n\n\n\nint main(void) {\n  char *result = all_tests();\n\n  if (result != 0) {\n    printf(\"%s\\n\", result);\n  }\n  else {\n    printf(\"ALL TESTS PASSED\\n\");\n  }\n  printf(\"Tests run: %d\\n\", tests_run);\n\n  return result != 0;\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/tests/test_basic.h",
    "content": "static char* test_basic()\n{\n  //A matrix data\n  QDLDL_int Ap[]  = {0, 1, 2, 4, 5, 6, 8, 10, 12, 14, 17};\n  QDLDL_int Ai[]  = {0, 1, 1, 2, 3, 4, 1, 5, 0, 6, 3, 7, 6, 8, 1, 2, 9};\n  QDLDL_float Ax[] = {1.0, 0.460641, -0.121189, 0.417928, 0.177828,\n                      0.1, -0.0290058, -1.0, 0.350321, -0.441092, -0.0845395,\n                      -0.316228, 0.178663, -0.299077, 0.182452, -1.56506, -0.1};\n  QDLDL_int An = 10;\n\n  // RHS and solution to Ax = b\n  QDLDL_float b[]    = {1,2,3,4,5,6,7,8,9,10};\n  QDLDL_float xsol[] = {10.2171, 3.9416, -5.69096, 9.28661, 50.0, -6.11433,\n                     -26.3104, -27.7809, -45.8099, -3.74178};\n\n  //x replaces b during solve\n  int status = ldl_factor_solve(An,Ap,Ai,Ax,b);\n\n  mu_assert(\"Factorisation failed\", status >= 0);\n  mu_assert(\"Solve accuracy failed\", vec_diff_norm(b,xsol,An) < QDLDL_TESTS_TOL);\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/tests/test_identity.h",
    "content": "static char* test_identity()\n{\n  //A matrix data\n  QDLDL_int Ap[]  = {0, 1, 2, 3, 4};\n  QDLDL_int Ai[]  = {0, 1, 2, 3};\n  QDLDL_float Ax[] = {1.0, 1.0, 1.0, 1.0};\n  QDLDL_int An = 4;\n\n  // RHS and solution to Ax = b\n  QDLDL_float b[]    = {2,2,2,2};\n  QDLDL_float xsol[] = {2,2,2,2};\n\n  //x replaces b during solve\n  int status = ldl_factor_solve(An,Ap,Ai,Ax,b);\n\n  mu_assert(\"Factorisation failed\", status >= 0);\n  mu_assert(\"Solve accuracy failed\", vec_diff_norm(b,xsol,An) < QDLDL_TESTS_TOL);\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/tests/test_osqp_kkt.h",
    "content": "static char* test_osqp_kkt()\n{\n  // Unordered A\n  QDLDL_int Ap[]  = {0, 1, 2, 5, 6, 7, 8, 12};\n  QDLDL_int Ai[]  = {0, 1, 2, 1, 0, 3, 4, 5, 5, 6, 4, 3};\n  QDLDL_float Ax[] = {-0.25,  -0.25,   1.0,   0.513578,   0.529142,  -0.25,  -0.25,   1.10274,   0.15538,   1.25883,   0.13458,   0.621134};\n\n  // Ordered A (this works)\n  // QDLDL_int Ap[]  = {0, 1, 2, 5, 6, 7, 8, 12};\n  // QDLDL_int Ai[]  = {0, 1, 0, 1, 2, 3, 4, 5, 3, 4, 5, 6};\n  // QDLDL_float Ax[] = {-0.25, -0.25, 0.529142, 0.513578, 1.0, -0.25, -0.25, 1.10274, 0.621134, 0.13458, 0.15538, 1.25883};\n  QDLDL_int An = 7;\n\n  // RHS and solution to Ax = b\n  QDLDL_float b[]    = {-0.595598, -0.0193715, -0.576156, -0.168746, 0.61543, 0.419073, 1.31087};\n  QDLDL_float xsol[] = {1.13141, -1.1367, -0.591044, 1.68867, -2.24209, 0.32254, 0.407998};\n\n  //x replaces b during solve\n  int status = ldl_factor_solve(An,Ap,Ai,Ax,b);\n\n  mu_assert(\"Factorisation failed\", status >= 0);\n  mu_assert(\"Solve accuracy failed\", vec_diff_norm(b,xsol,An) < QDLDL_TESTS_TOL);\n\n  return 0;\n}\n\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/tests/test_rank_deficient.h",
    "content": "static char* test_rank_deficient()\n{\n  //A matrix data\n  QDLDL_int Ap[]  = {0, 1, 3};\n  QDLDL_int Ai[]  = {0, 0, 1};\n  QDLDL_float Ax[] = {1.0, 1.0, 1.0};\n  QDLDL_int An = 2;\n\n  // RHS for Ax = b (should fail to solve)\n  QDLDL_float b[]    = {1,1};\n\n  //x replaces b during solve\n  int status = ldl_factor_solve(An,Ap,Ai,Ax,b);\n\n  mu_assert(\"Rank deficiency not detected\", status < 0);\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/tests/test_singleton.h",
    "content": "static char* test_singleton()\n{\n  //A matrix data\n  QDLDL_int Ap[]  = {0, 1};\n  QDLDL_int Ai[]  = {0};\n  QDLDL_float Ax[] = {0.2};\n  QDLDL_int An = 1;\n\n  // RHS and solution to Ax = b\n  QDLDL_float b[]    = {2};\n  QDLDL_float xsol[] = {10.0};\n\n  //x replaces b during solve\n  int status = ldl_factor_solve(An,Ap,Ai,Ax,b);\n\n  mu_assert(\"Factorisation failed\", status >= 0);\n  mu_assert(\"Solve accuracy failed\", vec_diff_norm(b,xsol,An) < QDLDL_TESTS_TOL);\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/tests/test_sym_structure.h",
    "content": "static char* test_sym_structure()\n{\n  //A matrix data\n  QDLDL_int Ap[]  = {0, 2, 4};\n  QDLDL_int Ai[]  = {0, 1, 0, 1};\n  QDLDL_float Ax[] = {5.0,1.0,1.0,5.0};\n  QDLDL_int An = 2;\n\n  // RHS for Ax = b\n  QDLDL_float b[]    = {1,1};\n\n  //x replaces b during solve\n  int status = ldl_factor_solve(An,Ap,Ai,Ax,b);\n\n  mu_assert(\"Fully symmetric input not detected\", status < 0);\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/tests/test_tril_structure.h",
    "content": "static char* test_tril_structure()\n{\n  //A matrix data\n  QDLDL_int Ap[]  = {0, 2, 3};\n  QDLDL_int Ai[]  = {0, 1, 1};\n  QDLDL_float Ax[] = {5.0,1.0,5.0};\n  QDLDL_int An = 2;\n\n  // RHS for Ax = b\n  QDLDL_float b[]    = {1,1};\n\n  //x replaces b during solve\n  int status = ldl_factor_solve(An,Ap,Ai,Ax,b);\n\n  mu_assert(\"Tril input not detected\", status < 0);\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/tests/test_two_by_two.h",
    "content": "static char* test_two_by_two()\n{\n  //A matrix data\n  QDLDL_int Ap[]  = {0,1,3};\n  QDLDL_int Ai[]  = {0, 0, 1};\n  QDLDL_float Ax[] = {1.0, 1.0, -1.0};\n  QDLDL_int An = 2;\n\n  // RHS and solution to Ax = b\n  QDLDL_float b[]    = {2,4};\n  QDLDL_float xsol[] = {3,-1};\n\n  //x replaces b during solve\n  int status = ldl_factor_solve(An,Ap,Ai,Ax,b);\n\n  mu_assert(\"Factorisation failed\", status >= 0);\n  mu_assert(\"Solve accuracy failed\", vec_diff_norm(b,xsol,An) < QDLDL_TESTS_TOL);\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/lin_sys/direct/qdldl/qdldl_sources/tests/test_zero_on_diag.h",
    "content": "static char* test_zero_on_diag()\n{\n  //A matrix data\n  QDLDL_int Ap[]  = {0, 1, 2, 5};\n  QDLDL_int Ai[]  = {0 ,0, 0, 1, 2};\n  QDLDL_float Ax[] = {4,1,2,1,-3};\n  QDLDL_int An = 3;\n\n  // RHS and solution to Ax = b\n  QDLDL_float b[]    = {6,9,12};\n  QDLDL_float xsol[] = {17,-46,-8};\n\n  //x replaces b during solve (should fill due to zero in middle)\n  //NB : this system is solvable, but not by LDL\n  int status = ldl_factor_solve(An,Ap,Ai,Ax,b);\n\n  mu_assert(\"Factorisation failed\", status >= 0);\n  mu_assert(\"Solve accuracy failed\", vec_diff_norm(b,xsol,An) < QDLDL_TESTS_TOL);\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/lin_sys/lib_handler.c",
    "content": "#include \"lib_handler.h\"\n#include <ctype.h> // Needed for tolower functions\n\n#include \"constants.h\"\n#include \"util.h\"\n\nsoHandle_t lh_load_lib(const char *libName) {\n    soHandle_t h = OSQP_NULL;\n\n    if (!libName) {\n        #ifdef PRINTING\n        c_eprint(\"no library name given\");\n        #endif\n        return OSQP_NULL;\n    }\n\n#ifdef IS_WINDOWS\n    h = LoadLibrary (libName);\n    if (!h) {\n        #ifdef PRINTING\n        c_eprint(\"Windows error while loading dynamic library %s, error = %d\",\n                libName, (int)GetLastError());\n        #endif\n    }\n#else\n    h = dlopen (libName, RTLD_LAZY);\n    if (!h) {\n        #ifdef PRINTING\n        c_eprint(\"Error while loading dynamic library %s: %s\", libName, dlerror());\n        #endif\n    }\n#endif\n\n    return h;\n} /* lh_load_lib */\n\n\nc_int lh_unload_lib (soHandle_t h) {\n    c_int rc = 1;\n\n#ifdef IS_WINDOWS\n    rc = FreeLibrary (h);\n    rc = ! rc;\n#else\n    rc = dlclose (h);\n#endif\n\n    return rc;\n} /* LSL_unLoadLib */\n\n\n#ifdef IS_WINDOWS\ntypedef FARPROC symtype;\n#else\ntypedef void* symtype;\n#endif\n/** Loads a symbol from a dynamically linked library.\n * This function is not defined in the header to allow a workaround for the problem that dlsym returns an object instead of a function pointer.\n * However, Windows also needs special care.\n *\n * The method does six attempts to load the symbol. Next to its given name, it also tries variations of lower case and upper case form and with an extra underscore.\n * @param h Handle of dynamically linked library.\n * @param symName Name of the symbol to load.\n * @return A pointer to the symbol, or OSQP_NULL if not found.\n */\nsymtype lh_load_sym (soHandle_t h, const char *symName) {\n    symtype s;\n    const char *from;\n    char *to;\n    const char *tripSym;\n    char* err;\n    char lcbuf[257];\n    char ucbuf[257];\n    char ocbuf[257];\n    size_t symLen;\n    int trip;\n\n    s = OSQP_NULL;\n    err = OSQP_NULL;\n\n    /* search in this order:\n     *  1. original\n     *  2. lower_\n     *  3. upper_\n     *  4. original_\n     *  5. lower\n     *  6. upper\n     */\n\n    symLen = 0;\n    for (trip = 1;  trip <= 6;  trip++) {\n        switch (trip) {\n        case 1:                             /* original */\n            tripSym = symName;\n            break;\n        case 2:                             /* lower_ */\n            for (from = symName, to = lcbuf;  *from;  from++, to++) {\n                *to = tolower(*from);\n            }\n            symLen = from - symName;\n            *to++ = '_';\n            *to = '\\0';\n            tripSym = lcbuf;\n            break;\n        case 3:                             /* upper_ */\n            for (from = symName, to = ucbuf;  *from;  from++, to++) {\n                *to = toupper(*from);\n            }\n            *to++ = '_';\n            *to = '\\0';\n            tripSym = ucbuf;\n            break;\n        case 4:                             /* original_ */\n            c_strcpy(ocbuf, symName);\n            ocbuf[symLen] = '_';\n            ocbuf[symLen+1] = '\\0';\n            tripSym = ocbuf;\n            break;\n        case 5:                             /* lower */\n            lcbuf[symLen] = '\\0';\n            tripSym = lcbuf;\n            break;\n        case 6:                             /* upper */\n            ucbuf[symLen] = '\\0';\n            tripSym = ucbuf;\n            break;\n        default:\n            tripSym = symName;\n        } /* end switch */\n#ifdef IS_WINDOWS\n        s = GetProcAddress (h, tripSym);\n        if (s) {\n            return s;\n        } else {\n            #ifdef PRINTING\n            c_eprint(\"Cannot find symbol %s in dynamic library, error = %d\",\n                    symName, (int)GetLastError());\n            #endif\n        }\n#else\n        s = dlsym (h, tripSym);\n        err = dlerror();  /* we have only one chance; a successive call to dlerror() returns OSQP_NULL */\n        if (err) {\n            #ifdef PRINTING\n            c_eprint(\"Cannot find symbol %s in dynamic library, error = %s\",\n                    symName, err);\n            #endif\n        } else {\n            return s;\n        }\n#endif\n    } /* end loop over symbol name variations */\n\n    return OSQP_NULL;\n} /* lh_load_sym */\n"
  },
  {
    "path": "osqp/lin_sys/lib_handler.h",
    "content": "#ifndef LIB_HANDLER_H\n#define LIB_HANDLER_H\n\n#include \"glob_opts.h\"\n\n#ifdef IS_WINDOWS\n#include <windows.h>\ntypedef HINSTANCE soHandle_t;\n#else\n#include <unistd.h>\n#include <dlfcn.h>\ntypedef void *soHandle_t;\n#endif\n\n#ifdef IS_WINDOWS\n#define SHAREDLIBEXT \"dll\"\n#elif defined IS_MAC\n#define SHAREDLIBEXT \"dylib\"\n#else\n#define SHAREDLIBEXT \"so\"\n#endif\n\n\n// NB: A shared library should be put into the shared library search path:\n//  Linux:    LD_LIBRARY_PATH\n//  Mac OSX:  DYLD_LIBRARY_PATH\n//  Windows:  PATH\n\n\n/** Loads a dynamically linked library.\n * @param libname The name of the library to load.\n * @return Shared library handle, or OSQP_NULL if failure.\n */\nsoHandle_t lh_load_lib(const char* libname);\n\n\n/** Unloads a shared library.\n * @param libhandle Handle of shared library to unload.\n * @return Zero on success, nonzero on failure.\n */\nc_int lh_unload_lib(soHandle_t libhandle);\n\n\n#endif /*LIB_HANDLER_H*/\n"
  },
  {
    "path": "osqp/src/CMakeLists.txt",
    "content": "# Add the OSQP sources\nset(\n    osqp_src\n    \"${CMAKE_CURRENT_SOURCE_DIR}/auxil.c\"\n    \"${CMAKE_CURRENT_SOURCE_DIR}/error.c\"\n    \"${CMAKE_CURRENT_SOURCE_DIR}/lin_alg.c\"\n    \"${CMAKE_CURRENT_SOURCE_DIR}/osqp.c\"\n    \"${CMAKE_CURRENT_SOURCE_DIR}/proj.c\"\n    \"${CMAKE_CURRENT_SOURCE_DIR}/scaling.c\"\n    \"${CMAKE_CURRENT_SOURCE_DIR}/util.c\"\n)\n\n# Add the KKT update only in normal mode and matrix-updating embedded mode (not mode 1)\nif (NOT (EMBEDDED EQUAL 1))\n    list(\n      APPEND\n      osqp_src\n      \"${CMAKE_CURRENT_SOURCE_DIR}/kkt.c\"\n    )\nendif()\n\n# Add more files that should only be in non-embedded code\nif (NOT DEFINED EMBEDDED)\n    list(\n      APPEND\n      osqp_src\n      \"${CMAKE_CURRENT_SOURCE_DIR}/cs.c\"\n      \"${CMAKE_CURRENT_SOURCE_DIR}/polish.c\"\n      \"${CMAKE_CURRENT_SOURCE_DIR}/lin_sys.c\"\n    )\nendif()\n\n# Add the ctrl-c handler if enabled\nif (CTRLC)\n    list(\n      APPEND\n      osqp_src\n      \"${CMAKE_CURRENT_SOURCE_DIR}/ctrlc.c\"\n    )\nendif()\n\n# Pass the source list up to the main CMakeLists scope\nset(\n  osqp_src\n  \"${osqp_src}\"\n  PARENT_SCOPE\n)\n"
  },
  {
    "path": "osqp/src/auxil.c",
    "content": "#include \"osqp.h\" // For OSQP rho update\n#include \"auxil.h\"\n#include \"proj.h\"\n#include \"lin_alg.h\"\n#include \"constants.h\"\n#include \"scaling.h\"\n#include \"util.h\"\n\n/***********************************************************\n* Auxiliary functions needed to compute ADMM iterations * *\n***********************************************************/\n#if EMBEDDED != 1\nc_float compute_rho_estimate(OSQPWorkspace *work) {\n  c_int   n, m;                       // Dimensions\n  c_float pri_res, dua_res;           // Primal and dual residuals\n  c_float pri_res_norm, dua_res_norm; // Normalization for the residuals\n  c_float temp_res_norm;              // Temporary residual norm\n  c_float rho_estimate;               // Rho estimate value\n\n  // Get problem dimensions\n  n = work->data->n;\n  m = work->data->m;\n\n  // Get primal and dual residuals\n  pri_res = vec_norm_inf(work->z_prev, m);\n  dua_res = vec_norm_inf(work->x_prev, n);\n\n  // Normalize primal residual\n  pri_res_norm  = vec_norm_inf(work->z, m);           // ||z||\n  temp_res_norm = vec_norm_inf(work->Ax, m);          // ||Ax||\n  pri_res_norm  = c_max(pri_res_norm, temp_res_norm); // max (||z||,||Ax||)\n  pri_res      /= (pri_res_norm + 1e-10);             // Normalize primal\n                                                      // residual (prevent 0\n                                                      // division)\n\n  // Normalize dual residual\n  dua_res_norm  = vec_norm_inf(work->data->q, n);     // ||q||\n  temp_res_norm = vec_norm_inf(work->Aty, n);         // ||A' y||\n  dua_res_norm  = c_max(dua_res_norm, temp_res_norm);\n  temp_res_norm = vec_norm_inf(work->Px, n);          //  ||P x||\n  dua_res_norm  = c_max(dua_res_norm, temp_res_norm); // max(||q||,||A' y||,||P\n                                                      // x||)\n  dua_res      /= (dua_res_norm + 1e-10);             // Normalize dual residual\n                                                      // (prevent 0 division)\n\n\n  // Return rho estimate\n  rho_estimate = work->settings->rho * c_sqrt(pri_res / (dua_res + 1e-10)); // (prevent\n                                                                            // 0\n                                                                            // division)\n  rho_estimate = c_min(c_max(rho_estimate, RHO_MIN), RHO_MAX);              // Constrain\n                                                                            // rho\n                                                                            // values\n  return rho_estimate;\n}\n\nc_int adapt_rho(OSQPWorkspace *work) {\n  c_int   exitflag; // Exitflag\n  c_float rho_new;  // New rho value\n\n  exitflag = 0;     // Initialize exitflag to 0\n\n  // Compute new rho\n  rho_new = compute_rho_estimate(work);\n\n  // Set rho estimate in info\n  work->info->rho_estimate = rho_new;\n\n  // Check if the new rho is large or small enough and update it in case\n  if ((rho_new > work->settings->rho * work->settings->adaptive_rho_tolerance) ||\n      (rho_new < work->settings->rho /  work->settings->adaptive_rho_tolerance)) {\n    exitflag                 = osqp_update_rho(work, rho_new);\n    work->info->rho_updates += 1;\n  }\n\n  return exitflag;\n}\n\nvoid set_rho_vec(OSQPWorkspace *work) {\n  c_int i;\n\n  work->settings->rho = c_min(c_max(work->settings->rho, RHO_MIN), RHO_MAX);\n\n  for (i = 0; i < work->data->m; i++) {\n    if ((work->data->l[i] < -OSQP_INFTY * MIN_SCALING) &&\n        (work->data->u[i] > OSQP_INFTY * MIN_SCALING)) {\n      // Loose bounds\n      work->constr_type[i] = -1;\n      work->rho_vec[i]     = RHO_MIN;\n    } else if (work->data->u[i] - work->data->l[i] < RHO_TOL) {\n      // Equality constraints\n      work->constr_type[i] = 1;\n      work->rho_vec[i]     = RHO_EQ_OVER_RHO_INEQ * work->settings->rho;\n    } else {\n      // Inequality constraints\n      work->constr_type[i] = 0;\n      work->rho_vec[i]     = work->settings->rho;\n    }\n    work->rho_inv_vec[i] = 1. / work->rho_vec[i];\n  }\n}\n\nc_int update_rho_vec(OSQPWorkspace *work) {\n  c_int i, exitflag, constr_type_changed;\n\n  exitflag            = 0;\n  constr_type_changed = 0;\n\n  for (i = 0; i < work->data->m; i++) {\n    if ((work->data->l[i] < -OSQP_INFTY * MIN_SCALING) &&\n        (work->data->u[i] > OSQP_INFTY * MIN_SCALING)) {\n      // Loose bounds\n      if (work->constr_type[i] != -1) {\n        work->constr_type[i] = -1;\n        work->rho_vec[i]     = RHO_MIN;\n        work->rho_inv_vec[i] = 1. / RHO_MIN;\n        constr_type_changed  = 1;\n      }\n    } else if (work->data->u[i] - work->data->l[i] < RHO_TOL) {\n      // Equality constraints\n      if (work->constr_type[i] != 1) {\n        work->constr_type[i] = 1;\n        work->rho_vec[i]     = RHO_EQ_OVER_RHO_INEQ * work->settings->rho;\n        work->rho_inv_vec[i] = 1. / work->rho_vec[i];\n        constr_type_changed  = 1;\n      }\n    } else {\n      // Inequality constraints\n      if (work->constr_type[i] != 0) {\n        work->constr_type[i] = 0;\n        work->rho_vec[i]     = work->settings->rho;\n        work->rho_inv_vec[i] = 1. / work->settings->rho;\n        constr_type_changed  = 1;\n      }\n    }\n  }\n\n  // Update rho_vec in KKT matrix if constraints type has changed\n  if (constr_type_changed == 1) {\n    exitflag = work->linsys_solver->update_rho_vec(work->linsys_solver,\n                                                   work->rho_vec);\n  }\n\n  return exitflag;\n}\n\n#endif // EMBEDDED != 1\n\n\nvoid swap_vectors(c_float **a, c_float **b) {\n  c_float *temp;\n\n  temp = *b;\n  *b   = *a;\n  *a   = temp;\n}\n\nvoid cold_start(OSQPWorkspace *work) {\n  vec_set_scalar(work->x, 0., work->data->n);\n  vec_set_scalar(work->z, 0., work->data->m);\n  vec_set_scalar(work->y, 0., work->data->m);\n}\n\nstatic void compute_rhs(OSQPWorkspace *work) {\n  c_int i; // Index\n\n  for (i = 0; i < work->data->n; i++) {\n    // Cycle over part related to x variables\n    work->xz_tilde[i] = work->settings->sigma * work->x_prev[i] -\n                        work->data->q[i];\n  }\n\n  for (i = 0; i < work->data->m; i++) {\n    // Cycle over dual variable in the first step (nu)\n    work->xz_tilde[i + work->data->n] = work->z_prev[i] - work->rho_inv_vec[i] *\n                                        work->y[i];\n  }\n}\n\nvoid update_xz_tilde(OSQPWorkspace *work) {\n  // Compute right-hand side\n  compute_rhs(work);\n\n  // Solve linear system\n  work->linsys_solver->solve(work->linsys_solver, work->xz_tilde);\n}\n\nvoid update_x(OSQPWorkspace *work) {\n  c_int i;\n\n  // update x\n  for (i = 0; i < work->data->n; i++) {\n    work->x[i] = work->settings->alpha * work->xz_tilde[i] +\n                 ((c_float)1.0 - work->settings->alpha) * work->x_prev[i];\n  }\n\n  // update delta_x\n  for (i = 0; i < work->data->n; i++) {\n    work->delta_x[i] = work->x[i] - work->x_prev[i];\n  }\n}\n\nvoid update_z(OSQPWorkspace *work) {\n  c_int i;\n\n  // update z\n  for (i = 0; i < work->data->m; i++) {\n    work->z[i] = work->settings->alpha * work->xz_tilde[i + work->data->n] +\n                 ((c_float)1.0 - work->settings->alpha) * work->z_prev[i] +\n                 work->rho_inv_vec[i] * work->y[i];\n  }\n\n  // project z\n  project(work, work->z);\n}\n\nvoid update_y(OSQPWorkspace *work) {\n  c_int i; // Index\n\n  for (i = 0; i < work->data->m; i++) {\n    work->delta_y[i] = work->rho_vec[i] *\n                       (work->settings->alpha *\n                        work->xz_tilde[i + work->data->n] +\n                        ((c_float)1.0 - work->settings->alpha) * work->z_prev[i] -\n                        work->z[i]);\n    work->y[i] += work->delta_y[i];\n  }\n}\n\nc_float compute_obj_val(OSQPWorkspace *work, c_float *x) {\n  c_float obj_val;\n\n  obj_val = quad_form(work->data->P, x) +\n            vec_prod(work->data->q, x, work->data->n);\n\n  if (work->settings->scaling) {\n    obj_val *= work->scaling->cinv;\n  }\n\n  return obj_val;\n}\n\nc_float compute_pri_res(OSQPWorkspace *work, c_float *x, c_float *z) {\n  // NB: Use z_prev as working vector\n  // pr = Ax - z\n\n  mat_vec(work->data->A, x, work->Ax, 0); // Ax\n  vec_add_scaled(work->z_prev, work->Ax, z, work->data->m, -1);\n\n  // If scaling active -> rescale residual\n  if (work->settings->scaling && !work->settings->scaled_termination) {\n    return vec_scaled_norm_inf(work->scaling->Einv, work->z_prev, work->data->m);\n  }\n\n  // Return norm of the residual\n  return vec_norm_inf(work->z_prev, work->data->m);\n}\n\nc_float compute_pri_tol(OSQPWorkspace *work, c_float eps_abs, c_float eps_rel) {\n  c_float max_rel_eps, temp_rel_eps;\n\n  // max_rel_eps = max(||z||, ||A x||)\n  if (work->settings->scaling && !work->settings->scaled_termination) {\n    // ||Einv * z||\n    max_rel_eps =\n      vec_scaled_norm_inf(work->scaling->Einv, work->z, work->data->m);\n\n    // ||Einv * A * x||\n    temp_rel_eps = vec_scaled_norm_inf(work->scaling->Einv,\n                                       work->Ax,\n                                       work->data->m);\n\n    // Choose maximum\n    max_rel_eps = c_max(max_rel_eps, temp_rel_eps);\n  } else { // No unscaling required\n    // ||z||\n    max_rel_eps = vec_norm_inf(work->z, work->data->m);\n\n    // ||A * x||\n    temp_rel_eps = vec_norm_inf(work->Ax, work->data->m);\n\n    // Choose maximum\n    max_rel_eps = c_max(max_rel_eps, temp_rel_eps);\n  }\n\n  // eps_prim\n  return eps_abs + eps_rel * max_rel_eps;\n}\n\nc_float compute_dua_res(OSQPWorkspace *work, c_float *x, c_float *y) {\n  // NB: Use x_prev as temporary vector\n  // NB: Only upper triangular part of P is stored.\n  // dr = q + A'*y + P*x\n\n  // dr = q\n  prea_vec_copy(work->data->q, work->x_prev, work->data->n);\n\n  // P * x (upper triangular part)\n  mat_vec(work->data->P, x, work->Px, 0);\n\n  // P' * x (lower triangular part with no diagonal)\n  mat_tpose_vec(work->data->P, x, work->Px, 1, 1);\n\n  // dr += P * x (full P matrix)\n  vec_add_scaled(work->x_prev, work->x_prev, work->Px, work->data->n, 1);\n\n  // dr += A' * y\n  if (work->data->m > 0) {\n    mat_tpose_vec(work->data->A, y, work->Aty, 0, 0);\n    vec_add_scaled(work->x_prev, work->x_prev, work->Aty, work->data->n, 1);\n  }\n\n  // If scaling active -> rescale residual\n  if (work->settings->scaling && !work->settings->scaled_termination) {\n    return work->scaling->cinv * vec_scaled_norm_inf(work->scaling->Dinv,\n                                                     work->x_prev,\n                                                     work->data->n);\n  }\n\n  return vec_norm_inf(work->x_prev, work->data->n);\n}\n\nc_float compute_dua_tol(OSQPWorkspace *work, c_float eps_abs, c_float eps_rel) {\n  c_float max_rel_eps, temp_rel_eps;\n\n  // max_rel_eps = max(||q||, ||A' y|, ||P x||)\n  if (work->settings->scaling && !work->settings->scaled_termination) {\n    // || Dinv q||\n    max_rel_eps = vec_scaled_norm_inf(work->scaling->Dinv,\n                                      work->data->q,\n                                      work->data->n);\n\n    // || Dinv A' y ||\n    temp_rel_eps = vec_scaled_norm_inf(work->scaling->Dinv,\n                                       work->Aty,\n                                       work->data->n);\n    max_rel_eps = c_max(max_rel_eps, temp_rel_eps);\n\n    // || Dinv P x||\n    temp_rel_eps = vec_scaled_norm_inf(work->scaling->Dinv,\n                                       work->Px,\n                                       work->data->n);\n    max_rel_eps = c_max(max_rel_eps, temp_rel_eps);\n\n    // Multiply by cinv\n    max_rel_eps *= work->scaling->cinv;\n  } else { // No scaling required\n    // ||q||\n    max_rel_eps = vec_norm_inf(work->data->q, work->data->n);\n\n    // ||A'*y||\n    temp_rel_eps = vec_norm_inf(work->Aty, work->data->n);\n    max_rel_eps  = c_max(max_rel_eps, temp_rel_eps);\n\n    // ||P*x||\n    temp_rel_eps = vec_norm_inf(work->Px, work->data->n);\n    max_rel_eps  = c_max(max_rel_eps, temp_rel_eps);\n  }\n\n  // eps_dual\n  return eps_abs + eps_rel * max_rel_eps;\n}\n\nc_int is_primal_infeasible(OSQPWorkspace *work, c_float eps_prim_inf) {\n  // This function checks for the primal infeasibility termination criteria.\n  //\n  // 1) A' * delta_y < eps * ||delta_y||\n  //\n  // 2) u'*max(delta_y, 0) + l'*min(delta_y, 0) < -eps * ||delta_y||\n  //\n\n  c_int i; // Index for loops\n  c_float norm_delta_y;\n  c_float ineq_lhs = 0.0;\n\n  // Project delta_y onto the polar of the recession cone of [l,u]\n  for (i = 0; i < work->data->m; i++) {\n    if (work->data->u[i] > OSQP_INFTY * MIN_SCALING) {          // Infinite upper bound\n      if (work->data->l[i] < -OSQP_INFTY * MIN_SCALING) {       // Infinite lower bound\n        // Both bounds infinite\n        work->delta_y[i] = 0.0;\n      } else {\n        // Only upper bound infinite\n        work->delta_y[i] = c_min(work->delta_y[i], 0.0);\n      }\n    } else if (work->data->l[i] < -OSQP_INFTY * MIN_SCALING) {  // Infinite lower bound\n      // Only lower bound infinite\n      work->delta_y[i] = c_max(work->delta_y[i], 0.0);\n    }\n  }\n\n  // Compute infinity norm of delta_y (unscale if necessary)\n  if (work->settings->scaling && !work->settings->scaled_termination) {\n    // Use work->Adelta_x as temporary vector\n    vec_ew_prod(work->scaling->E, work->delta_y, work->Adelta_x, work->data->m);\n    norm_delta_y = vec_norm_inf(work->Adelta_x, work->data->m);\n  } else {\n    norm_delta_y = vec_norm_inf(work->delta_y, work->data->m);\n  }\n\n  if (norm_delta_y > eps_prim_inf) { // ||delta_y|| > 0\n\n    for (i = 0; i < work->data->m; i++) {\n      ineq_lhs += work->data->u[i] * c_max(work->delta_y[i], 0) + \\\n                  work->data->l[i] * c_min(work->delta_y[i], 0);\n    }\n\n    // Check if the condition is satisfied: ineq_lhs < -eps\n    if (ineq_lhs < -eps_prim_inf * norm_delta_y) {\n      // Compute and return ||A'delta_y|| < eps_prim_inf\n      mat_tpose_vec(work->data->A, work->delta_y, work->Atdelta_y, 0, 0);\n\n      // Unscale if necessary\n      if (work->settings->scaling && !work->settings->scaled_termination) {\n        vec_ew_prod(work->scaling->Dinv,\n                    work->Atdelta_y,\n                    work->Atdelta_y,\n                    work->data->n);\n      }\n\n      return vec_norm_inf(work->Atdelta_y, work->data->n) < eps_prim_inf * norm_delta_y;\n    }\n  }\n\n  // Conditions not satisfied -> not primal infeasible\n  return 0;\n}\n\nc_int is_dual_infeasible(OSQPWorkspace *work, c_float eps_dual_inf) {\n  // This function checks for the scaled dual infeasibility termination\n  // criteria.\n  //\n  // 1) q * delta_x < - eps * || delta_x ||\n  //\n  // 2) ||P * delta_x || < eps * || delta_x ||\n  //\n  // 3) -> (A * delta_x)_i > -eps * || delta_x ||,    l_i != -inf\n  //    -> (A * delta_x)_i <  eps * || delta_x ||,    u_i != inf\n  //\n\n\n  c_int   i; // Index for loops\n  c_float norm_delta_x;\n  c_float cost_scaling;\n\n  // Compute norm of delta_x\n  if (work->settings->scaling && !work->settings->scaled_termination) { // Unscale\n                                                                        // if\n                                                                        // necessary\n    norm_delta_x = vec_scaled_norm_inf(work->scaling->D,\n                                       work->delta_x,\n                                       work->data->n);\n    cost_scaling = work->scaling->c;\n  } else {\n    norm_delta_x = vec_norm_inf(work->delta_x, work->data->n);\n    cost_scaling = 1.0;\n  }\n\n  // Prevent 0 division || delta_x || > 0\n  if (norm_delta_x > eps_dual_inf) {\n    // Normalize delta_x by its norm\n\n    /* vec_mult_scalar(work->delta_x, 1./norm_delta_x, work->data->n); */\n\n    // Check first if q'*delta_x < 0\n    if (vec_prod(work->data->q, work->delta_x, work->data->n) <\n        -cost_scaling * eps_dual_inf * norm_delta_x) {\n      // Compute product P * delta_x (NB: P is store in upper triangular form)\n      mat_vec(work->data->P, work->delta_x, work->Pdelta_x, 0);\n      mat_tpose_vec(work->data->P, work->delta_x, work->Pdelta_x, 1, 1);\n\n      // Scale if necessary\n      if (work->settings->scaling && !work->settings->scaled_termination) {\n        vec_ew_prod(work->scaling->Dinv,\n                    work->Pdelta_x,\n                    work->Pdelta_x,\n                    work->data->n);\n      }\n\n      // Check if || P * delta_x || = 0\n      if (vec_norm_inf(work->Pdelta_x, work->data->n) <\n          cost_scaling * eps_dual_inf * norm_delta_x) {\n        // Compute A * delta_x\n        mat_vec(work->data->A, work->delta_x, work->Adelta_x, 0);\n\n        // Scale if necessary\n        if (work->settings->scaling && !work->settings->scaled_termination) {\n          vec_ew_prod(work->scaling->Einv,\n                      work->Adelta_x,\n                      work->Adelta_x,\n                      work->data->m);\n        }\n\n        // De Morgan Law Applied to dual infeasibility conditions for A * x\n        // NB: Note that MIN_SCALING is used to adjust the infinity value\n        //     in case the problem is scaled.\n        for (i = 0; i < work->data->m; i++) {\n          if (((work->data->u[i] < OSQP_INFTY * MIN_SCALING) &&\n               (work->Adelta_x[i] >  eps_dual_inf * norm_delta_x)) ||\n              ((work->data->l[i] > -OSQP_INFTY * MIN_SCALING) &&\n               (work->Adelta_x[i] < -eps_dual_inf * norm_delta_x))) {\n            // At least one condition not satisfied -> not dual infeasible\n            return 0;\n          }\n        }\n\n        // All conditions passed -> dual infeasible\n        return 1;\n      }\n    }\n  }\n\n  // Conditions not satisfied -> not dual infeasible\n  return 0;\n}\n\nc_int has_solution(OSQPInfo * info){\n\n  return ((info->status_val != OSQP_PRIMAL_INFEASIBLE) &&\n      (info->status_val != OSQP_PRIMAL_INFEASIBLE_INACCURATE) &&\n      (info->status_val != OSQP_DUAL_INFEASIBLE) &&\n      (info->status_val != OSQP_DUAL_INFEASIBLE_INACCURATE) &&\n      (info->status_val != OSQP_NON_CVX));\n\n}\n\nvoid store_solution(OSQPWorkspace *work) {\n#ifndef EMBEDDED\n  c_float norm_vec;\n#endif /* ifndef EMBEDDED */\n\n  if (has_solution(work->info)) {\n    prea_vec_copy(work->x, work->solution->x, work->data->n); // primal\n    prea_vec_copy(work->y, work->solution->y, work->data->m); // dual\n\n    // Unscale solution if scaling has been performed\n    if (work->settings->scaling)\n      unscale_solution(work);\n  } else {\n    // No solution present. Solution is NaN\n    vec_set_scalar(work->solution->x, OSQP_NAN, work->data->n);\n    vec_set_scalar(work->solution->y, OSQP_NAN, work->data->m);\n\n#ifndef EMBEDDED\n\n    // Normalize infeasibility certificates if embedded is off\n    // NB: It requires a division\n    if ((work->info->status_val == OSQP_PRIMAL_INFEASIBLE) ||\n        ((work->info->status_val == OSQP_PRIMAL_INFEASIBLE_INACCURATE))) {\n      norm_vec = vec_norm_inf(work->delta_y, work->data->m);\n      vec_mult_scalar(work->delta_y, 1. / norm_vec, work->data->m);\n    }\n\n    if ((work->info->status_val == OSQP_DUAL_INFEASIBLE) ||\n        ((work->info->status_val == OSQP_DUAL_INFEASIBLE_INACCURATE))) {\n      norm_vec = vec_norm_inf(work->delta_x, work->data->n);\n      vec_mult_scalar(work->delta_x, 1. / norm_vec, work->data->n);\n    }\n\n#endif /* ifndef EMBEDDED */\n\n    // Cold start iterates to 0 for next runs (they cannot start from NaN)\n    cold_start(work);\n  }\n}\n\nvoid update_info(OSQPWorkspace *work,\n                 c_int          iter,\n                 c_int          compute_objective,\n                 c_int          polish) {\n  c_float *x, *z, *y;                   // Allocate pointers to variables\n  c_float *obj_val, *pri_res, *dua_res; // objective value, residuals\n\n#ifdef PROFILING\n  c_float *run_time;                    // Execution time\n#endif /* ifdef PROFILING */\n\n#ifndef EMBEDDED\n\n  if (polish) {\n    x       = work->pol->x;\n    y       = work->pol->y;\n    z       = work->pol->z;\n    obj_val = &work->pol->obj_val;\n    pri_res = &work->pol->pri_res;\n    dua_res = &work->pol->dua_res;\n# ifdef PROFILING\n    run_time = &work->info->polish_time;\n# endif /* ifdef PROFILING */\n  } else {\n#endif // EMBEDDED\n  x                = work->x;\n  y                = work->y;\n  z                = work->z;\n  obj_val          = &work->info->obj_val;\n  pri_res          = &work->info->pri_res;\n  dua_res          = &work->info->dua_res;\n  work->info->iter = iter; // Update iteration number\n#ifdef PROFILING\n  run_time = &work->info->solve_time;\n#endif /* ifdef PROFILING */\n#ifndef EMBEDDED\n}\n\n#endif /* ifndef EMBEDDED */\n\n\n  // Compute the objective if needed\n  if (compute_objective) {\n    *obj_val = compute_obj_val(work, x);\n  }\n\n  // Compute primal residual\n  if (work->data->m == 0) {\n    // No constraints -> Always primal feasible\n    *pri_res = 0.;\n  } else {\n    *pri_res = compute_pri_res(work, x, z);\n  }\n\n  // Compute dual residual\n  *dua_res = compute_dua_res(work, x, y);\n\n  // Update timing\n#ifdef PROFILING\n  *run_time = osqp_toc(work->timer);\n#endif /* ifdef PROFILING */\n\n#ifdef PRINTING\n  work->summary_printed = 0; // The just updated info have not been printed\n#endif /* ifdef PRINTING */\n}\n\n\nvoid reset_info(OSQPInfo *info) {\n#ifdef PROFILING\n\n  // Initialize info values.\n  info->solve_time = 0.0;  // Solve time to zero\n# ifndef EMBEDDED\n  info->polish_time = 0.0; // Polish time to zero\n# endif /* ifndef EMBEDDED */\n\n  // NB: We do not reset the setup_time because it is performed only once\n#endif /* ifdef PROFILING */\n\n  update_status(info, OSQP_UNSOLVED); // Problem is unsolved\n\n#if EMBEDDED != 1\n  info->rho_updates = 0;              // Rho updates are now 0\n#endif /* if EMBEDDED != 1 */\n}\n\nvoid update_status(OSQPInfo *info, c_int status_val) {\n  // Update status value\n  info->status_val = status_val;\n\n  // Update status string depending on status val\n  if (status_val == OSQP_SOLVED) c_strcpy(info->status, \"solved\");\n\n  if (status_val == OSQP_SOLVED_INACCURATE) c_strcpy(info->status,\n                                                     \"solved inaccurate\");\n  else if (status_val == OSQP_PRIMAL_INFEASIBLE) c_strcpy(info->status,\n                                                          \"primal infeasible\");\n  else if (status_val == OSQP_PRIMAL_INFEASIBLE_INACCURATE) c_strcpy(info->status,\n                                                                     \"primal infeasible inaccurate\");\n  else if (status_val == OSQP_UNSOLVED) c_strcpy(info->status, \"unsolved\");\n  else if (status_val == OSQP_DUAL_INFEASIBLE) c_strcpy(info->status,\n                                                        \"dual infeasible\");\n  else if (status_val == OSQP_DUAL_INFEASIBLE_INACCURATE) c_strcpy(info->status,\n                                                                   \"dual infeasible inaccurate\");\n  else if (status_val == OSQP_MAX_ITER_REACHED) c_strcpy(info->status,\n                                                         \"maximum iterations reached\");\n#ifdef PROFILING\n  else if (status_val == OSQP_TIME_LIMIT_REACHED) c_strcpy(info->status,\n                                                           \"run time limit reached\");\n#endif /* ifdef PROFILING */\n  else if (status_val == OSQP_SIGINT) c_strcpy(info->status, \"interrupted\");\n\n  else if (status_val == OSQP_NON_CVX) c_strcpy(info->status, \"problem non convex\");\n\n}\n\nc_int check_termination(OSQPWorkspace *work, c_int approximate) {\n  c_float eps_prim, eps_dual, eps_prim_inf, eps_dual_inf;\n  c_int   exitflag;\n  c_int   prim_res_check, dual_res_check, prim_inf_check, dual_inf_check;\n  c_float eps_abs, eps_rel;\n\n  // Initialize variables to 0\n  exitflag       = 0;\n  prim_res_check = 0; dual_res_check = 0;\n  prim_inf_check = 0; dual_inf_check = 0;\n\n  // Initialize tolerances\n  eps_abs      = work->settings->eps_abs;\n  eps_rel      = work->settings->eps_rel;\n  eps_prim_inf = work->settings->eps_prim_inf;\n  eps_dual_inf = work->settings->eps_dual_inf;\n\n  // If residuals are too large, the problem is probably non convex\n  if ((work->info->pri_res > OSQP_INFTY) ||\n      (work->info->dua_res > OSQP_INFTY)){\n    // Looks like residuals are diverging. Probably the problem is non convex!\n    // Terminate and report it\n    update_status(work->info, OSQP_NON_CVX);\n    work->info->obj_val = OSQP_NAN;\n    return 1;\n  }\n\n  // If approximate solution required, increase tolerances by 10\n  if (approximate) {\n    eps_abs      *= 10;\n    eps_rel      *= 10;\n    eps_prim_inf *= 10;\n    eps_dual_inf *= 10;\n  }\n\n  // Check residuals\n  if (work->data->m == 0) {\n    prim_res_check = 1; // No constraints -> Primal feasibility always satisfied\n  }\n  else {\n    // Compute primal tolerance\n    eps_prim = compute_pri_tol(work, eps_abs, eps_rel);\n\n    // Primal feasibility check\n    if (work->info->pri_res < eps_prim) {\n      prim_res_check = 1;\n    } else {\n      // Primal infeasibility check\n      prim_inf_check = is_primal_infeasible(work, eps_prim_inf);\n    }\n  } // End check if m == 0\n\n  // Compute dual tolerance\n  eps_dual = compute_dua_tol(work, eps_abs, eps_rel);\n\n  // Dual feasibility check\n  if (work->info->dua_res < eps_dual) {\n    dual_res_check = 1;\n  } else {\n    // Check dual infeasibility\n    dual_inf_check = is_dual_infeasible(work, eps_dual_inf);\n  }\n\n  // Compare checks to determine solver status\n  if (prim_res_check && dual_res_check) {\n    // Update final information\n    if (approximate) {\n      update_status(work->info, OSQP_SOLVED_INACCURATE);\n    } else {\n      update_status(work->info, OSQP_SOLVED);\n    }\n    exitflag = 1;\n  }\n  else if (prim_inf_check) {\n    // Update final information\n    if (approximate) {\n      update_status(work->info, OSQP_PRIMAL_INFEASIBLE_INACCURATE);\n    } else {\n      update_status(work->info, OSQP_PRIMAL_INFEASIBLE);\n    }\n\n    if (work->settings->scaling && !work->settings->scaled_termination) {\n      // Update infeasibility certificate\n      vec_ew_prod(work->scaling->E, work->delta_y, work->delta_y, work->data->m);\n    }\n    work->info->obj_val = OSQP_INFTY;\n    exitflag            = 1;\n  }\n  else if (dual_inf_check) {\n    // Update final information\n    if (approximate) {\n      update_status(work->info, OSQP_DUAL_INFEASIBLE_INACCURATE);\n    } else {\n      update_status(work->info, OSQP_DUAL_INFEASIBLE);\n    }\n\n    if (work->settings->scaling && !work->settings->scaled_termination) {\n      // Update infeasibility certificate\n      vec_ew_prod(work->scaling->D, work->delta_x, work->delta_x, work->data->n);\n    }\n    work->info->obj_val = -OSQP_INFTY;\n    exitflag            = 1;\n  }\n\n  return exitflag;\n}\n\n\n#ifndef EMBEDDED\n\nc_int validate_data(const OSQPData *data) {\n  c_int j, ptr;\n\n  if (!data) {\n# ifdef PRINTING\n    c_eprint(\"Missing data\");\n# endif\n    return 1;\n  }\n\n  if (!(data->P)) {\n# ifdef PRINTING\n    c_eprint(\"Missing matrix P\");\n# endif\n    return 1;\n  }\n\n  if (!(data->A)) {\n# ifdef PRINTING\n    c_eprint(\"Missing matrix A\");\n# endif\n    return 1;\n  }\n\n  // General dimensions Tests\n  if ((data->n <= 0) || (data->m < 0)) {\n# ifdef PRINTING\n    c_eprint(\"n must be positive and m nonnegative; n = %i, m = %i\",\n             (int)data->n, (int)data->m);\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  // Matrix P\n  if (data->P->m != data->n) {\n# ifdef PRINTING\n    c_eprint(\"P does not have dimension n x n with n = %i\", (int)data->n);\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if (data->P->m != data->P->n) {\n# ifdef PRINTING\n    c_eprint(\"P is not square\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  for (j = 0; j < data->n; j++) { // COLUMN\n    for (ptr = data->P->p[j]; ptr < data->P->p[j + 1]; ptr++) {\n      if (data->P->i[ptr] > j) {  // if ROW > COLUMN\n# ifdef PRINTING\n        c_eprint(\"P is not upper triangular\");\n# endif /* ifdef PRINTING */\n        return 1;\n      }\n    }\n  }\n\n  // Matrix A\n  if ((data->A->m != data->m) || (data->A->n != data->n)) {\n# ifdef PRINTING\n    c_eprint(\"A does not have dimension %i x %i\", (int)data->m, (int)data->n);\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  // Lower and upper bounds\n  for (j = 0; j < data->m; j++) {\n    if (data->l[j] > data->u[j]) {\n# ifdef PRINTING\n      c_eprint(\"Lower bound at index %d is greater than upper bound: %.4e > %.4e\",\n               (int)j, data->l[j], data->u[j]);\n# endif /* ifdef PRINTING */\n      return 1;\n    }\n  }\n\n  // TODO: Complete with other checks\n\n  return 0;\n}\n\nc_int validate_linsys_solver(c_int linsys_solver) {\n  if ((linsys_solver != QDLDL_SOLVER) &&\n      (linsys_solver != MKL_PARDISO_SOLVER)) {\n    return 1;\n  }\n\n  // TODO: Add more solvers in case\n\n  // Valid solver\n  return 0;\n}\n\nc_int validate_settings(const OSQPSettings *settings) {\n  if (!settings) {\n# ifdef PRINTING\n    c_eprint(\"Missing settings!\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if (settings->scaling < 0) {\n# ifdef PRINTING\n    c_eprint(\"scaling must be nonnegative\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if ((settings->adaptive_rho != 0) && (settings->adaptive_rho != 1)) {\n# ifdef PRINTING\n    c_eprint(\"adaptive_rho must be either 0 or 1\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if (settings->adaptive_rho_interval < 0) {\n# ifdef PRINTING\n    c_eprint(\"adaptive_rho_interval must be nonnegative\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n# ifdef PROFILING\n\n  if (settings->adaptive_rho_fraction <= 0) {\n#  ifdef PRINTING\n    c_eprint(\"adaptive_rho_fraction must be positive\");\n#  endif /* ifdef PRINTING */\n    return 1;\n  }\n# endif /* ifdef PROFILING */\n\n  if (settings->adaptive_rho_tolerance < 1.0) {\n# ifdef PRINTING\n    c_eprint(\"adaptive_rho_tolerance must be >= 1\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if (settings->polish_refine_iter < 0) {\n# ifdef PRINTING\n    c_eprint(\"polish_refine_iter must be nonnegative\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if (settings->rho <= 0.0) {\n# ifdef PRINTING\n    c_eprint(\"rho must be positive\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if (settings->sigma <= 0.0) {\n# ifdef PRINTING\n    c_eprint(\"sigma must be positive\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if (settings->delta <= 0.0) {\n# ifdef PRINTING\n    c_eprint(\"delta must be positive\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if (settings->max_iter <= 0) {\n# ifdef PRINTING\n    c_eprint(\"max_iter must be positive\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if (settings->eps_abs < 0.0) {\n# ifdef PRINTING\n    c_eprint(\"eps_abs must be nonnegative\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if (settings->eps_rel < 0.0) {\n# ifdef PRINTING\n    c_eprint(\"eps_rel must be nonnegative\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if ((settings->eps_rel == 0.0) &&\n      (settings->eps_abs == 0.0)) {\n# ifdef PRINTING\n    c_eprint(\"at least one of eps_abs and eps_rel must be positive\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if (settings->eps_prim_inf <= 0.0) {\n# ifdef PRINTING\n    c_eprint(\"eps_prim_inf must be positive\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if (settings->eps_dual_inf <= 0.0) {\n# ifdef PRINTING\n    c_eprint(\"eps_dual_inf must be positive\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if ((settings->alpha <= 0.0) ||\n      (settings->alpha >= 2.0)) {\n# ifdef PRINTING\n    c_eprint(\"alpha must be strictly between 0 and 2\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if (validate_linsys_solver(settings->linsys_solver)) {\n# ifdef PRINTING\n    c_eprint(\"linsys_solver not recognized\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if ((settings->verbose != 0) &&\n      (settings->verbose != 1)) {\n# ifdef PRINTING\n    c_eprint(\"verbose must be either 0 or 1\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if ((settings->scaled_termination != 0) &&\n      (settings->scaled_termination != 1)) {\n# ifdef PRINTING\n    c_eprint(\"scaled_termination must be either 0 or 1\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if (settings->check_termination < 0) {\n# ifdef PRINTING\n    c_eprint(\"check_termination must be nonnegative\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  if ((settings->warm_start != 0) &&\n      (settings->warm_start != 1)) {\n# ifdef PRINTING\n    c_eprint(\"warm_start must be either 0 or 1\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n# ifdef PROFILING\n\n  if (settings->time_limit < 0.0) {\n#  ifdef PRINTING\n    c_eprint(\"time_limit must be nonnegative\\n\");\n#  endif /* ifdef PRINTING */\n    return 1;\n  }\n# endif /* ifdef PROFILING */\n\n  return 0;\n}\n\n#endif // #ifndef EMBEDDED\n"
  },
  {
    "path": "osqp/src/cs.c",
    "content": "#include \"cs.h\"\n\n\nstatic void* csc_malloc(c_int n, c_int size) {\n  return c_malloc(n * size);\n}\n\nstatic void* csc_calloc(c_int n, c_int size) {\n  return c_calloc(n, size);\n}\n\ncsc* csc_matrix(c_int m, c_int n, c_int nzmax, c_float *x, c_int *i, c_int *p)\n{\n  csc *M = (csc *)c_malloc(sizeof(csc));\n\n  if (!M) return OSQP_NULL;\n\n  M->m     = m;\n  M->n     = n;\n  M->nz    = -1;\n  M->nzmax = nzmax;\n  M->x     = x;\n  M->i     = i;\n  M->p     = p;\n  return M;\n}\n\ncsc* csc_spalloc(c_int m, c_int n, c_int nzmax, c_int values, c_int triplet) {\n  csc *A = csc_calloc(1, sizeof(csc)); /* allocate the csc struct */\n\n  if (!A) return OSQP_NULL;            /* out of memory */\n\n  A->m     = m;                        /* define dimensions and nzmax */\n  A->n     = n;\n  A->nzmax = nzmax = c_max(nzmax, 1);\n  A->nz    = triplet ? 0 : -1;         /* allocate triplet or comp.col */\n  A->p     = csc_malloc(triplet ? nzmax : n + 1, sizeof(c_int));\n  A->i     = csc_malloc(nzmax,  sizeof(c_int));\n  A->x     = values ? csc_malloc(nzmax,  sizeof(c_float)) : OSQP_NULL;\n  if (!A->p || !A->i || (values && !A->x)){\n    csc_spfree(A);\n    return OSQP_NULL;\n  } else return A;\n}\n\nvoid csc_spfree(csc *A) {\n  if (A){\n    if (A->p) c_free(A->p);\n    if (A->i) c_free(A->i);\n    if (A->x) c_free(A->x);\n    c_free(A);\n  }\n}\n\ncsc* triplet_to_csc(const csc *T, c_int *TtoC) {\n  c_int m, n, nz, p, k, *Cp, *Ci, *w, *Ti, *Tj;\n  c_float *Cx, *Tx;\n  csc     *C;\n\n  m  = T->m;\n  n  = T->n;\n  Ti = T->i;\n  Tj = T->p;\n  Tx = T->x;\n  nz = T->nz;\n  C  = csc_spalloc(m, n, nz, Tx != OSQP_NULL, 0);     /* allocate result */\n  w  = csc_calloc(n, sizeof(c_int));                  /* get workspace */\n\n  if (!C || !w) return csc_done(C, w, OSQP_NULL, 0);  /* out of memory */\n\n  Cp = C->p;\n  Ci = C->i;\n  Cx = C->x;\n\n  for (k = 0; k < nz; k++) w[Tj[k]]++;  /* column counts */\n  csc_cumsum(Cp, w, n);                 /* column pointers */\n\n  for (k = 0; k < nz; k++) {\n    Ci[p = w[Tj[k]]++] = Ti[k];         /* A(i,j) is the pth entry in C */\n\n    if (Cx) {\n      Cx[p] = Tx[k];\n\n      if (TtoC != OSQP_NULL) TtoC[k] = p;  // Assign vector of indices\n    }\n  }\n  return csc_done(C, w, OSQP_NULL, 1);     /* success; free w and return C */\n}\n\ncsc* triplet_to_csr(const csc *T, c_int *TtoC) {\n  c_int m, n, nz, p, k, *Cp, *Cj, *w, *Ti, *Tj;\n  c_float *Cx, *Tx;\n  csc     *C;\n\n  m  = T->m;\n  n  = T->n;\n  Ti = T->i;\n  Tj = T->p;\n  Tx = T->x;\n  nz = T->nz;\n  C  = csc_spalloc(m, n, nz, Tx != OSQP_NULL, 0);     /* allocate result */\n  w  = csc_calloc(m, sizeof(c_int));                  /* get workspace */\n\n  if (!C || !w) return csc_done(C, w, OSQP_NULL, 0);  /* out of memory */\n\n  Cp = C->p;\n  Cj = C->i;\n  Cx = C->x;\n\n  for (k = 0; k < nz; k++) w[Ti[k]]++;  /* row counts */\n  csc_cumsum(Cp, w, m);                 /* row pointers */\n\n  for (k = 0; k < nz; k++) {\n    Cj[p = w[Ti[k]]++] = Tj[k];         /* A(i,j) is the pth entry in C */\n\n    if (Cx) {\n      Cx[p] = Tx[k];\n\n      if (TtoC != OSQP_NULL) TtoC[k] = p;  // Assign vector of indices\n    }\n  }\n  return csc_done(C, w, OSQP_NULL, 1);     /* success; free w and return C */\n}\n\nc_int csc_cumsum(c_int *p, c_int *c, c_int n) {\n  c_int i, nz = 0;\n\n  if (!p || !c) return -1;  /* check inputs */\n\n  for (i = 0; i < n; i++)\n  {\n    p[i] = nz;\n    nz  += c[i];\n    c[i] = p[i];\n  }\n  p[n] = nz;\n  return nz; /* return sum (c [0..n-1]) */\n}\n\nc_int* csc_pinv(c_int const *p, c_int n) {\n  c_int k, *pinv;\n\n  if (!p) return OSQP_NULL;                /* p = OSQP_NULL denotes identity */\n\n  pinv = csc_malloc(n, sizeof(c_int));     /* allocate result */\n\n  if (!pinv) return OSQP_NULL;             /* out of memory */\n\n  for (k = 0; k < n; k++) pinv[p[k]] = k;  /* invert the permutation */\n  return pinv;                             /* return result */\n}\n\ncsc* csc_symperm(const csc *A, const c_int *pinv, c_int *AtoC, c_int values) {\n  c_int i, j, p, q, i2, j2, n, *Ap, *Ai, *Cp, *Ci, *w;\n  c_float *Cx, *Ax;\n  csc     *C;\n\n  n  = A->n;\n  Ap = A->p;\n  Ai = A->i;\n  Ax = A->x;\n  C  = csc_spalloc(n, n, Ap[n], values && (Ax != OSQP_NULL),\n                   0);                                /* alloc result*/\n  w = csc_calloc(n, sizeof(c_int));                   /* get workspace */\n\n  if (!C || !w) return csc_done(C, w, OSQP_NULL, 0);  /* out of memory */\n\n  Cp = C->p;\n  Ci = C->i;\n  Cx = C->x;\n\n  for (j = 0; j < n; j++)    /* count entries in each column of C */\n  {\n    j2 = pinv ? pinv[j] : j; /* column j of A is column j2 of C */\n\n    for (p = Ap[j]; p < Ap[j + 1]; p++) {\n      i = Ai[p];\n\n      if (i > j) continue;     /* skip lower triangular part of A */\n      i2 = pinv ? pinv[i] : i; /* row i of A is row i2 of C */\n      w[c_max(i2, j2)]++;      /* column count of C */\n    }\n  }\n  csc_cumsum(Cp, w, n);        /* compute column pointers of C */\n\n  for (j = 0; j < n; j++) {\n    j2 = pinv ? pinv[j] : j;   /* column j of A is column j2 of C */\n\n    for (p = Ap[j]; p < Ap[j + 1]; p++) {\n      i = Ai[p];\n\n      if (i > j) continue;                             /* skip lower triangular\n                                                          part of A*/\n      i2                         = pinv ? pinv[i] : i; /* row i of A is row i2\n                                                          of C */\n      Ci[q = w[c_max(i2, j2)]++] = c_min(i2, j2);\n\n      if (Cx) Cx[q] = Ax[p];\n\n      if (AtoC) { // If vector AtoC passed, store values of the mappings\n        AtoC[p] = q;\n      }\n    }\n  }\n  return csc_done(C, w, OSQP_NULL, 1); /* success; free workspace, return C */\n}\n\ncsc* copy_csc_mat(const csc *A) {\n  csc *B = csc_spalloc(A->m, A->n, A->p[A->n], 1, 0);\n\n  if (!B) return OSQP_NULL;\n\n  prea_int_vec_copy(A->p, B->p, A->n + 1);\n  prea_int_vec_copy(A->i, B->i, A->p[A->n]);\n  prea_vec_copy(A->x, B->x, A->p[A->n]);\n\n  return B;\n}\n\nvoid prea_copy_csc_mat(const csc *A, csc *B) {\n  prea_int_vec_copy(A->p, B->p, A->n + 1);\n  prea_int_vec_copy(A->i, B->i, A->p[A->n]);\n  prea_vec_copy(A->x, B->x, A->p[A->n]);\n\n  B->nzmax = A->nzmax;\n}\n\ncsc* csc_done(csc *C, void *w, void *x, c_int ok) {\n  c_free(w);                   /* free workspace */\n  c_free(x);\n  if (ok) return C;\n  else {\n    csc_spfree(C);\n    return OSQP_NULL;\n  }\n}\n\ncsc* csc_to_triu(csc *M) {\n  csc  *M_trip;    // Matrix in triplet format\n  csc  *M_triu;    // Resulting upper triangular matrix\n  c_int nnzorigM;  // Number of nonzeros from original matrix M\n  c_int nnzmaxM;   // Estimated maximum number of elements of upper triangular M\n  c_int n;         // Dimension of M\n  c_int ptr, i, j; // Counters for (i,j) and index in M\n  c_int z_M = 0;   // Counter for elements in M_trip\n\n\n  // Check if matrix is square\n  if (M->m != M->n) {\n#ifdef PRINTING\n    c_eprint(\"Matrix M not square\");\n#endif /* ifdef PRINTING */\n    return OSQP_NULL;\n  }\n  n = M->n;\n\n  // Get number of nonzeros full M\n  nnzorigM = M->p[n];\n\n  // Estimate nnzmaxM\n  // Number of nonzero elements in original M + diagonal part.\n  // -> Full matrix M as input: estimate is half the number of total elements +\n  // diagonal = .5 * (nnzorigM + n)\n  // -> Upper triangular matrix M as input: estimate is the number of total\n  // elements + diagonal = nnzorigM + n\n  // The maximum between the two is nnzorigM + n\n  nnzmaxM = nnzorigM + n;\n\n  // OLD\n  // nnzmaxM = n*(n+1)/2;  // Full upper triangular matrix (This version\n  // allocates too much memory!)\n  // nnzmaxM = .5 * (nnzorigM + n);  // half of the total elements + diagonal\n\n  // Allocate M_trip\n  M_trip = csc_spalloc(n, n, nnzmaxM, 1, 1); // Triplet format\n\n  if (!M_trip) {\n#ifdef PRINTING\n    c_eprint(\"Upper triangular matrix extraction failed (out of memory)\");\n#endif /* ifdef PRINTING */\n    return OSQP_NULL;\n  }\n\n  // Fill M_trip with only elements in M which are in the upper triangular\n  for (j = 0; j < n; j++) { // Cycle over columns\n    for (ptr = M->p[j]; ptr < M->p[j + 1]; ptr++) {\n      // Get row index\n      i = M->i[ptr];\n\n      // Assign element only if in the upper triangular\n      if (i <= j) {\n        // c_print(\"\\nM(%i, %i) = %.4f\", M->i[ptr], j, M->x[ptr]);\n\n        M_trip->i[z_M] = i;\n        M_trip->p[z_M] = j;\n        M_trip->x[z_M] = M->x[ptr];\n\n        // Increase counter for the number of elements\n        z_M++;\n      }\n    }\n  }\n\n  // Set number of nonzeros\n  M_trip->nz = z_M;\n\n  // Convert triplet matrix to csc format\n  M_triu = triplet_to_csc(M_trip, OSQP_NULL);\n\n  // Assign number of nonzeros of full matrix to triu M\n  M_triu->nzmax = nnzmaxM;\n\n  // Cleanup and return result\n  csc_spfree(M_trip);\n\n  // Return matrix in triplet form\n  return M_triu;\n}\n"
  },
  {
    "path": "osqp/src/ctrlc.c",
    "content": "/*\n * Implements signal handling (ctrl-c) for OSQP.\n *\n * Under Windows, we use SetConsoleCtrlHandler.\n * Under Unix systems, we use sigaction.\n * For Mex files, we use utSetInterruptEnabled/utIsInterruptPending.\n *\n */\n\n#include \"ctrlc.h\"\n\n#if defined MATLAB\n\nstatic int istate;\n\nvoid osqp_start_interrupt_listener(void) {\n  istate = utSetInterruptEnabled(1);\n}\n\nvoid osqp_end_interrupt_listener(void) {\n  utSetInterruptEnabled(istate);\n}\n\nint osqp_is_interrupted(void) {\n  return utIsInterruptPending();\n}\n\n#elif defined IS_WINDOWS\n\nstatic int int_detected;\nstatic BOOL WINAPI handle_ctrlc(DWORD dwCtrlType) {\n  if (dwCtrlType != CTRL_C_EVENT) return FALSE;\n\n  int_detected = 1;\n  return TRUE;\n}\n\nvoid osqp_start_interrupt_listener(void) {\n  int_detected = 0;\n  SetConsoleCtrlHandler(handle_ctrlc, TRUE);\n}\n\nvoid osqp_end_interrupt_listener(void) {\n  SetConsoleCtrlHandler(handle_ctrlc, FALSE);\n}\n\nint osqp_is_interrupted(void) {\n  return int_detected;\n}\n\n#else /* Unix */\n\n# include <signal.h>\nstatic int int_detected;\nstruct sigaction oact;\nstatic void handle_ctrlc(int dummy) {\n  int_detected = dummy ? dummy : -1;\n}\n\nvoid osqp_start_interrupt_listener(void) {\n  struct sigaction act;\n\n  int_detected = 0;\n  act.sa_flags = 0;\n  sigemptyset(&act.sa_mask);\n  act.sa_handler = handle_ctrlc;\n  sigaction(SIGINT, &act, &oact);\n}\n\nvoid osqp_end_interrupt_listener(void) {\n  struct sigaction act;\n\n  sigaction(SIGINT, &oact, &act);\n}\n\nint osqp_is_interrupted(void) {\n  return int_detected;\n}\n\n#endif /* END IF IS_MATLAB / WINDOWS */\n"
  },
  {
    "path": "osqp/src/error.c",
    "content": "#include \"error.h\"\n\nconst char *OSQP_ERROR_MESSAGE[] = {\n  \"Problem data validation.\",\n  \"Solver settings validation.\",\n  \"Linear system solver not available.\\nTried to obtain it from shared library.\",\n  \"Linear system solver initialization.\",\n  \"KKT matrix factorization.\\nThe problem seems to be non-convex.\",\n  \"Memory allocation.\",\n  \"Solver workspace not initialized.\",\n};\n\n\nc_int _osqp_error(enum osqp_error_type error_code,\n\t\t const char * function_name) {\n# ifdef PRINTING\n  c_print(\"ERROR in %s: %s\\n\", function_name, OSQP_ERROR_MESSAGE[error_code-1]);\n# endif\n  return (c_int)error_code;\n}\n\n"
  },
  {
    "path": "osqp/src/kkt.c",
    "content": "#include \"kkt.h\"\n\n#ifndef EMBEDDED\n\n\ncsc* form_KKT(const csc  *P,\n              const  csc *A,\n              c_int       format,\n              c_float     param1,\n              c_float    *param2,\n              c_int      *PtoKKT,\n              c_int      *AtoKKT,\n              c_int     **Pdiag_idx,\n              c_int      *Pdiag_n,\n              c_int      *param2toKKT) {\n  c_int  nKKT, nnzKKTmax; // Size, number of nonzeros and max number of nonzeros\n                          // in KKT matrix\n  csc   *KKT_trip, *KKT;  // KKT matrix in triplet format and CSC format\n  c_int  ptr, i, j;       // Counters for elements (i,j) and index pointer\n  c_int  zKKT = 0;        // Counter for total number of elements in P and in\n                          // KKT\n  c_int *KKT_TtoC;        // Pointer to vector mapping from KKT in triplet form\n                          // to CSC\n\n  // Get matrix dimensions\n  nKKT = P->m + A->m;\n\n  // Get maximum number of nonzero elements (only upper triangular part)\n  nnzKKTmax = P->p[P->n] + // Number of elements in P\n              P->m +       // Number of elements in param1 * I\n              A->p[A->n] + // Number of nonzeros in A\n              A->m;        // Number of elements in - diag(param2)\n\n  // Preallocate KKT matrix in triplet format\n  KKT_trip = csc_spalloc(nKKT, nKKT, nnzKKTmax, 1, 1);\n\n  if (!KKT_trip) return OSQP_NULL;  // Failed to preallocate matrix\n\n  // Allocate vector of indices on the diagonal. Worst case it has m elements\n  if (Pdiag_idx != OSQP_NULL) {\n    (*Pdiag_idx) = c_malloc(P->m * sizeof(c_int));\n    *Pdiag_n     = 0; // Set 0 diagonal elements to start\n  }\n\n  // Allocate Triplet matrices\n  // P + param1 I\n  for (j = 0; j < P->n; j++) { // cycle over columns\n    // No elements in column j => add diagonal element param1\n    if (P->p[j] == P->p[j + 1]) {\n      KKT_trip->i[zKKT] = j;\n      KKT_trip->p[zKKT] = j;\n      KKT_trip->x[zKKT] = param1;\n      zKKT++;\n    }\n\n    for (ptr = P->p[j]; ptr < P->p[j + 1]; ptr++) { // cycle over rows\n      // Get current row\n      i = P->i[ptr];\n\n      // Add element of P\n      KKT_trip->i[zKKT] = i;\n      KKT_trip->p[zKKT] = j;\n      KKT_trip->x[zKKT] = P->x[ptr];\n\n      if (PtoKKT != OSQP_NULL) PtoKKT[ptr] = zKKT;  // Update index from P to\n                                                    // KKTtrip\n\n      if (i == j) {                                 // P has a diagonal element,\n                                                    // add param1\n        KKT_trip->x[zKKT] += param1;\n\n        // If index vector pointer supplied -> Store the index\n        if (Pdiag_idx != OSQP_NULL) {\n          (*Pdiag_idx)[*Pdiag_n] = ptr;\n          (*Pdiag_n)++;\n        }\n      }\n      zKKT++;\n\n      // Add diagonal param1 in case\n      if ((i < j) &&                  // Diagonal element not reached\n          (ptr + 1 == P->p[j + 1])) { // last element of column j\n        // Add diagonal element param1\n        KKT_trip->i[zKKT] = j;\n        KKT_trip->p[zKKT] = j;\n        KKT_trip->x[zKKT] = param1;\n        zKKT++;\n      }\n    }\n  }\n\n  if (Pdiag_idx != OSQP_NULL) {\n    // Realloc Pdiag_idx so that it contains exactly *Pdiag_n diagonal elements\n    (*Pdiag_idx) = c_realloc((*Pdiag_idx), (*Pdiag_n) * sizeof(c_int));\n  }\n\n\n  // A' at top right\n  for (j = 0; j < A->n; j++) {                      // Cycle over columns of A\n    for (ptr = A->p[j]; ptr < A->p[j + 1]; ptr++) {\n      KKT_trip->p[zKKT] = P->m + A->i[ptr];         // Assign column index from\n                                                    // row index of A\n      KKT_trip->i[zKKT] = j;                        // Assign row index from\n                                                    // column index of A\n      KKT_trip->x[zKKT] = A->x[ptr];                // Assign A value element\n\n      if (AtoKKT != OSQP_NULL) AtoKKT[ptr] = zKKT;  // Update index from A to\n                                                    // KKTtrip\n      zKKT++;\n    }\n  }\n\n  // - diag(param2) at bottom right\n  for (j = 0; j < A->m; j++) {\n    KKT_trip->i[zKKT] = j + P->n;\n    KKT_trip->p[zKKT] = j + P->n;\n    KKT_trip->x[zKKT] = -param2[j];\n\n    if (param2toKKT != OSQP_NULL) param2toKKT[j] = zKKT;  // Update index from\n                                                          // param2 to KKTtrip\n    zKKT++;\n  }\n\n  // Allocate number of nonzeros\n  KKT_trip->nz = zKKT;\n\n  // Convert triplet matrix to csc format\n  if (!PtoKKT && !AtoKKT && !param2toKKT) {\n    // If no index vectors passed, do not store KKT mapping from Trip to CSC/CSR\n    if (format == 0) KKT = triplet_to_csc(KKT_trip, OSQP_NULL);\n    else KKT = triplet_to_csr(KKT_trip, OSQP_NULL);\n  }\n  else {\n    // Allocate vector of indices from triplet to csc\n    KKT_TtoC = c_malloc((zKKT) * sizeof(c_int));\n\n    if (!KKT_TtoC) {\n      // Error in allocating KKT_TtoC vector\n      csc_spfree(KKT_trip);\n      c_free(*Pdiag_idx);\n      return OSQP_NULL;\n    }\n\n    // Store KKT mapping from Trip to CSC/CSR\n    if (format == 0)\n      KKT = triplet_to_csc(KKT_trip, KKT_TtoC);\n    else\n      KKT = triplet_to_csr(KKT_trip, KKT_TtoC);\n\n    // Update vectors of indices from P, A, param2 to KKT (now in CSC format)\n    if (PtoKKT != OSQP_NULL) {\n      for (i = 0; i < P->p[P->n]; i++) {\n        PtoKKT[i] = KKT_TtoC[PtoKKT[i]];\n      }\n    }\n\n    if (AtoKKT != OSQP_NULL) {\n      for (i = 0; i < A->p[A->n]; i++) {\n        AtoKKT[i] = KKT_TtoC[AtoKKT[i]];\n      }\n    }\n\n    if (param2toKKT != OSQP_NULL) {\n      for (i = 0; i < A->m; i++) {\n        param2toKKT[i] = KKT_TtoC[param2toKKT[i]];\n      }\n    }\n\n    // Free mapping\n    c_free(KKT_TtoC);\n  }\n\n  // Clean matrix in triplet format and return result\n  csc_spfree(KKT_trip);\n\n  return KKT;\n}\n\n#endif /* ifndef EMBEDDED */\n\n\n#if EMBEDDED != 1\n\nvoid update_KKT_P(csc          *KKT,\n                  const csc    *P,\n                  const c_int  *PtoKKT,\n                  const c_float param1,\n                  const c_int  *Pdiag_idx,\n                  const c_int   Pdiag_n) {\n  c_int i, j; // Iterations\n\n  // Update elements of KKT using P\n  for (i = 0; i < P->p[P->n]; i++) {\n    KKT->x[PtoKKT[i]] = P->x[i];\n  }\n\n  // Update diagonal elements of KKT by adding sigma\n  for (i = 0; i < Pdiag_n; i++) {\n    j                  = Pdiag_idx[i]; // Extract index of the element on the\n                                       // diagonal\n    KKT->x[PtoKKT[j]] += param1;\n  }\n}\n\nvoid update_KKT_A(csc *KKT, const csc *A, const c_int *AtoKKT) {\n  c_int i; // Iterations\n\n  // Update elements of KKT using A\n  for (i = 0; i < A->p[A->n]; i++) {\n    KKT->x[AtoKKT[i]] = A->x[i];\n  }\n}\n\nvoid update_KKT_param2(csc *KKT, const c_float *param2,\n                       const c_int *param2toKKT, const c_int m) {\n  c_int i; // Iterations\n\n  // Update elements of KKT using param2\n  for (i = 0; i < m; i++) {\n    KKT->x[param2toKKT[i]] = -param2[i];\n  }\n}\n\n#endif // EMBEDDED != 1\n"
  },
  {
    "path": "osqp/src/lin_alg.c",
    "content": "#include \"lin_alg.h\"\n\n\n/* VECTOR FUNCTIONS ----------------------------------------------------------*/\n\n\nvoid vec_add_scaled(c_float       *c,\n                    const c_float *a,\n                    const c_float *b,\n                    c_int          n,\n                    c_float        sc) {\n  c_int i;\n\n  for (i = 0; i < n; i++) {\n    c[i] =  a[i] + sc * b[i];\n  }\n}\n\nc_float vec_scaled_norm_inf(const c_float *S, const c_float *v, c_int l) {\n  c_int   i;\n  c_float abs_Sv_i;\n  c_float max = 0.0;\n\n  for (i = 0; i < l; i++) {\n    abs_Sv_i = c_absval(S[i] * v[i]);\n\n    if (abs_Sv_i > max) max = abs_Sv_i;\n  }\n  return max;\n}\n\nc_float vec_norm_inf(const c_float *v, c_int l) {\n  c_int   i;\n  c_float abs_v_i;\n  c_float max = 0.0;\n\n  for (i = 0; i < l; i++) {\n    abs_v_i = c_absval(v[i]);\n\n    if (abs_v_i > max) max = abs_v_i;\n  }\n  return max;\n}\n\nc_float vec_norm_inf_diff(const c_float *a, const c_float *b, c_int l) {\n  c_float nmDiff = 0.0, tmp;\n  c_int   i;\n\n  for (i = 0; i < l; i++) {\n    tmp = c_absval(a[i] - b[i]);\n\n    if (tmp > nmDiff) nmDiff = tmp;\n  }\n  return nmDiff;\n}\n\nc_float vec_mean(const c_float *a, c_int n) {\n  c_float mean = 0.0;\n  c_int   i;\n\n  for (i = 0; i < n; i++) {\n    mean += a[i];\n  }\n  mean /= (c_float)n;\n\n  return mean;\n}\n\nvoid int_vec_set_scalar(c_int *a, c_int sc, c_int n) {\n  c_int i;\n\n  for (i = 0; i < n; i++) {\n    a[i] = sc;\n  }\n}\n\nvoid vec_set_scalar(c_float *a, c_float sc, c_int n) {\n  c_int i;\n\n  for (i = 0; i < n; i++) {\n    a[i] = sc;\n  }\n}\n\nvoid vec_add_scalar(c_float *a, c_float sc, c_int n) {\n  c_int i;\n\n  for (i = 0; i < n; i++) {\n    a[i] += sc;\n  }\n}\n\nvoid vec_mult_scalar(c_float *a, c_float sc, c_int n) {\n  c_int i;\n\n  for (i = 0; i < n; i++) {\n    a[i] *= sc;\n  }\n}\n\n#ifndef EMBEDDED\nc_float* vec_copy(c_float *a, c_int n) {\n  c_float *b;\n  c_int    i;\n\n  b = c_malloc(n * sizeof(c_float));\n  if (!b) return OSQP_NULL;\n\n  for (i = 0; i < n; i++) {\n    b[i] = a[i];\n  }\n\n  return b;\n}\n\n#endif // end EMBEDDED\n\n\nvoid prea_int_vec_copy(const c_int *a, c_int *b, c_int n) {\n  c_int i;\n\n  for (i = 0; i < n; i++) {\n    b[i] = a[i];\n  }\n}\n\nvoid prea_vec_copy(const c_float *a, c_float *b, c_int n) {\n  c_int i;\n\n  for (i = 0; i < n; i++) {\n    b[i] = a[i];\n  }\n}\n\nvoid vec_ew_recipr(const c_float *a, c_float *b, c_int n) {\n  c_int i;\n\n  for (i = 0; i < n; i++) {\n    b[i] = (c_float)1.0 / a[i];\n  }\n}\n\nc_float vec_prod(const c_float *a, const c_float *b, c_int n) {\n  c_float prod = 0.0;\n  c_int   i; // Index\n\n  for (i = 0; i < n; i++) {\n    prod += a[i] * b[i];\n  }\n\n  return prod;\n}\n\nvoid vec_ew_prod(const c_float *a, const c_float *b, c_float *c, c_int n) {\n  c_int i;\n\n  for (i = 0; i < n; i++) {\n    c[i] = b[i] * a[i];\n  }\n}\n\n#if EMBEDDED != 1\nvoid vec_ew_sqrt(c_float *a, c_int n) {\n  c_int i;\n\n  for (i = 0; i < n; i++) {\n    a[i] = c_sqrt(a[i]);\n  }\n}\n\nvoid vec_ew_max(c_float *a, c_int n, c_float max_val) {\n  c_int i;\n\n  for (i = 0; i < n; i++) {\n    a[i] = c_max(a[i], max_val);\n  }\n}\n\nvoid vec_ew_min(c_float *a, c_int n, c_float min_val) {\n  c_int i;\n\n  for (i = 0; i < n; i++) {\n    a[i] = c_min(a[i], min_val);\n  }\n}\n\nvoid vec_ew_max_vec(const c_float *a, const c_float *b, c_float *c, c_int n) {\n  c_int i;\n\n  for (i = 0; i < n; i++) {\n    c[i] = c_max(a[i], b[i]);\n  }\n}\n\nvoid vec_ew_min_vec(const c_float *a, const c_float *b, c_float *c, c_int n) {\n  c_int i;\n\n  for (i = 0; i < n; i++) {\n    c[i] = c_min(a[i], b[i]);\n  }\n}\n\n#endif // EMBEDDED != 1\n\n\n/* MATRIX FUNCTIONS ----------------------------------------------------------*/\n\n/* multiply scalar to matrix */\nvoid mat_mult_scalar(csc *A, c_float sc) {\n  c_int i, nnzA;\n\n  nnzA = A->p[A->n];\n\n  for (i = 0; i < nnzA; i++) {\n    A->x[i] *= sc;\n  }\n}\n\nvoid mat_premult_diag(csc *A, const c_float *d) {\n  c_int j, i;\n\n  for (j = 0; j < A->n; j++) {                // Cycle over columns\n    for (i = A->p[j]; i < A->p[j + 1]; i++) { // Cycle every row in the column\n      A->x[i] *= d[A->i[i]];                  // Scale by corresponding element\n                                              // of d for row i\n    }\n  }\n}\n\nvoid mat_postmult_diag(csc *A, const c_float *d) {\n  c_int j, i;\n\n  for (j = 0; j < A->n; j++) {                // Cycle over columns j\n    for (i = A->p[j]; i < A->p[j + 1]; i++) { // Cycle every row i in column j\n      A->x[i] *= d[j];                        // Scale by corresponding element\n                                              // of d for column j\n    }\n  }\n}\n\nvoid mat_vec(const csc *A, const c_float *x, c_float *y, c_int plus_eq) {\n  c_int i, j;\n\n  if (!plus_eq) {\n    // y = 0\n    for (i = 0; i < A->m; i++) {\n      y[i] = 0;\n    }\n  }\n\n  // if A is empty\n  if (A->p[A->n] == 0) {\n    return;\n  }\n\n  if (plus_eq == -1) {\n    // y -=  A*x\n    for (j = 0; j < A->n; j++) {\n      for (i = A->p[j]; i < A->p[j + 1]; i++) {\n        y[A->i[i]] -= A->x[i] * x[j];\n      }\n    }\n  } else {\n    // y +=  A*x\n    for (j = 0; j < A->n; j++) {\n      for (i = A->p[j]; i < A->p[j + 1]; i++) {\n        y[A->i[i]] += A->x[i] * x[j];\n      }\n    }\n  }\n}\n\nvoid mat_tpose_vec(const csc *A, const c_float *x, c_float *y,\n                   c_int plus_eq, c_int skip_diag) {\n  c_int i, j, k;\n\n  if (!plus_eq) {\n    // y = 0\n    for (i = 0; i < A->n; i++) {\n      y[i] = 0;\n    }\n  }\n\n  // if A is empty\n  if (A->p[A->n] == 0) {\n    return;\n  }\n\n  if (plus_eq == -1) {\n    // y -=  A*x\n    if (skip_diag) {\n      for (j = 0; j < A->n; j++) {\n        for (k = A->p[j]; k < A->p[j + 1]; k++) {\n          i     = A->i[k];\n          y[j] -= i == j ? 0 : A->x[k] * x[i];\n        }\n      }\n    } else {\n      for (j = 0; j < A->n; j++) {\n        for (k = A->p[j]; k < A->p[j + 1]; k++) {\n          y[j] -= A->x[k] * x[A->i[k]];\n        }\n      }\n    }\n  } else {\n    // y +=  A*x\n    if (skip_diag) {\n      for (j = 0; j < A->n; j++) {\n        for (k = A->p[j]; k < A->p[j + 1]; k++) {\n          i     = A->i[k];\n          y[j] += i == j ? 0 : A->x[k] * x[i];\n        }\n      }\n    } else {\n      for (j = 0; j < A->n; j++) {\n        for (k = A->p[j]; k < A->p[j + 1]; k++) {\n          y[j] += A->x[k] * x[A->i[k]];\n        }\n      }\n    }\n  }\n}\n\n#if EMBEDDED != 1\nvoid mat_inf_norm_cols(const csc *M, c_float *E) {\n  c_int j, ptr;\n\n  // Initialize zero max elements\n  for (j = 0; j < M->n; j++) {\n    E[j] = 0.;\n  }\n\n  // Compute maximum across columns\n  for (j = 0; j < M->n; j++) {\n    for (ptr = M->p[j]; ptr < M->p[j + 1]; ptr++) {\n      E[j] = c_max(c_absval(M->x[ptr]), E[j]);\n    }\n  }\n}\n\nvoid mat_inf_norm_rows(const csc *M, c_float *E) {\n  c_int i, j, ptr;\n\n  // Initialize zero max elements\n  for (j = 0; j < M->m; j++) {\n    E[j] = 0.;\n  }\n\n  // Compute maximum across rows\n  for (j = 0; j < M->n; j++) {\n    for (ptr = M->p[j]; ptr < M->p[j + 1]; ptr++) {\n      i    = M->i[ptr];\n      E[i] = c_max(c_absval(M->x[ptr]), E[i]);\n    }\n  }\n}\n\nvoid mat_inf_norm_cols_sym_triu(const csc *M, c_float *E) {\n  c_int   i, j, ptr;\n  c_float abs_x;\n\n  // Initialize zero max elements\n  for (j = 0; j < M->n; j++) {\n    E[j] = 0.;\n  }\n\n  // Compute maximum across columns\n  // Note that element (i, j) contributes to\n  // -> Column j (as expected in any matrices)\n  // -> Column i (which is equal to row i for symmetric matrices)\n  for (j = 0; j < M->n; j++) {\n    for (ptr = M->p[j]; ptr < M->p[j + 1]; ptr++) {\n      i     = M->i[ptr];\n      abs_x = c_absval(M->x[ptr]);\n      E[j]  = c_max(abs_x, E[j]);\n\n      if (i != j) {\n        E[i] = c_max(abs_x, E[i]);\n      }\n    }\n  }\n}\n\n#endif /* if EMBEDDED != 1 */\n\n\nc_float quad_form(const csc *P, const c_float *x) {\n  c_float quad_form = 0.;\n  c_int   i, j, ptr;                                // Pointers to iterate over\n                                                    // matrix: (i,j) a element\n                                                    // pointer\n\n  for (j = 0; j < P->n; j++) {                      // Iterate over columns\n    for (ptr = P->p[j]; ptr < P->p[j + 1]; ptr++) { // Iterate over rows\n      i = P->i[ptr];                                // Row index\n\n      if (i == j) {                                 // Diagonal element\n        quad_form += (c_float).5 * P->x[ptr] * x[i] * x[i];\n      }\n      else if (i < j) {                             // Off-diagonal element\n        quad_form += P->x[ptr] * x[i] * x[j];\n      }\n      else {                                        // Element in lower diagonal\n                                                    // part\n#ifdef PRINTING\n        c_eprint(\"quad_form matrix is not upper triangular\");\n#endif /* ifdef PRINTING */\n        return OSQP_NULL;\n      }\n    }\n  }\n  return quad_form;\n}\n"
  },
  {
    "path": "osqp/src/lin_sys.c",
    "content": "#include \"lin_sys.h\"\n\n#include \"qdldl_interface.h\" // Include only this solver in the same directory\n\nconst char *LINSYS_SOLVER_NAME[] = {\n  \"qdldl\", \"mkl pardiso\"\n};\n\n#ifdef ENABLE_MKL_PARDISO\n# include \"pardiso_interface.h\"\n# include \"pardiso_loader.h\"\n#endif /* ifdef ENABLE_MKL_PARDISO */\n\n// Load linear system solver shared library\nc_int load_linsys_solver(enum linsys_solver_type linsys_solver) {\n  switch (linsys_solver) {\n  case QDLDL_SOLVER:\n\n    // We do not load  QDLDL solver. We have the source.\n    return 0;\n\n# ifdef ENABLE_MKL_PARDISO\n  case MKL_PARDISO_SOLVER:\n\n    // Load Pardiso library\n    return lh_load_pardiso(OSQP_NULL);\n\n# endif /* ifdef ENABLE_MKL_PARDISO */\n  default: // QDLDL\n    return 0;\n  }\n}\n\n// Unload linear system solver shared library\nc_int unload_linsys_solver(enum linsys_solver_type linsys_solver) {\n  switch (linsys_solver) {\n  case QDLDL_SOLVER:\n\n    // We do not load QDLDL solver. We have the source.\n    return 0;\n\n# ifdef ENABLE_MKL_PARDISO\n  case MKL_PARDISO_SOLVER:\n\n    // Unload Pardiso library\n    return lh_unload_pardiso();\n\n# endif /* ifdef ENABLE_MKL_PARDISO */\n  default: //  QDLDL\n    return 0;\n  }\n}\n\n// Initialize linear system solver structure\n// NB: Only the upper triangular part of P is stuffed!\nc_int init_linsys_solver(LinSysSolver          **s,\n                         const csc              *P,\n                         const csc              *A,\n                         c_float                 sigma,\n                         const c_float          *rho_vec,\n                         enum linsys_solver_type linsys_solver,\n                         c_int                   polish) {\n  switch (linsys_solver) {\n  case QDLDL_SOLVER:\n    return init_linsys_solver_qdldl((qdldl_solver **)s, P, A, sigma, rho_vec, polish);\n\n# ifdef ENABLE_MKL_PARDISO\n  case MKL_PARDISO_SOLVER:\n    return init_linsys_solver_pardiso((pardiso_solver **)s, P, A, sigma, rho_vec, polish);\n\n# endif /* ifdef ENABLE_MKL_PARDISO */\n  default: // QDLDL\n    return init_linsys_solver_qdldl((qdldl_solver **)s, P, A, sigma, rho_vec, polish);\n  }\n}\n"
  },
  {
    "path": "osqp/src/osqp.c",
    "content": "#include \"osqp.h\"\n#include \"auxil.h\"\n#include \"util.h\"\n#include \"scaling.h\"\n#include \"glob_opts.h\"\n#include \"error.h\"\n\n\n#ifndef EMBEDDED\n# include \"polish.h\"\n#endif /* ifndef EMBEDDED */\n\n#ifdef CTRLC\n# include \"ctrlc.h\"\n#endif /* ifdef CTRLC */\n\n#ifndef EMBEDDED\n# include \"lin_sys.h\"\n#endif /* ifndef EMBEDDED */\n\n/**********************\n* Main API Functions *\n**********************/\nvoid osqp_set_default_settings(OSQPSettings *settings) {\n\n  settings->rho           = (c_float)RHO;            /* ADMM step */\n  settings->sigma         = (c_float)SIGMA;          /* ADMM step */\n  settings->scaling = SCALING;                       /* heuristic problem scaling */\n#if EMBEDDED != 1\n  settings->adaptive_rho           = ADAPTIVE_RHO;\n  settings->adaptive_rho_interval  = ADAPTIVE_RHO_INTERVAL;\n  settings->adaptive_rho_tolerance = (c_float)ADAPTIVE_RHO_TOLERANCE;\n\n# ifdef PROFILING\n  settings->adaptive_rho_fraction = (c_float)ADAPTIVE_RHO_FRACTION;\n# endif /* ifdef PROFILING */\n#endif  /* if EMBEDDED != 1 */\n\n  settings->max_iter      = MAX_ITER;                /* maximum iterations to\n                                                        take */\n  settings->eps_abs       = (c_float)EPS_ABS;        /* absolute convergence\n                                                        tolerance */\n  settings->eps_rel       = (c_float)EPS_REL;        /* relative convergence\n                                                        tolerance */\n  settings->eps_prim_inf  = (c_float)EPS_PRIM_INF;   /* primal infeasibility\n                                                        tolerance */\n  settings->eps_dual_inf  = (c_float)EPS_DUAL_INF;   /* dual infeasibility\n                                                        tolerance */\n  settings->alpha         = (c_float)ALPHA;          /* relaxation parameter */\n  settings->linsys_solver = LINSYS_SOLVER;           /* relaxation parameter */\n\n#ifndef EMBEDDED\n  settings->delta              = DELTA;              /* regularization parameter\n                                                        for polish */\n  settings->polish             = POLISH;             /* ADMM solution polish: 1\n                                                      */\n  settings->polish_refine_iter = POLISH_REFINE_ITER; /* iterative refinement\n                                                        steps in polish */\n  settings->verbose            = VERBOSE;            /* print output */\n#endif /* ifndef EMBEDDED */\n\n  settings->scaled_termination = SCALED_TERMINATION; /* Evaluate scaled\n                                                        termination criteria*/\n  settings->check_termination  = CHECK_TERMINATION;  /* Interval for evaluating\n                                                        termination criteria */\n  settings->warm_start         = WARM_START;         /* warm starting */\n\n#ifdef PROFILING\n  settings->time_limit = TIME_LIMIT;\n#endif /* ifdef PROFILING */\n}\n\n#ifndef EMBEDDED\n\n\nc_int osqp_setup(OSQPWorkspace** workp, const OSQPData *data, const OSQPSettings *settings) {\n  c_int exitflag;\n\n  OSQPWorkspace * work;\n\n  // Validate data\n  if (validate_data(data)) return osqp_error(OSQP_DATA_VALIDATION_ERROR);\n\n  // Validate settings\n  if (validate_settings(settings)) return osqp_error(OSQP_SETTINGS_VALIDATION_ERROR);\n\n  // Allocate empty workspace\n  work = c_calloc(1, sizeof(OSQPWorkspace));\n  if (!(work)) return osqp_error(OSQP_MEM_ALLOC_ERROR);\n  *workp = work;\n\n  // Start and allocate directly timer\n# ifdef PROFILING\n  work->timer = c_malloc(sizeof(OSQPTimer));\n  if (!(work->timer)) return osqp_error(OSQP_MEM_ALLOC_ERROR);\n  osqp_tic(work->timer);\n# endif /* ifdef PROFILING */\n\n  // Copy problem data into workspace\n  work->data = c_malloc(sizeof(OSQPData));\n  if (!(work->data)) return osqp_error(OSQP_MEM_ALLOC_ERROR);\n  work->data->n = data->n;\n  work->data->m = data->m;\n\n  // Cost function\n  work->data->P = copy_csc_mat(data->P);\n  work->data->q = vec_copy(data->q, data->n);\n  if (!(work->data->P) || !(work->data->q)) return osqp_error(OSQP_MEM_ALLOC_ERROR);\n\n  // Constraints\n  work->data->A = copy_csc_mat(data->A);\n  if (!(work->data->A)) return osqp_error(OSQP_MEM_ALLOC_ERROR);\n  work->data->l = vec_copy(data->l, data->m);\n  work->data->u = vec_copy(data->u, data->m);\n  if ( data->m && (!(work->data->l) || !(work->data->u)) )\n    return osqp_error(OSQP_MEM_ALLOC_ERROR);\n\n  // Vectorized rho parameter\n  work->rho_vec     = c_malloc(data->m * sizeof(c_float));\n  work->rho_inv_vec = c_malloc(data->m * sizeof(c_float));\n  if ( data->m && (!(work->rho_vec) || !(work->rho_inv_vec)) )\n    return osqp_error(OSQP_MEM_ALLOC_ERROR);\n\n  // Type of constraints\n  work->constr_type = c_calloc(data->m, sizeof(c_int));\n  if (data->m && !(work->constr_type)) return osqp_error(OSQP_MEM_ALLOC_ERROR);\n\n  // Allocate internal solver variables (ADMM steps)\n  work->x        = c_calloc(data->n, sizeof(c_float));\n  work->z        = c_calloc(data->m, sizeof(c_float));\n  work->xz_tilde = c_calloc(data->n + data->m, sizeof(c_float));\n  work->x_prev   = c_calloc(data->n, sizeof(c_float));\n  work->z_prev   = c_calloc(data->m, sizeof(c_float));\n  work->y        = c_calloc(data->m, sizeof(c_float));\n  if (!(work->x) || !(work->xz_tilde) || !(work->x_prev))\n    return osqp_error(OSQP_MEM_ALLOC_ERROR);\n  if ( data->m && (!(work->z) || !(work->z_prev) || !(work->y)) )\n    return osqp_error(OSQP_MEM_ALLOC_ERROR);\n\n  // Initialize variables x, y, z to 0\n  cold_start(work);\n\n  // Primal and dual residuals variables\n  work->Ax  = c_calloc(data->m, sizeof(c_float));\n  work->Px  = c_calloc(data->n, sizeof(c_float));\n  work->Aty = c_calloc(data->n, sizeof(c_float));\n\n  // Primal infeasibility variables\n  work->delta_y   = c_calloc(data->m, sizeof(c_float));\n  work->Atdelta_y = c_calloc(data->n, sizeof(c_float));\n\n  // Dual infeasibility variables\n  work->delta_x  = c_calloc(data->n, sizeof(c_float));\n  work->Pdelta_x = c_calloc(data->n, sizeof(c_float));\n  work->Adelta_x = c_calloc(data->m, sizeof(c_float));\n\n  if (!(work->Px) || !(work->Aty) || !(work->Atdelta_y) ||\n      !(work->delta_x) || !(work->Pdelta_x))\n    return osqp_error(OSQP_MEM_ALLOC_ERROR);\n  if ( data->m && (!(work->Ax) || !(work->delta_y) || !(work->Adelta_x)) )\n    return osqp_error(OSQP_MEM_ALLOC_ERROR);\n\n  // Copy settings\n  work->settings = copy_settings(settings);\n  if (!(work->settings)) return osqp_error(OSQP_MEM_ALLOC_ERROR);\n\n  // Perform scaling\n  if (settings->scaling) {\n    // Allocate scaling structure\n    work->scaling = c_malloc(sizeof(OSQPScaling));\n    if (!(work->scaling)) return osqp_error(OSQP_MEM_ALLOC_ERROR);\n    work->scaling->D    = c_malloc(data->n * sizeof(c_float));\n    work->scaling->Dinv = c_malloc(data->n * sizeof(c_float));\n    work->scaling->E    = c_malloc(data->m * sizeof(c_float));\n    work->scaling->Einv = c_malloc(data->m * sizeof(c_float));\n    if (!(work->scaling->D) || !(work->scaling->Dinv))\n      return osqp_error(OSQP_MEM_ALLOC_ERROR);\n    if ( data->m && (!(work->scaling->E) || !(work->scaling->Einv)) )\n      return osqp_error(OSQP_MEM_ALLOC_ERROR);\n\n\n    // Allocate workspace variables used in scaling\n    work->D_temp   = c_malloc(data->n * sizeof(c_float));\n    work->D_temp_A = c_malloc(data->n * sizeof(c_float));\n    work->E_temp   = c_malloc(data->m * sizeof(c_float));\n    // if (!(work->D_temp) || !(work->D_temp_A) || !(work->E_temp))\n    //   return osqp_error(OSQP_MEM_ALLOC_ERROR);\n    if (!(work->D_temp) || !(work->D_temp_A)) return osqp_error(OSQP_MEM_ALLOC_ERROR);\n    if (data->m && !(work->E_temp))           return osqp_error(OSQP_MEM_ALLOC_ERROR);\n\n    // Scale data\n    scale_data(work);\n  } else {\n    work->scaling  = OSQP_NULL;\n    work->D_temp   = OSQP_NULL;\n    work->D_temp_A = OSQP_NULL;\n    work->E_temp   = OSQP_NULL;\n  }\n\n  // Set type of constraints\n  set_rho_vec(work);\n\n  // Load linear system solver\n  if (load_linsys_solver(work->settings->linsys_solver)) return osqp_error(OSQP_LINSYS_SOLVER_LOAD_ERROR);\n\n  // Initialize linear system solver structure\n  exitflag = init_linsys_solver(&(work->linsys_solver), work->data->P, work->data->A,\n                                work->settings->sigma, work->rho_vec,\n                                work->settings->linsys_solver, 0);\n\n  if (exitflag) {\n    return osqp_error(exitflag);\n  }\n\n  // Initialize active constraints structure\n  work->pol = c_malloc(sizeof(OSQPPolish));\n  if (!(work->pol)) return osqp_error(OSQP_MEM_ALLOC_ERROR);\n  work->pol->Alow_to_A = c_malloc(data->m * sizeof(c_int));\n  work->pol->Aupp_to_A = c_malloc(data->m * sizeof(c_int));\n  work->pol->A_to_Alow = c_malloc(data->m * sizeof(c_int));\n  work->pol->A_to_Aupp = c_malloc(data->m * sizeof(c_int));\n  work->pol->x         = c_malloc(data->n * sizeof(c_float));\n  work->pol->z         = c_malloc(data->m * sizeof(c_float));\n  work->pol->y         = c_malloc(data->m * sizeof(c_float));\n  if (!(work->pol->x)) return osqp_error(OSQP_MEM_ALLOC_ERROR);\n  if ( data->m && (!(work->pol->Alow_to_A) || !(work->pol->Aupp_to_A) ||\n      !(work->pol->A_to_Alow) || !(work->pol->A_to_Aupp) ||\n      !(work->pol->z) || !(work->pol->y)) )\n    return osqp_error(OSQP_MEM_ALLOC_ERROR);\n\n  // Allocate solution\n  work->solution = c_calloc(1, sizeof(OSQPSolution));\n  if (!(work->solution)) return osqp_error(OSQP_MEM_ALLOC_ERROR);\n  work->solution->x = c_calloc(1, data->n * sizeof(c_float));\n  work->solution->y = c_calloc(1, data->m * sizeof(c_float));\n  if (!(work->solution->x))            return osqp_error(OSQP_MEM_ALLOC_ERROR);\n  if (data->m && !(work->solution->y)) return osqp_error(OSQP_MEM_ALLOC_ERROR);\n\n  // Allocate and initialize information\n  work->info = c_calloc(1, sizeof(OSQPInfo));\n  if (!(work->info)) return osqp_error(OSQP_MEM_ALLOC_ERROR);\n  work->info->status_polish = 0;              // Polishing not performed\n  update_status(work->info, OSQP_UNSOLVED);\n# ifdef PROFILING\n  work->info->solve_time  = 0.0;                   // Solve time to zero\n  work->info->update_time = 0.0;                   // Update time to zero\n  work->info->polish_time = 0.0;                   // Polish time to zero\n  work->info->run_time    = 0.0;                   // Total run time to zero\n  work->info->setup_time  = osqp_toc(work->timer); // Update timer information\n\n  work->first_run         = 1;\n  work->clear_update_time = 0;\n  work->rho_update_from_solve = 0;\n# endif /* ifdef PROFILING */\n  work->info->rho_updates  = 0;                    // Rho updates set to 0\n  work->info->rho_estimate = work->settings->rho;  // Best rho estimate\n\n  // Print header\n# ifdef PRINTING\n  if (work->settings->verbose) print_setup_header(work);\n  work->summary_printed = 0; // Initialize last summary  to not printed\n# endif /* ifdef PRINTING */\n\n\n  // If adaptive rho and automatic interval, but profiling disabled, we need to\n  // set the interval to a default value\n# ifndef PROFILING\n  if (work->settings->adaptive_rho && !work->settings->adaptive_rho_interval) {\n    if (work->settings->check_termination) {\n      // If check_termination is enabled, we set it to a multiple of the check\n      // termination interval\n      work->settings->adaptive_rho_interval = ADAPTIVE_RHO_MULTIPLE_TERMINATION *\n                                              work->settings->check_termination;\n    } else {\n      // If check_termination is disabled we set it to a predefined fix number\n      work->settings->adaptive_rho_interval = ADAPTIVE_RHO_FIXED;\n    }\n  }\n# endif /* ifndef PROFILING */\n\n  // Return exit flag\n  return 0;\n}\n\n#endif // #ifndef EMBEDDED\n\n\nc_int osqp_solve(OSQPWorkspace *work) {\n\n  c_int exitflag;\n  c_int iter;\n  c_int compute_cost_function; // Boolean: compute the cost function in the loop or not\n  c_int can_check_termination; // Boolean: check termination or not\n\n#ifdef PROFILING\n  c_float temp_run_time;       // Temporary variable to store current run time\n#endif /* ifdef PROFILING */\n\n#ifdef PRINTING\n  c_int can_print;             // Boolean whether you can print\n#endif /* ifdef PRINTING */\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n#ifdef PROFILING\n  if (work->clear_update_time == 1)\n    work->info->update_time = 0.0;\n  work->rho_update_from_solve = 1;\n#endif /* ifdef PROFILING */\n\n  // Initialize variables\n  exitflag              = 0;\n  can_check_termination = 0;\n#ifdef PRINTING\n  can_print = work->settings->verbose;\n#endif /* ifdef PRINTING */\n#ifdef PRINTING\n  compute_cost_function = work->settings->verbose; // Compute cost function only\n                                                   // if verbose is on\n#else /* ifdef PRINTING */\n  compute_cost_function = 0;                       // Never compute cost\n                                                   // function during the\n                                                   // iterations if no printing\n                                                   // enabled\n#endif /* ifdef PRINTING */\n\n\n\n#ifdef PROFILING\n  osqp_tic(work->timer); // Start timer\n#endif /* ifdef PROFILING */\n\n\n#ifdef PRINTING\n\n  if (work->settings->verbose) {\n    // Print Header for every column\n    print_header();\n  }\n#endif /* ifdef PRINTING */\n\n#ifdef CTRLC\n\n  // initialize Ctrl-C support\n  osqp_start_interrupt_listener();\n#endif /* ifdef CTRLC */\n\n  // Initialize variables (cold start or warm start depending on settings)\n  if (!work->settings->warm_start) cold_start(work);  // If not warm start ->\n                                                      // set x, z, y to zero\n\n  // Main ADMM algorithm\n  for (iter = 1; iter <= work->settings->max_iter; iter++) {\n    // Update x_prev, z_prev (preallocated, no malloc)\n    swap_vectors(&(work->x), &(work->x_prev));\n    swap_vectors(&(work->z), &(work->z_prev));\n\n    /* ADMM STEPS */\n    /* Compute \\tilde{x}^{k+1}, \\tilde{z}^{k+1} */\n    update_xz_tilde(work);\n\n    /* Compute x^{k+1} */\n    update_x(work);\n\n    /* Compute z^{k+1} */\n    update_z(work);\n\n    /* Compute y^{k+1} */\n    update_y(work);\n\n    /* End of ADMM Steps */\n\n#ifdef CTRLC\n\n    // Check the interrupt signal\n    if (osqp_is_interrupted()) {\n      update_status(work->info, OSQP_SIGINT);\n# ifdef PRINTING\n      c_print(\"Solver interrupted\\n\");\n# endif /* ifdef PRINTING */\n      exitflag = 1;\n      goto exit;\n    }\n#endif /* ifdef CTRLC */\n\n#ifdef PROFILING\n\n    // Check if solver time_limit is enabled. In case, check if the current\n    // run time is more than the time_limit option.\n    if (work->first_run) {\n      temp_run_time = work->info->setup_time + osqp_toc(work->timer);\n    }\n    else {\n      temp_run_time = work->info->update_time + osqp_toc(work->timer);\n    }\n\n    if (work->settings->time_limit &&\n        (temp_run_time >= work->settings->time_limit)) {\n      update_status(work->info, OSQP_TIME_LIMIT_REACHED);\n# ifdef PRINTING\n      if (work->settings->verbose) c_print(\"run time limit reached\\n\");\n      can_print = 0;  // Not printing at this iteration\n# endif /* ifdef PRINTING */\n      break;\n    }\n#endif /* ifdef PROFILING */\n\n\n    // Can we check for termination ?\n    can_check_termination = work->settings->check_termination &&\n                            (iter % work->settings->check_termination == 0);\n\n#ifdef PRINTING\n\n    // Can we print ?\n    can_print = work->settings->verbose &&\n                ((iter % PRINT_INTERVAL == 0) || (iter == 1));\n\n    if (can_check_termination || can_print) { // Update status in either of\n                                              // these cases\n      // Update information\n      update_info(work, iter, compute_cost_function, 0);\n\n      if (can_print) {\n        // Print summary\n        print_summary(work);\n      }\n\n      if (can_check_termination) {\n        // Check algorithm termination\n        if (check_termination(work, 0)) {\n          // Terminate algorithm\n          break;\n        }\n      }\n    }\n#else /* ifdef PRINTING */\n\n    if (can_check_termination) {\n      // Update information and compute also objective value\n      update_info(work, iter, compute_cost_function, 0);\n\n      // Check algorithm termination\n      if (check_termination(work, 0)) {\n        // Terminate algorithm\n        break;\n      }\n    }\n#endif /* ifdef PRINTING */\n\n\n#if EMBEDDED != 1\n# ifdef PROFILING\n\n    // If adaptive rho with automatic interval, check if the solve time is a\n    // certain fraction\n    // of the setup time.\n    if (work->settings->adaptive_rho && !work->settings->adaptive_rho_interval) {\n      // Check time\n      if (osqp_toc(work->timer) >\n          work->settings->adaptive_rho_fraction * work->info->setup_time) {\n        // Enough time has passed. We now get the number of iterations between\n        // the updates.\n        if (work->settings->check_termination) {\n          // If check_termination is enabled, we round the number of iterations\n          // between\n          // rho updates to the closest multiple of check_termination\n          work->settings->adaptive_rho_interval = (c_int)c_roundmultiple(iter,\n                                                                         work->settings->check_termination);\n        } else {\n          // If check_termination is disabled, we round the number of iterations\n          // between\n          // updates to the closest multiple of the default check_termination\n          // interval.\n          work->settings->adaptive_rho_interval = (c_int)c_roundmultiple(iter,\n                                                                         CHECK_TERMINATION);\n        }\n\n        // Make sure the interval is not 0 and at least check_termination times\n        work->settings->adaptive_rho_interval = c_max(\n          work->settings->adaptive_rho_interval,\n          work->settings->check_termination);\n      } // If time condition is met\n    }   // If adaptive rho enabled and interval set to auto\n# endif // PROFILING\n\n    // Adapt rho\n    if (work->settings->adaptive_rho &&\n        work->settings->adaptive_rho_interval &&\n        (iter % work->settings->adaptive_rho_interval == 0)) {\n      // Update info with the residuals if it hasn't been done before\n# ifdef PRINTING\n\n      if (!can_check_termination && !can_print) {\n        // Information has not been computed neither for termination or printing\n        // reasons\n        update_info(work, iter, compute_cost_function, 0);\n      }\n# else /* ifdef PRINTING */\n\n      if (!can_check_termination) {\n        // Information has not been computed before for termination check\n        update_info(work, iter, compute_cost_function, 0);\n      }\n# endif /* ifdef PRINTING */\n\n      // Actually update rho\n      if (adapt_rho(work)) {\n# ifdef PRINTING\n        c_eprint(\"Failed rho update\");\n# endif // PRINTING\n        exitflag = 1;\n        goto exit;\n      }\n    }\n#endif // EMBEDDED != 1\n\n  }        // End of ADMM for loop\n\n\n  // Update information and check termination condition if it hasn't been done\n  // during last iteration (max_iter reached or check_termination disabled)\n  if (!can_check_termination) {\n    /* Update information */\n#ifdef PRINTING\n\n    if (!can_print) {\n      // Update info only if it hasn't been updated before for printing\n      // reasons\n      update_info(work, iter - 1, compute_cost_function, 0);\n    }\n#else /* ifdef PRINTING */\n\n    // If no printing is enabled, update info directly\n    update_info(work, iter - 1, compute_cost_function, 0);\n#endif /* ifdef PRINTING */\n\n#ifdef PRINTING\n\n    /* Print summary */\n    if (work->settings->verbose && !work->summary_printed) print_summary(work);\n#endif /* ifdef PRINTING */\n\n    /* Check whether a termination criterion is triggered */\n    check_termination(work, 0);\n  }\n\n  // Compute objective value in case it was not\n  // computed during the iterations\n  if (!compute_cost_function && has_solution(work->info)){\n    work->info->obj_val = compute_obj_val(work, work->x);\n  }\n\n\n#ifdef PRINTING\n  /* Print summary for last iteration */\n  if (work->settings->verbose && !work->summary_printed) {\n    print_summary(work);\n  }\n#endif /* ifdef PRINTING */\n\n  /* if max iterations reached, change status accordingly */\n  if (work->info->status_val == OSQP_UNSOLVED) {\n    if (!check_termination(work, 1)) { // Try to check for approximate\n      update_status(work->info, OSQP_MAX_ITER_REACHED);\n    }\n  }\n\n#ifdef PROFILING\n  /* if time-limit reached check termination and update status accordingly */\n if (work->info->status_val == OSQP_TIME_LIMIT_REACHED) {\n    if (!check_termination(work, 1)) { // Try for approximate solutions\n      update_status(work->info, OSQP_TIME_LIMIT_REACHED); /* Change update status back to OSQP_TIME_LIMIT_REACHED */\n    }\n  }\n#endif /* ifdef PROFILING */\n\n\n#if EMBEDDED != 1\n  /* Update rho estimate */\n  work->info->rho_estimate = compute_rho_estimate(work);\n#endif /* if EMBEDDED != 1 */\n\n  /* Update solve time */\n#ifdef PROFILING\n  work->info->solve_time = osqp_toc(work->timer);\n#endif /* ifdef PROFILING */\n\n\n#ifndef EMBEDDED\n  // Polish the obtained solution\n  if (work->settings->polish && (work->info->status_val == OSQP_SOLVED))\n    polish(work);\n#endif /* ifndef EMBEDDED */\n\n#ifdef PROFILING\n  /* Update total time */\n  if (work->first_run) {\n    // total time: setup + solve + polish\n    work->info->run_time = work->info->setup_time +\n                           work->info->solve_time +\n                           work->info->polish_time;\n  } else {\n    // total time: update + solve + polish\n    work->info->run_time = work->info->update_time +\n                           work->info->solve_time +\n                           work->info->polish_time;\n  }\n\n  // Indicate that the solve function has already been executed\n  if (work->first_run) work->first_run = 0;\n\n  // Indicate that the update_time should be set to zero\n  work->clear_update_time = 1;\n\n  // Indicate that osqp_update_rho is not called from osqp_solve\n  work->rho_update_from_solve = 0;\n#endif /* ifdef PROFILING */\n\n#ifdef PRINTING\n  /* Print final footer */\n  if (work->settings->verbose) print_footer(work->info, work->settings->polish);\n#endif /* ifdef PRINTING */\n\n  // Store solution\n  store_solution(work);\n\n\n// Define exit flag for quitting function\n#if defined(PROFILING) || defined(CTRLC) || EMBEDDED != 1\nexit:\n#endif /* if defined(PROFILING) || defined(CTRLC) || EMBEDDED != 1 */\n\n#ifdef CTRLC\n  // Restore previous signal handler\n  osqp_end_interrupt_listener();\n#endif /* ifdef CTRLC */\n\n  return exitflag;\n}\n\n\n#ifndef EMBEDDED\n\nc_int osqp_cleanup(OSQPWorkspace *work) {\n  c_int exitflag = 0;\n\n  if (work) { // If workspace has been allocated\n    // Free Data\n    if (work->data) {\n      if (work->data->P) csc_spfree(work->data->P);\n      if (work->data->A) csc_spfree(work->data->A);\n      if (work->data->q) c_free(work->data->q);\n      if (work->data->l) c_free(work->data->l);\n      if (work->data->u) c_free(work->data->u);\n      c_free(work->data);\n    }\n\n    // Free scaling variables\n    if (work->scaling){\n      if (work->scaling->D)    c_free(work->scaling->D);\n      if (work->scaling->Dinv) c_free(work->scaling->Dinv);\n      if (work->scaling->E)    c_free(work->scaling->E);\n      if (work->scaling->Einv) c_free(work->scaling->Einv);\n      c_free(work->scaling);\n    }\n\n    // Free temp workspace variables for scaling\n    if (work->D_temp)   c_free(work->D_temp);\n    if (work->D_temp_A) c_free(work->D_temp_A);\n    if (work->E_temp)   c_free(work->E_temp);\n\n    // Free linear system solver structure\n    if (work->linsys_solver) {\n      if (work->linsys_solver->free) {\n        work->linsys_solver->free(work->linsys_solver);\n      }\n    }\n\n    // Unload linear system solver after free\n    if (work->settings) {\n      exitflag = unload_linsys_solver(work->settings->linsys_solver);\n    }\n\n#ifndef EMBEDDED\n    // Free active constraints structure\n    if (work->pol) {\n      if (work->pol->Alow_to_A) c_free(work->pol->Alow_to_A);\n      if (work->pol->Aupp_to_A) c_free(work->pol->Aupp_to_A);\n      if (work->pol->A_to_Alow) c_free(work->pol->A_to_Alow);\n      if (work->pol->A_to_Aupp) c_free(work->pol->A_to_Aupp);\n      if (work->pol->x)         c_free(work->pol->x);\n      if (work->pol->z)         c_free(work->pol->z);\n      if (work->pol->y)         c_free(work->pol->y);\n      c_free(work->pol);\n    }\n#endif /* ifndef EMBEDDED */\n\n    // Free other Variables\n    if (work->rho_vec)     c_free(work->rho_vec);\n    if (work->rho_inv_vec) c_free(work->rho_inv_vec);\n#if EMBEDDED != 1\n    if (work->constr_type) c_free(work->constr_type);\n#endif\n    if (work->x)           c_free(work->x);\n    if (work->z)           c_free(work->z);\n    if (work->xz_tilde)    c_free(work->xz_tilde);\n    if (work->x_prev)      c_free(work->x_prev);\n    if (work->z_prev)      c_free(work->z_prev);\n    if (work->y)           c_free(work->y);\n    if (work->Ax)          c_free(work->Ax);\n    if (work->Px)          c_free(work->Px);\n    if (work->Aty)         c_free(work->Aty);\n    if (work->delta_y)     c_free(work->delta_y);\n    if (work->Atdelta_y)   c_free(work->Atdelta_y);\n    if (work->delta_x)     c_free(work->delta_x);\n    if (work->Pdelta_x)    c_free(work->Pdelta_x);\n    if (work->Adelta_x)    c_free(work->Adelta_x);\n\n    // Free Settings\n    if (work->settings) c_free(work->settings);\n\n    // Free solution\n    if (work->solution) {\n      if (work->solution->x) c_free(work->solution->x);\n      if (work->solution->y) c_free(work->solution->y);\n      c_free(work->solution);\n    }\n\n    // Free information\n    if (work->info) c_free(work->info);\n\n# ifdef PROFILING\n    // Free timer\n    if (work->timer) c_free(work->timer);\n# endif /* ifdef PROFILING */\n\n    // Free work\n    c_free(work);\n  }\n\n  return exitflag;\n}\n\n#endif // #ifndef EMBEDDED\n\n\n/************************\n* Update problem data  *\n************************/\nc_int osqp_update_lin_cost(OSQPWorkspace *work, const c_float *q_new) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n#ifdef PROFILING\n  if (work->clear_update_time == 1) {\n    work->clear_update_time = 0;\n    work->info->update_time = 0.0;\n  }\n  osqp_tic(work->timer); // Start timer\n#endif /* ifdef PROFILING */\n\n  // Replace q by the new vector\n  prea_vec_copy(q_new, work->data->q, work->data->n);\n\n  // Scaling\n  if (work->settings->scaling) {\n    vec_ew_prod(work->scaling->D, work->data->q, work->data->q, work->data->n);\n    vec_mult_scalar(work->data->q, work->scaling->c, work->data->n);\n  }\n\n  // Reset solver information\n  reset_info(work->info);\n\n#ifdef PROFILING\n  work->info->update_time += osqp_toc(work->timer);\n#endif /* ifdef PROFILING */\n\n  return 0;\n}\n\nc_int osqp_update_bounds(OSQPWorkspace *work,\n                         const c_float *l_new,\n                         const c_float *u_new) {\n  c_int i, exitflag = 0;\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n#ifdef PROFILING\n  if (work->clear_update_time == 1) {\n    work->clear_update_time = 0;\n    work->info->update_time = 0.0;\n  }\n  osqp_tic(work->timer); // Start timer\n#endif /* ifdef PROFILING */\n\n  // Check if lower bound is smaller than upper bound\n  for (i = 0; i < work->data->m; i++) {\n    if (l_new[i] > u_new[i]) {\n#ifdef PRINTING\n      c_eprint(\"lower bound must be lower than or equal to upper bound\");\n#endif /* ifdef PRINTING */\n      return 1;\n    }\n  }\n\n  // Replace l and u by the new vectors\n  prea_vec_copy(l_new, work->data->l, work->data->m);\n  prea_vec_copy(u_new, work->data->u, work->data->m);\n\n  // Scaling\n  if (work->settings->scaling) {\n    vec_ew_prod(work->scaling->E, work->data->l, work->data->l, work->data->m);\n    vec_ew_prod(work->scaling->E, work->data->u, work->data->u, work->data->m);\n  }\n\n  // Reset solver information\n  reset_info(work->info);\n\n#if EMBEDDED != 1\n  // Update rho_vec and refactor if constraints type changes\n  exitflag = update_rho_vec(work);\n#endif // EMBEDDED != 1\n\n#ifdef PROFILING\n  work->info->update_time += osqp_toc(work->timer);\n#endif /* ifdef PROFILING */\n\n  return exitflag;\n}\n\nc_int osqp_update_lower_bound(OSQPWorkspace *work, const c_float *l_new) {\n  c_int i, exitflag = 0;\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n#ifdef PROFILING\n  if (work->clear_update_time == 1) {\n    work->clear_update_time = 0;\n    work->info->update_time = 0.0;\n  }\n  osqp_tic(work->timer); // Start timer\n#endif /* ifdef PROFILING */\n\n  // Replace l by the new vector\n  prea_vec_copy(l_new, work->data->l, work->data->m);\n\n  // Scaling\n  if (work->settings->scaling) {\n    vec_ew_prod(work->scaling->E, work->data->l, work->data->l, work->data->m);\n  }\n\n  // Check if lower bound is smaller than upper bound\n  for (i = 0; i < work->data->m; i++) {\n    if (work->data->l[i] > work->data->u[i]) {\n#ifdef PRINTING\n      c_eprint(\"upper bound must be greater than or equal to lower bound\");\n#endif /* ifdef PRINTING */\n      return 1;\n    }\n  }\n\n  // Reset solver information\n  reset_info(work->info);\n\n#if EMBEDDED != 1\n  // Update rho_vec and refactor if constraints type changes\n  exitflag = update_rho_vec(work);\n#endif // EMBEDDED ! =1\n\n#ifdef PROFILING\n  work->info->update_time += osqp_toc(work->timer);\n#endif /* ifdef PROFILING */\n\n  return exitflag;\n}\n\nc_int osqp_update_upper_bound(OSQPWorkspace *work, const c_float *u_new) {\n  c_int i, exitflag = 0;\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n#ifdef PROFILING\n  if (work->clear_update_time == 1) {\n    work->clear_update_time = 0;\n    work->info->update_time = 0.0;\n  }\n  osqp_tic(work->timer); // Start timer\n#endif /* ifdef PROFILING */\n\n  // Replace u by the new vector\n  prea_vec_copy(u_new, work->data->u, work->data->m);\n\n  // Scaling\n  if (work->settings->scaling) {\n    vec_ew_prod(work->scaling->E, work->data->u, work->data->u, work->data->m);\n  }\n\n  // Check if upper bound is greater than lower bound\n  for (i = 0; i < work->data->m; i++) {\n    if (work->data->u[i] < work->data->l[i]) {\n#ifdef PRINTING\n      c_eprint(\"lower bound must be lower than or equal to upper bound\");\n#endif /* ifdef PRINTING */\n      return 1;\n    }\n  }\n\n  // Reset solver information\n  reset_info(work->info);\n\n#if EMBEDDED != 1\n  // Update rho_vec and refactor if constraints type changes\n  exitflag = update_rho_vec(work);\n#endif // EMBEDDED != 1\n\n#ifdef PROFILING\n  work->info->update_time += osqp_toc(work->timer);\n#endif /* ifdef PROFILING */\n\n  return exitflag;\n}\n\nc_int osqp_warm_start(OSQPWorkspace *work, const c_float *x, const c_float *y) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Update warm_start setting to true\n  if (!work->settings->warm_start) work->settings->warm_start = 1;\n\n  // Copy primal and dual variables into the iterates\n  prea_vec_copy(x, work->x, work->data->n);\n  prea_vec_copy(y, work->y, work->data->m);\n\n  // Scale iterates\n  if (work->settings->scaling) {\n    vec_ew_prod(work->scaling->Dinv, work->x, work->x, work->data->n);\n    vec_ew_prod(work->scaling->Einv, work->y, work->y, work->data->m);\n    vec_mult_scalar(work->y, work->scaling->c, work->data->m);\n  }\n\n  // Compute Ax = z and store it in z\n  mat_vec(work->data->A, work->x, work->z, 0);\n\n  return 0;\n}\n\nc_int osqp_warm_start_x(OSQPWorkspace *work, const c_float *x) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Update warm_start setting to true\n  if (!work->settings->warm_start) work->settings->warm_start = 1;\n\n  // Copy primal variable into the iterate x\n  prea_vec_copy(x, work->x, work->data->n);\n\n  // Scale iterate\n  if (work->settings->scaling) {\n    vec_ew_prod(work->scaling->Dinv, work->x, work->x, work->data->n);\n  }\n\n  // Compute Ax = z and store it in z\n  mat_vec(work->data->A, work->x, work->z, 0);\n\n  return 0;\n}\n\nc_int osqp_warm_start_y(OSQPWorkspace *work, const c_float *y) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Update warm_start setting to true\n  if (!work->settings->warm_start) work->settings->warm_start = 1;\n\n  // Copy primal variable into the iterate y\n  prea_vec_copy(y, work->y, work->data->m);\n\n  // Scale iterate\n  if (work->settings->scaling) {\n    vec_ew_prod(work->scaling->Einv, work->y, work->y, work->data->m);\n    vec_mult_scalar(work->y, work->scaling->c, work->data->m);\n  }\n\n  return 0;\n}\n\n\n#if EMBEDDED != 1\n\nc_int osqp_update_P(OSQPWorkspace *work,\n                    const c_float *Px_new,\n                    const c_int   *Px_new_idx,\n                    c_int          P_new_n) {\n  c_int i;        // For indexing\n  c_int exitflag; // Exit flag\n  c_int nnzP;     // Number of nonzeros in P\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n#ifdef PROFILING\n  if (work->clear_update_time == 1) {\n    work->clear_update_time = 0;\n    work->info->update_time = 0.0;\n  }\n  osqp_tic(work->timer); // Start timer\n#endif /* ifdef PROFILING */\n\n  nnzP = work->data->P->p[work->data->P->n];\n\n  if (Px_new_idx) { // Passing the index of elements changed\n    // Check if number of elements is less or equal than the total number of\n    // nonzeros in P\n    if (P_new_n > nnzP) {\n# ifdef PRINTING\n      c_eprint(\"new number of elements (%i) greater than elements in P (%i)\",\n               (int)P_new_n,\n               (int)nnzP);\n# endif /* ifdef PRINTING */\n      return 1;\n    }\n  }\n\n  if (work->settings->scaling) {\n    // Unscale data\n    unscale_data(work);\n  }\n\n  // Update P elements\n  if (Px_new_idx) { // Change only Px_new_idx\n    for (i = 0; i < P_new_n; i++) {\n      work->data->P->x[Px_new_idx[i]] = Px_new[i];\n    }\n  }\n  else // Change whole P\n  {\n    for (i = 0; i < nnzP; i++) {\n      work->data->P->x[i] = Px_new[i];\n    }\n  }\n\n  if (work->settings->scaling) {\n    // Scale data\n    scale_data(work);\n  }\n\n  // Update linear system structure with new data\n  exitflag = work->linsys_solver->update_matrices(work->linsys_solver,\n                                                  work->data->P,\n                                                  work->data->A);\n\n  // Reset solver information\n  reset_info(work->info);\n\n# ifdef PRINTING\n\n  if (exitflag < 0) {\n    c_eprint(\"new KKT matrix is not quasidefinite\");\n  }\n# endif /* ifdef PRINTING */\n\n#ifdef PROFILING\n  work->info->update_time += osqp_toc(work->timer);\n#endif /* ifdef PROFILING */\n\n  return exitflag;\n}\n\n\nc_int osqp_update_A(OSQPWorkspace *work,\n                    const c_float *Ax_new,\n                    const c_int   *Ax_new_idx,\n                    c_int          A_new_n) {\n  c_int i;        // For indexing\n  c_int exitflag; // Exit flag\n  c_int nnzA;     // Number of nonzeros in A\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n#ifdef PROFILING\n  if (work->clear_update_time == 1) {\n    work->clear_update_time = 0;\n    work->info->update_time = 0.0;\n  }\n  osqp_tic(work->timer); // Start timer\n#endif /* ifdef PROFILING */\n\n  nnzA = work->data->A->p[work->data->A->n];\n\n  if (Ax_new_idx) { // Passing the index of elements changed\n    // Check if number of elements is less or equal than the total number of\n    // nonzeros in A\n    if (A_new_n > nnzA) {\n# ifdef PRINTING\n      c_eprint(\"new number of elements (%i) greater than elements in A (%i)\",\n               (int)A_new_n,\n               (int)nnzA);\n# endif /* ifdef PRINTING */\n      return 1;\n    }\n  }\n\n  if (work->settings->scaling) {\n    // Unscale data\n    unscale_data(work);\n  }\n\n  // Update A elements\n  if (Ax_new_idx) { // Change only Ax_new_idx\n    for (i = 0; i < A_new_n; i++) {\n      work->data->A->x[Ax_new_idx[i]] = Ax_new[i];\n    }\n  }\n  else { // Change whole A\n    for (i = 0; i < nnzA; i++) {\n      work->data->A->x[i] = Ax_new[i];\n    }\n  }\n\n  if (work->settings->scaling) {\n    // Scale data\n    scale_data(work);\n  }\n\n  // Update linear system structure with new data\n  exitflag = work->linsys_solver->update_matrices(work->linsys_solver,\n                                                  work->data->P,\n                                                  work->data->A);\n\n  // Reset solver information\n  reset_info(work->info);\n\n# ifdef PRINTING\n\n  if (exitflag < 0) {\n    c_eprint(\"new KKT matrix is not quasidefinite\");\n  }\n# endif /* ifdef PRINTING */\n\n#ifdef PROFILING\n  work->info->update_time += osqp_toc(work->timer);\n#endif /* ifdef PROFILING */\n\n  return exitflag;\n}\n\n\nc_int osqp_update_P_A(OSQPWorkspace *work,\n                      const c_float *Px_new,\n                      const c_int   *Px_new_idx,\n                      c_int          P_new_n,\n                      const c_float *Ax_new,\n                      const c_int   *Ax_new_idx,\n                      c_int          A_new_n) {\n  c_int i;          // For indexing\n  c_int exitflag;   // Exit flag\n  c_int nnzP, nnzA; // Number of nonzeros in P and A\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n#ifdef PROFILING\n  if (work->clear_update_time == 1) {\n    work->clear_update_time = 0;\n    work->info->update_time = 0.0;\n  }\n  osqp_tic(work->timer); // Start timer\n#endif /* ifdef PROFILING */\n\n  nnzP = work->data->P->p[work->data->P->n];\n  nnzA = work->data->A->p[work->data->A->n];\n\n\n  if (Px_new_idx) { // Passing the index of elements changed\n    // Check if number of elements is less or equal than the total number of\n    // nonzeros in P\n    if (P_new_n > nnzP) {\n# ifdef PRINTING\n      c_eprint(\"new number of elements (%i) greater than elements in P (%i)\",\n               (int)P_new_n,\n               (int)nnzP);\n# endif /* ifdef PRINTING */\n      return 1;\n    }\n  }\n\n\n  if (Ax_new_idx) { // Passing the index of elements changed\n    // Check if number of elements is less or equal than the total number of\n    // nonzeros in A\n    if (A_new_n > nnzA) {\n# ifdef PRINTING\n      c_eprint(\"new number of elements (%i) greater than elements in A (%i)\",\n               (int)A_new_n,\n               (int)nnzA);\n# endif /* ifdef PRINTING */\n      return 2;\n    }\n  }\n\n  if (work->settings->scaling) {\n    // Unscale data\n    unscale_data(work);\n  }\n\n  // Update P elements\n  if (Px_new_idx) { // Change only Px_new_idx\n    for (i = 0; i < P_new_n; i++) {\n      work->data->P->x[Px_new_idx[i]] = Px_new[i];\n    }\n  }\n  else // Change whole P\n  {\n    for (i = 0; i < nnzP; i++) {\n      work->data->P->x[i] = Px_new[i];\n    }\n  }\n\n  // Update A elements\n  if (Ax_new_idx) { // Change only Ax_new_idx\n    for (i = 0; i < A_new_n; i++) {\n      work->data->A->x[Ax_new_idx[i]] = Ax_new[i];\n    }\n  }\n  else { // Change whole A\n    for (i = 0; i < nnzA; i++) {\n      work->data->A->x[i] = Ax_new[i];\n    }\n  }\n\n  if (work->settings->scaling) {\n    // Scale data\n    scale_data(work);\n  }\n\n  // Update linear system structure with new data\n  exitflag = work->linsys_solver->update_matrices(work->linsys_solver,\n                                                  work->data->P,\n                                                  work->data->A);\n\n  // Reset solver information\n  reset_info(work->info);\n\n# ifdef PRINTING\n\n  if (exitflag < 0) {\n    c_eprint(\"new KKT matrix is not quasidefinite\");\n  }\n# endif /* ifdef PRINTING */\n\n#ifdef PROFILING\n  work->info->update_time += osqp_toc(work->timer);\n#endif /* ifdef PROFILING */\n\n  return exitflag;\n}\n\nc_int osqp_update_rho(OSQPWorkspace *work, c_float rho_new) {\n  c_int exitflag, i;\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Check value of rho\n  if (rho_new <= 0) {\n# ifdef PRINTING\n    c_eprint(\"rho must be positive\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n#ifdef PROFILING\n  if (work->rho_update_from_solve == 0) {\n    if (work->clear_update_time == 1) {\n      work->clear_update_time = 0;\n      work->info->update_time = 0.0;\n    }\n    osqp_tic(work->timer); // Start timer\n  }\n#endif /* ifdef PROFILING */\n\n  // Update rho in settings\n  work->settings->rho = c_min(c_max(rho_new, RHO_MIN), RHO_MAX);\n\n  // Update rho_vec and rho_inv_vec\n  for (i = 0; i < work->data->m; i++) {\n    if (work->constr_type[i] == 0) {\n      // Inequalities\n      work->rho_vec[i]     = work->settings->rho;\n      work->rho_inv_vec[i] = 1. / work->settings->rho;\n    }\n    else if (work->constr_type[i] == 1) {\n      // Equalities\n      work->rho_vec[i]     = RHO_EQ_OVER_RHO_INEQ * work->settings->rho;\n      work->rho_inv_vec[i] = 1. / work->rho_vec[i];\n    }\n  }\n\n  // Update rho_vec in KKT matrix\n  exitflag = work->linsys_solver->update_rho_vec(work->linsys_solver,\n                                                 work->rho_vec);\n\n#ifdef PROFILING\n  if (work->rho_update_from_solve == 0)\n    work->info->update_time += osqp_toc(work->timer);\n#endif /* ifdef PROFILING */\n\n  return exitflag;\n}\n\n#endif // EMBEDDED != 1\n\n/****************************\n* Update problem settings  *\n****************************/\nc_int osqp_update_max_iter(OSQPWorkspace *work, c_int max_iter_new) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Check that max_iter is positive\n  if (max_iter_new <= 0) {\n#ifdef PRINTING\n    c_eprint(\"max_iter must be positive\");\n#endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  // Update max_iter\n  work->settings->max_iter = max_iter_new;\n\n  return 0;\n}\n\nc_int osqp_update_eps_abs(OSQPWorkspace *work, c_float eps_abs_new) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Check that eps_abs is positive\n  if (eps_abs_new < 0.) {\n#ifdef PRINTING\n    c_eprint(\"eps_abs must be nonnegative\");\n#endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  // Update eps_abs\n  work->settings->eps_abs = eps_abs_new;\n\n  return 0;\n}\n\nc_int osqp_update_eps_rel(OSQPWorkspace *work, c_float eps_rel_new) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Check that eps_rel is positive\n  if (eps_rel_new < 0.) {\n#ifdef PRINTING\n    c_eprint(\"eps_rel must be nonnegative\");\n#endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  // Update eps_rel\n  work->settings->eps_rel = eps_rel_new;\n\n  return 0;\n}\n\nc_int osqp_update_eps_prim_inf(OSQPWorkspace *work, c_float eps_prim_inf_new) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Check that eps_prim_inf is positive\n  if (eps_prim_inf_new < 0.) {\n#ifdef PRINTING\n    c_eprint(\"eps_prim_inf must be nonnegative\");\n#endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  // Update eps_prim_inf\n  work->settings->eps_prim_inf = eps_prim_inf_new;\n\n  return 0;\n}\n\nc_int osqp_update_eps_dual_inf(OSQPWorkspace *work, c_float eps_dual_inf_new) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Check that eps_dual_inf is positive\n  if (eps_dual_inf_new < 0.) {\n#ifdef PRINTING\n    c_eprint(\"eps_dual_inf must be nonnegative\");\n#endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  // Update eps_dual_inf\n  work->settings->eps_dual_inf = eps_dual_inf_new;\n\n\n  return 0;\n}\n\nc_int osqp_update_alpha(OSQPWorkspace *work, c_float alpha_new) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Check that alpha is between 0 and 2\n  if ((alpha_new <= 0.) || (alpha_new >= 2.)) {\n#ifdef PRINTING\n    c_eprint(\"alpha must be between 0 and 2\");\n#endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  // Update alpha\n  work->settings->alpha = alpha_new;\n\n  return 0;\n}\n\nc_int osqp_update_warm_start(OSQPWorkspace *work, c_int warm_start_new) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Check that warm_start is either 0 or 1\n  if ((warm_start_new != 0) && (warm_start_new != 1)) {\n#ifdef PRINTING\n    c_eprint(\"warm_start should be either 0 or 1\");\n#endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  // Update warm_start\n  work->settings->warm_start = warm_start_new;\n\n  return 0;\n}\n\nc_int osqp_update_scaled_termination(OSQPWorkspace *work, c_int scaled_termination_new) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Check that scaled_termination is either 0 or 1\n  if ((scaled_termination_new != 0) && (scaled_termination_new != 1)) {\n#ifdef PRINTING\n    c_eprint(\"scaled_termination should be either 0 or 1\");\n#endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  // Update scaled_termination\n  work->settings->scaled_termination = scaled_termination_new;\n\n  return 0;\n}\n\nc_int osqp_update_check_termination(OSQPWorkspace *work, c_int check_termination_new) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Check that check_termination is nonnegative\n  if (check_termination_new < 0) {\n#ifdef PRINTING\n    c_eprint(\"check_termination should be nonnegative\");\n#endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  // Update check_termination\n  work->settings->check_termination = check_termination_new;\n\n  return 0;\n}\n\n#ifndef EMBEDDED\n\nc_int osqp_update_delta(OSQPWorkspace *work, c_float delta_new) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Check that delta is positive\n  if (delta_new <= 0.) {\n# ifdef PRINTING\n    c_eprint(\"delta must be positive\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  // Update delta\n  work->settings->delta = delta_new;\n\n  return 0;\n}\n\nc_int osqp_update_polish(OSQPWorkspace *work, c_int polish_new) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Check that polish is either 0 or 1\n  if ((polish_new != 0) && (polish_new != 1)) {\n# ifdef PRINTING\n    c_eprint(\"polish should be either 0 or 1\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  // Update polish\n  work->settings->polish = polish_new;\n\n# ifdef PROFILING\n\n  // Reset polish time to zero\n  work->info->polish_time = 0.0;\n# endif /* ifdef PROFILING */\n\n  return 0;\n}\n\nc_int osqp_update_polish_refine_iter(OSQPWorkspace *work, c_int polish_refine_iter_new) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Check that polish_refine_iter is nonnegative\n  if (polish_refine_iter_new < 0) {\n# ifdef PRINTING\n    c_eprint(\"polish_refine_iter must be nonnegative\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  // Update polish_refine_iter\n  work->settings->polish_refine_iter = polish_refine_iter_new;\n\n  return 0;\n}\n\nc_int osqp_update_verbose(OSQPWorkspace *work, c_int verbose_new) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Check that verbose is either 0 or 1\n  if ((verbose_new != 0) && (verbose_new != 1)) {\n# ifdef PRINTING\n    c_eprint(\"verbose should be either 0 or 1\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  // Update verbose\n  work->settings->verbose = verbose_new;\n\n  return 0;\n}\n\n#endif // EMBEDDED\n\n#ifdef PROFILING\n\nc_int osqp_update_time_limit(OSQPWorkspace *work, c_float time_limit_new) {\n\n  // Check if workspace has been initialized\n  if (!work) return osqp_error(OSQP_WORKSPACE_NOT_INIT_ERROR);\n\n  // Check that time_limit is nonnegative\n  if (time_limit_new < 0.) {\n# ifdef PRINTING\n    c_print(\"time_limit must be nonnegative\\n\");\n# endif /* ifdef PRINTING */\n    return 1;\n  }\n\n  // Update time_limit\n  work->settings->time_limit = time_limit_new;\n\n  return 0;\n}\n#endif /* ifdef PROFILING */\n"
  },
  {
    "path": "osqp/src/polish.c",
    "content": "#include \"polish.h\"\n#include \"lin_alg.h\"\n#include \"util.h\"\n#include \"auxil.h\"\n#include \"lin_sys.h\"\n#include \"kkt.h\"\n#include \"proj.h\"\n#include \"error.h\"\n\n/**\n * Form reduced matrix A that contains only rows that are active at the\n * solution.\n * Ared = vstack[Alow, Aupp]\n * Active constraints are guessed from the primal and dual solution returned by\n * the ADMM.\n * @param  work Workspace\n * @return      Number of rows in Ared, negative if error\n */\nstatic c_int form_Ared(OSQPWorkspace *work) {\n  c_int j, ptr;\n  c_int Ared_nnz = 0;\n\n  // Initialize counters for active constraints\n  work->pol->n_low = 0;\n  work->pol->n_upp = 0;\n\n  /* Guess which linear constraints are lower-active, upper-active and free\n   *    A_to_Alow[j] = -1    (if j-th row of A is not inserted in Alow)\n   *    A_to_Alow[j] =  i    (if j-th row of A is inserted at i-th row of Alow)\n   * Aupp is formed in the equivalent way.\n   * Ared is formed by stacking vertically Alow and Aupp.\n   */\n  for (j = 0; j < work->data->m; j++) {\n    if (work->z[j] - work->data->l[j] < -work->y[j]) { // lower-active\n      work->pol->Alow_to_A[work->pol->n_low] = j;\n      work->pol->A_to_Alow[j]                = work->pol->n_low++;\n    } else {\n      work->pol->A_to_Alow[j] = -1;\n    }\n  }\n\n  for (j = 0; j < work->data->m; j++) {\n    if (work->data->u[j] - work->z[j] < work->y[j]) { // upper-active\n      work->pol->Aupp_to_A[work->pol->n_upp] = j;\n      work->pol->A_to_Aupp[j]                = work->pol->n_upp++;\n    } else {\n      work->pol->A_to_Aupp[j] = -1;\n    }\n  }\n\n  // Check if there are no active constraints\n  if (work->pol->n_low + work->pol->n_upp == 0) {\n    // Form empty Ared\n    work->pol->Ared = csc_spalloc(0, work->data->n, 0, 1, 0);\n    if (!(work->pol->Ared)) return -1;\n    int_vec_set_scalar(work->pol->Ared->p, 0, work->data->n + 1);\n    return 0; // mred = 0\n  }\n\n  // Count number of elements in Ared\n  for (j = 0; j < work->data->A->p[work->data->A->n]; j++) {\n    if ((work->pol->A_to_Alow[work->data->A->i[j]] != -1) ||\n        (work->pol->A_to_Aupp[work->data->A->i[j]] != -1)) Ared_nnz++;\n  }\n\n  // Form Ared\n  // Ared = vstack[Alow, Aupp]\n  work->pol->Ared = csc_spalloc(work->pol->n_low + work->pol->n_upp,\n                                work->data->n, Ared_nnz, 1, 0);\n  if (!(work->pol->Ared)) return -1;\n  Ared_nnz = 0; // counter\n\n  for (j = 0; j < work->data->n; j++) { // Cycle over columns of A\n    work->pol->Ared->p[j] = Ared_nnz;\n\n    for (ptr = work->data->A->p[j]; ptr < work->data->A->p[j + 1]; ptr++) {\n      // Cycle over elements in j-th column\n      if (work->pol->A_to_Alow[work->data->A->i[ptr]] != -1) {\n        // Lower-active rows of A\n        work->pol->Ared->i[Ared_nnz] =\n          work->pol->A_to_Alow[work->data->A->i[ptr]];\n        work->pol->Ared->x[Ared_nnz++] = work->data->A->x[ptr];\n      } else if (work->pol->A_to_Aupp[work->data->A->i[ptr]] != -1) {\n        // Upper-active rows of A\n        work->pol->Ared->i[Ared_nnz] = work->pol->A_to_Aupp[work->data->A->i[ptr]] \\\n                                       + work->pol->n_low;\n        work->pol->Ared->x[Ared_nnz++] = work->data->A->x[ptr];\n      }\n    }\n  }\n\n  // Update the last element in Ared->p\n  work->pol->Ared->p[work->data->n] = Ared_nnz;\n\n  // Return number of rows in Ared\n  return work->pol->n_low + work->pol->n_upp;\n}\n\n/**\n * Form reduced right-hand side rhs_red = vstack[-q, l_low, u_upp]\n * @param  work Workspace\n * @param  rhs  right-hand-side\n * @return      reduced rhs\n */\nstatic void form_rhs_red(OSQPWorkspace *work, c_float *rhs) {\n  c_int j;\n\n  // Form the rhs of the reduced KKT linear system\n  for (j = 0; j < work->data->n; j++) { // -q\n    rhs[j] = -work->data->q[j];\n  }\n\n  for (j = 0; j < work->pol->n_low; j++) { // l_low\n    rhs[work->data->n + j] = work->data->l[work->pol->Alow_to_A[j]];\n  }\n\n  for (j = 0; j < work->pol->n_upp; j++) { // u_upp\n    rhs[work->data->n + work->pol->n_low + j] =\n      work->data->u[work->pol->Aupp_to_A[j]];\n  }\n}\n\n/**\n * Perform iterative refinement on the polished solution:\n *    (repeat)\n *    1. (K + dK) * dz = b - K*z\n *    2. z <- z + dz\n * @param  work Solver workspace\n * @param  p    Private variable for solving linear system\n * @param  z    Initial z value\n * @param  b    RHS of the linear system\n * @return      Exitflag\n */\nstatic c_int iterative_refinement(OSQPWorkspace *work,\n                                  LinSysSolver  *p,\n                                  c_float       *z,\n                                  c_float       *b) {\n  c_int i, j, n;\n  c_float *rhs;\n\n  if (work->settings->polish_refine_iter > 0) {\n\n    // Assign dimension n\n    n = work->data->n + work->pol->Ared->m;\n\n    // Allocate rhs vector\n    rhs = (c_float *)c_malloc(sizeof(c_float) * n);\n\n    if (!rhs) {\n      return osqp_error(OSQP_MEM_ALLOC_ERROR);\n    } else {\n      for (i = 0; i < work->settings->polish_refine_iter; i++) {\n        // Form the RHS for the iterative refinement:  b - K*z\n        prea_vec_copy(b, rhs, n);\n\n        // Upper Part: R^{n}\n        // -= Px (upper triang)\n        mat_vec(work->data->P, z, rhs, -1);\n\n        // -= Px (lower triang)\n        mat_tpose_vec(work->data->P, z, rhs, -1, 1);\n\n        // -= Ared'*y_red\n        mat_tpose_vec(work->pol->Ared, z + work->data->n, rhs, -1, 0);\n\n        // Lower Part: R^{m}\n        mat_vec(work->pol->Ared, z, rhs + work->data->n, -1);\n\n        // Solve linear system. Store solution in rhs\n        p->solve(p, rhs);\n\n        // Update solution\n        for (j = 0; j < n; j++) {\n          z[j] += rhs[j];\n        }\n      }\n    }\n    if (rhs) c_free(rhs);\n  }\n  return 0;\n}\n\n/**\n * Compute dual variable y from yred\n * @param work Workspace\n * @param yred Dual variables associated to active constraints\n */\nstatic void get_ypol_from_yred(OSQPWorkspace *work, c_float *yred) {\n  c_int j;\n\n  // If there are no active constraints\n  if (work->pol->n_low + work->pol->n_upp == 0) {\n    vec_set_scalar(work->pol->y, 0., work->data->m);\n    return;\n  }\n\n  // NB: yred = vstack[ylow, yupp]\n  for (j = 0; j < work->data->m; j++) {\n    if (work->pol->A_to_Alow[j] != -1) {\n      // lower-active\n      work->pol->y[j] = yred[work->pol->A_to_Alow[j]];\n    } else if (work->pol->A_to_Aupp[j] != -1) {\n      // upper-active\n      work->pol->y[j] = yred[work->pol->A_to_Aupp[j] + work->pol->n_low];\n    } else {\n      // inactive\n      work->pol->y[j] = 0.0;\n    }\n  }\n}\n\nc_int polish(OSQPWorkspace *work) {\n  c_int mred, polish_successful, exitflag;\n  c_float *rhs_red;\n  LinSysSolver *plsh;\n  c_float *pol_sol; // Polished solution\n\n#ifdef PROFILING\n  osqp_tic(work->timer); // Start timer\n#endif /* ifdef PROFILING */\n\n  // Form Ared by assuming the active constraints and store in work->pol->Ared\n  mred = form_Ared(work);\n  if (mred < 0) { // work->pol->red = OSQP_NULL\n    // Polishing failed\n    work->info->status_polish = -1;\n\n    return -1;\n  }\n\n  // Form and factorize reduced KKT\n  exitflag = init_linsys_solver(&plsh, work->data->P, work->pol->Ared,\n                                work->settings->delta, OSQP_NULL,\n                                work->settings->linsys_solver, 1);\n\n  if (exitflag) {\n    // Polishing failed\n    work->info->status_polish = -1;\n\n    // Memory clean-up\n    if (work->pol->Ared) csc_spfree(work->pol->Ared);\n\n    return 1;\n  }\n\n  // Form reduced right-hand side rhs_red\n  rhs_red = c_malloc(sizeof(c_float) * (work->data->n + mred));\n  if (!rhs_red) {\n    // Polishing failed\n    work->info->status_polish = -1;\n\n    // Memory clean-up\n    csc_spfree(work->pol->Ared);\n\n    return -1;\n  }\n  form_rhs_red(work, rhs_red);\n\n  pol_sol = vec_copy(rhs_red, work->data->n + mred);\n  if (!pol_sol) {\n    // Polishing failed\n    work->info->status_polish = -1;\n\n    // Memory clean-up\n    csc_spfree(work->pol->Ared);\n    c_free(rhs_red);\n  \n    return -1;\n  }\n\n  // Solve the reduced KKT system\n  plsh->solve(plsh, pol_sol);\n\n  // Perform iterative refinement to compensate for the regularization error\n  exitflag = iterative_refinement(work, plsh, pol_sol, rhs_red);\n\n  if (exitflag) {\n    // Polishing failed\n    work->info->status_polish = -1;\n\n    // Memory clean-up\n    csc_spfree(work->pol->Ared);\n    c_free(rhs_red);\n    c_free(pol_sol);\n  \n    return -1;\n  }\n\n  // Store the polished solution (x,z,y)\n  prea_vec_copy(pol_sol, work->pol->x, work->data->n);   // pol->x\n  mat_vec(work->data->A, work->pol->x, work->pol->z, 0); // pol->z\n  get_ypol_from_yred(work, pol_sol + work->data->n);     // pol->y\n\n  // Ensure (z,y) satisfies normal cone constraint\n  project_normalcone(work, work->pol->z, work->pol->y);\n\n  // Compute primal and dual residuals at the polished solution\n  update_info(work, 0, 1, 1);\n\n  // Check if polish was successful\n  polish_successful = (work->pol->pri_res < work->info->pri_res &&\n                       work->pol->dua_res < work->info->dua_res) || // Residuals\n                                                                    // are\n                                                                    // reduced\n                      (work->pol->pri_res < work->info->pri_res &&\n                       work->info->dua_res < 1e-10) ||              // Dual\n                                                                    // residual\n                                                                    // already\n                                                                    // tiny\n                      (work->pol->dua_res < work->info->dua_res &&\n                       work->info->pri_res < 1e-10);                // Primal\n                                                                    // residual\n                                                                    // already\n                                                                    // tiny\n\n  if (polish_successful) {\n    // Update solver information\n    work->info->obj_val       = work->pol->obj_val;\n    work->info->pri_res       = work->pol->pri_res;\n    work->info->dua_res       = work->pol->dua_res;\n    work->info->status_polish = 1;\n\n    // Update (x, z, y) in ADMM iterations\n    // NB: z needed for warm starting\n    prea_vec_copy(work->pol->x, work->x, work->data->n);\n    prea_vec_copy(work->pol->z, work->z, work->data->m);\n    prea_vec_copy(work->pol->y, work->y, work->data->m);\n\n    // Print summary\n#ifdef PRINTING\n\n    if (work->settings->verbose) print_polish(work);\n#endif /* ifdef PRINTING */\n  } else { // Polishing failed\n    work->info->status_polish = -1;\n\n    // TODO: Try to find a better solution on the line connecting ADMM\n    //       and polished solution\n  }\n\n  // Memory clean-up\n  plsh->free(plsh);\n\n  // Checks that they are not NULL are already performed earlier\n  csc_spfree(work->pol->Ared);\n  c_free(rhs_red);\n  c_free(pol_sol);\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/src/proj.c",
    "content": "#include \"proj.h\"\n\n\nvoid project(OSQPWorkspace *work, c_float *z) {\n  c_int i, m;\n\n  m = work->data->m;\n\n  for (i = 0; i < m; i++) {\n    z[i] = c_min(c_max(z[i],\n                       work->data->l[i]), // Between lower\n                 work->data->u[i]);       // and upper bounds\n  }\n}\n\nvoid project_normalcone(OSQPWorkspace *work, c_float *z, c_float *y) {\n  c_int i, m;\n\n  // NB: Use z_prev as temporary vector\n\n  m = work->data->m;\n\n  for (i = 0; i < m; i++) {\n    work->z_prev[i] = z[i] + y[i];\n    z[i]            = c_min(c_max(work->z_prev[i], work->data->l[i]),\n                            work->data->u[i]);\n    y[i] = work->z_prev[i] - z[i];\n  }\n}\n"
  },
  {
    "path": "osqp/src/scaling.c",
    "content": "#include \"scaling.h\"\n\n#if EMBEDDED != 1\n\n\n// Set values lower than threshold SCALING_REG to 1\nvoid limit_scaling(c_float *D, c_int n) {\n  c_int i;\n\n  for (i = 0; i < n; i++) {\n    D[i] = D[i] < MIN_SCALING ? 1.0 : D[i];\n    D[i] = D[i] > MAX_SCALING ? MAX_SCALING : D[i];\n  }\n}\n\n/**\n * Compute infinite norm of the columns of the KKT matrix without forming it\n *\n * The norm is stored in the vector v = (D, E)\n *\n * @param P        Cost matrix\n * @param A        Constraints matrix\n * @param D        Norm of columns related to variables\n * @param D_temp_A Temporary vector for norm of columns of A\n * @param E        Norm of columns related to constraints\n * @param n        Dimension of KKT matrix\n */\nvoid compute_inf_norm_cols_KKT(const csc *P, const csc *A,\n                               c_float *D, c_float *D_temp_A,\n                               c_float *E, c_int n) {\n  // First half\n  //  [ P ]\n  //  [ A ]\n  mat_inf_norm_cols_sym_triu(P, D);\n  mat_inf_norm_cols(A, D_temp_A);\n  vec_ew_max_vec(D, D_temp_A, D, n);\n\n  // Second half\n  //  [ A']\n  //  [ 0 ]\n  mat_inf_norm_rows(A, E);\n}\n\nc_int scale_data(OSQPWorkspace *work) {\n  // Scale KKT matrix\n  //\n  //    [ P   A']\n  //    [ A   0 ]\n  //\n  // with diagonal matrix\n  //\n  //  S = [ D    ]\n  //      [    E ]\n  //\n\n  c_int   i;          // Iterations index\n  c_int   n, m;       // Number of constraints and variables\n  c_float c_temp;     // Cost function scaling\n  c_float inf_norm_q; // Infinity norm of q\n\n  n = work->data->n;\n  m = work->data->m;\n\n  // Initialize scaling to 1\n  work->scaling->c = 1.0;\n  vec_set_scalar(work->scaling->D,    1., work->data->n);\n  vec_set_scalar(work->scaling->Dinv, 1., work->data->n);\n  vec_set_scalar(work->scaling->E,    1., work->data->m);\n  vec_set_scalar(work->scaling->Einv, 1., work->data->m);\n\n\n  for (i = 0; i < work->settings->scaling; i++) {\n    //\n    // First Ruiz step\n    //\n\n    // Compute norm of KKT columns\n    compute_inf_norm_cols_KKT(work->data->P, work->data->A,\n                              work->D_temp, work->D_temp_A,\n                              work->E_temp, n);\n\n    // Set to 1 values with 0 norms (avoid crazy scaling)\n    limit_scaling(work->D_temp, n);\n    limit_scaling(work->E_temp, m);\n\n    // Take square root of norms\n    vec_ew_sqrt(work->D_temp, n);\n    vec_ew_sqrt(work->E_temp, m);\n\n    // Divide scalings D and E by themselves\n    vec_ew_recipr(work->D_temp, work->D_temp, n);\n    vec_ew_recipr(work->E_temp, work->E_temp, m);\n\n    // Equilibrate matrices P and A and vector q\n    // P <- DPD\n    mat_premult_diag(work->data->P, work->D_temp);\n    mat_postmult_diag(work->data->P, work->D_temp);\n\n    // A <- EAD\n    mat_premult_diag(work->data->A, work->E_temp);\n    mat_postmult_diag(work->data->A, work->D_temp);\n\n    // q <- Dq\n    vec_ew_prod(work->D_temp,     work->data->q, work->data->q,    n);\n\n    // Update equilibration matrices D and E\n    vec_ew_prod(work->scaling->D, work->D_temp,  work->scaling->D, n);\n    vec_ew_prod(work->scaling->E, work->E_temp,  work->scaling->E, m);\n\n    //\n    // Cost normalization step\n    //\n\n    // Compute avg norm of cols of P\n    mat_inf_norm_cols_sym_triu(work->data->P, work->D_temp);\n    c_temp = vec_mean(work->D_temp, n);\n\n    // Compute inf norm of q\n    inf_norm_q = vec_norm_inf(work->data->q, n);\n\n    // If norm_q == 0, set it to 1 (ignore it in the scaling)\n    // NB: Using the same function as with vectors here\n    limit_scaling(&inf_norm_q, 1);\n\n    // Compute max between avg norm of cols of P and inf norm of q\n    c_temp = c_max(c_temp, inf_norm_q);\n\n    // Limit scaling (use same function as with vectors)\n    limit_scaling(&c_temp, 1);\n\n    // Invert scaling c = 1 / cost_measure\n    c_temp = 1. / c_temp;\n\n    // Scale P\n    mat_mult_scalar(work->data->P, c_temp);\n\n    // Scale q\n    vec_mult_scalar(work->data->q, c_temp, n);\n\n    // Update cost scaling\n    work->scaling->c *= c_temp;\n  }\n\n\n  // Store cinv, Dinv, Einv\n  work->scaling->cinv = 1. / work->scaling->c;\n  vec_ew_recipr(work->scaling->D, work->scaling->Dinv, work->data->n);\n  vec_ew_recipr(work->scaling->E, work->scaling->Einv, work->data->m);\n\n\n  // Scale problem vectors l, u\n  vec_ew_prod(work->scaling->E, work->data->l, work->data->l, work->data->m);\n  vec_ew_prod(work->scaling->E, work->data->u, work->data->u, work->data->m);\n\n  return 0;\n}\n\n#endif // EMBEDDED\n\nc_int unscale_data(OSQPWorkspace *work) {\n  // Unscale cost\n  mat_mult_scalar(work->data->P, work->scaling->cinv);\n  mat_premult_diag(work->data->P, work->scaling->Dinv);\n  mat_postmult_diag(work->data->P, work->scaling->Dinv);\n  vec_mult_scalar(work->data->q, work->scaling->cinv, work->data->n);\n  vec_ew_prod(work->scaling->Dinv, work->data->q, work->data->q, work->data->n);\n\n  // Unscale constraints\n  mat_premult_diag(work->data->A, work->scaling->Einv);\n  mat_postmult_diag(work->data->A, work->scaling->Dinv);\n  vec_ew_prod(work->scaling->Einv, work->data->l, work->data->l, work->data->m);\n  vec_ew_prod(work->scaling->Einv, work->data->u, work->data->u, work->data->m);\n\n  return 0;\n}\n\nc_int unscale_solution(OSQPWorkspace *work) {\n  // primal\n  vec_ew_prod(work->scaling->D,\n              work->solution->x,\n              work->solution->x,\n              work->data->n);\n\n  // dual\n  vec_ew_prod(work->scaling->E,\n              work->solution->y,\n              work->solution->y,\n              work->data->m);\n  vec_mult_scalar(work->solution->y, work->scaling->cinv, work->data->m);\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/src/util.c",
    "content": "#include \"util.h\"\n\n/***************\n* Versioning  *\n***************/\nconst char* osqp_version(void) {\n  return OSQP_VERSION;\n}\n\n/************************************\n* Printing Constants to set Layout *\n************************************/\n#ifdef PRINTING\n# define HEADER_LINE_LEN 65\n#endif /* ifdef PRINTING */\n\n/**********************\n* Utility Functions  *\n**********************/\nvoid c_strcpy(char dest[], const char source[]) {\n  int i = 0;\n\n  while (1) {\n    dest[i] = source[i];\n\n    if (dest[i] == '\\0') break;\n    i++;\n  }\n}\n\n#ifdef PRINTING\n\nstatic void print_line(void) {\n  char  the_line[HEADER_LINE_LEN + 1];\n  c_int i;\n\n  for (i = 0; i < HEADER_LINE_LEN; ++i) the_line[i] = '-';\n  the_line[HEADER_LINE_LEN] = '\\0';\n  c_print(\"%s\\n\", the_line);\n}\n\nvoid print_header(void) {\n  // Different indentation required for windows\n#if defined(IS_WINDOWS) && !defined(PYTHON)\n  c_print(\"iter  \");\n#else\n  c_print(\"iter   \");\n#endif\n\n  // Main information\n  c_print(\"objective    pri res    dua res    rho\");\n# ifdef PROFILING\n  c_print(\"        time\");\n# endif /* ifdef PROFILING */\n  c_print(\"\\n\");\n}\n\nvoid print_setup_header(const OSQPWorkspace *work) {\n  OSQPData *data;\n  OSQPSettings *settings;\n  c_int nnz; // Number of nonzeros in the problem\n\n  data     = work->data;\n  settings = work->settings;\n\n  // Number of nonzeros\n  nnz = data->P->p[data->P->n] + data->A->p[data->A->n];\n\n  print_line();\n  c_print(\"           OSQP v%s  -  Operator Splitting QP Solver\\n\"\n          \"              (c) Bartolomeo Stellato,  Goran Banjac\\n\"\n          \"        University of Oxford  -  Stanford University 2019\\n\",\n          OSQP_VERSION);\n  print_line();\n\n  // Print variables and constraints\n  c_print(\"problem:  \");\n  c_print(\"variables n = %i, constraints m = %i\\n          \",\n                                    (int)data->n,\n          (int)data->m);\n  c_print(\"nnz(P) + nnz(A) = %i\\n\", (int)nnz);\n\n  // Print Settings\n  c_print(\"settings: \");\n  c_print(\"linear system solver = %s\",\n          LINSYS_SOLVER_NAME[settings->linsys_solver]);\n\n  if (work->linsys_solver->nthreads != 1) {\n    c_print(\" (%d threads)\", (int)work->linsys_solver->nthreads);\n  }\n  c_print(\",\\n          \");\n\n  c_print(\"eps_abs = %.1e, eps_rel = %.1e,\\n          \",\n          settings->eps_abs, settings->eps_rel);\n  c_print(\"eps_prim_inf = %.1e, eps_dual_inf = %.1e,\\n          \",\n          settings->eps_prim_inf, settings->eps_dual_inf);\n  c_print(\"rho = %.2e \", settings->rho);\n\n  if (settings->adaptive_rho) c_print(\"(adaptive)\");\n  c_print(\",\\n          \");\n  c_print(\"sigma = %.2e, alpha = %.2f, \",\n          settings->sigma, settings->alpha);\n  c_print(\"max_iter = %i\\n\", (int)settings->max_iter);\n\n  if (settings->check_termination) c_print(\n      \"          check_termination: on (interval %i),\\n\",\n      (int)settings->check_termination);\n  else c_print(\"          check_termination: off,\\n\");\n\n# ifdef PROFILING\n  if (settings->time_limit) c_print(\"          time_limit: %.2e sec,\\n\",\n                                    settings->time_limit);\n# endif /* ifdef PROFILING */\n\n  if (settings->scaling) c_print(\"          scaling: on, \");\n  else c_print(\"          scaling: off, \");\n\n  if (settings->scaled_termination) c_print(\"scaled_termination: on\\n\");\n  else c_print(\"scaled_termination: off\\n\");\n\n  if (settings->warm_start) c_print(\"          warm start: on, \");\n  else c_print(\"          warm start: off, \");\n\n  if (settings->polish) c_print(\"polish: on, \");\n  else c_print(\"polish: off, \");\n\n  if (settings->time_limit) c_print(\"time_limit: %.2e sec\\n\", settings->time_limit);\n  else c_print(\"time_limit: off\\n\");\n\n  c_print(\"\\n\");\n}\n\nvoid print_summary(OSQPWorkspace *work) {\n  OSQPInfo *info;\n\n  info = work->info;\n\n  c_print(\"%4i\",     (int)info->iter);\n  c_print(\" %12.4e\", info->obj_val);\n  c_print(\"  %9.2e\", info->pri_res);\n  c_print(\"  %9.2e\", info->dua_res);\n  c_print(\"  %9.2e\", work->settings->rho);\n# ifdef PROFILING\n\n  if (work->first_run) {\n    // total time: setup + solve\n    c_print(\"  %9.2es\", info->setup_time + info->solve_time);\n  } else {\n    // total time: update + solve\n    c_print(\"  %9.2es\", info->update_time + info->solve_time);\n  }\n# endif /* ifdef PROFILING */\n  c_print(\"\\n\");\n\n  work->summary_printed = 1; // Summary has been printed\n}\n\nvoid print_polish(OSQPWorkspace *work) {\n  OSQPInfo *info;\n\n  info = work->info;\n\n  c_print(\"%4s\",     \"plsh\");\n  c_print(\" %12.4e\", info->obj_val);\n  c_print(\"  %9.2e\", info->pri_res);\n  c_print(\"  %9.2e\", info->dua_res);\n\n  // Different characters for windows/unix\n#if defined(IS_WINDOWS) && !defined(PYTHON)\n  c_print(\"  ---------\");\n#else\n  c_print(\"   --------\");\n#endif\n\n# ifdef PROFILING\n  if (work->first_run) {\n    // total time: setup + solve\n    c_print(\"  %9.2es\", info->setup_time + info->solve_time +\n            info->polish_time);\n  } else {\n    // total time: update + solve\n    c_print(\"  %9.2es\", info->update_time + info->solve_time +\n            info->polish_time);\n  }\n# endif /* ifdef PROFILING */\n  c_print(\"\\n\");\n}\n\nvoid print_footer(OSQPInfo *info, c_int polish) {\n  c_print(\"\\n\"); // Add space after iterations\n\n  c_print(\"status:               %s\\n\", info->status);\n\n  if (polish && (info->status_val == OSQP_SOLVED)) {\n    if (info->status_polish == 1) {\n      c_print(\"solution polish:      successful\\n\");\n    } else if (info->status_polish < 0) {\n      c_print(\"solution polish:      unsuccessful\\n\");\n    }\n  }\n\n  c_print(\"number of iterations: %i\\n\", (int)info->iter);\n\n  if ((info->status_val == OSQP_SOLVED) ||\n      (info->status_val == OSQP_SOLVED_INACCURATE)) {\n    c_print(\"optimal objective:    %.4f\\n\", info->obj_val);\n  }\n\n# ifdef PROFILING\n  c_print(\"run time:             %.2es\\n\", info->run_time);\n# endif /* ifdef PROFILING */\n\n# if EMBEDDED != 1\n  c_print(\"optimal rho estimate: %.2e\\n\", info->rho_estimate);\n# endif /* if EMBEDDED != 1 */\n  c_print(\"\\n\");\n}\n\n#endif /* End #ifdef PRINTING */\n\n\n#ifndef EMBEDDED\n\nOSQPSettings* copy_settings(const OSQPSettings *settings) {\n  OSQPSettings *new = c_malloc(sizeof(OSQPSettings));\n\n  if (!new) return OSQP_NULL;\n\n  // Copy settings\n  // NB. Copying them explicitly because memcpy is not\n  // defined when PRINTING is disabled (appears in string.h)\n  new->rho = settings->rho;\n  new->sigma = settings->sigma;\n  new->scaling = settings->scaling;\n\n# if EMBEDDED != 1\n  new->adaptive_rho = settings->adaptive_rho;\n  new->adaptive_rho_interval = settings->adaptive_rho_interval;\n  new->adaptive_rho_tolerance = settings->adaptive_rho_tolerance;\n# ifdef PROFILING\n  new->adaptive_rho_fraction = settings->adaptive_rho_fraction;\n# endif\n# endif // EMBEDDED != 1\n  new->max_iter = settings->max_iter;\n  new->eps_abs = settings->eps_abs;\n  new->eps_rel = settings->eps_rel;\n  new->eps_prim_inf = settings->eps_prim_inf;\n  new->eps_dual_inf = settings->eps_dual_inf;\n  new->alpha = settings->alpha;\n  new->linsys_solver = settings->linsys_solver;\n  new->delta = settings->delta;\n  new->polish = settings->polish;\n  new->polish_refine_iter = settings->polish_refine_iter;\n  new->verbose = settings->verbose;\n  new->scaled_termination = settings->scaled_termination;\n  new->check_termination = settings->check_termination;\n  new->warm_start = settings->warm_start;\n# ifdef PROFILING\n  new->time_limit = settings->time_limit;\n# endif\n\n  return new;\n}\n\n#endif // #ifndef EMBEDDED\n\n\n/*******************\n* Timer Functions *\n*******************/\n\n#ifdef PROFILING\n\n// Windows\n# ifdef IS_WINDOWS\n\nvoid osqp_tic(OSQPTimer *t)\n{\n  QueryPerformanceFrequency(&t->freq);\n  QueryPerformanceCounter(&t->tic);\n}\n\nc_float osqp_toc(OSQPTimer *t)\n{\n  QueryPerformanceCounter(&t->toc);\n  return (t->toc.QuadPart - t->tic.QuadPart) / (c_float)t->freq.QuadPart;\n}\n\n// Mac\n# elif defined IS_MAC\n\nvoid osqp_tic(OSQPTimer *t)\n{\n  /* read current clock cycles */\n  t->tic = mach_absolute_time();\n}\n\nc_float osqp_toc(OSQPTimer *t)\n{\n  uint64_t duration; /* elapsed time in clock cycles*/\n\n  t->toc   = mach_absolute_time();\n  duration = t->toc - t->tic;\n\n  /*conversion from clock cycles to nanoseconds*/\n  mach_timebase_info(&(t->tinfo));\n  duration *= t->tinfo.numer;\n  duration /= t->tinfo.denom;\n\n  return (c_float)duration / 1e9;\n}\n\n// Linux\n# else  /* ifdef IS_WINDOWS */\n\n/* read current time */\nvoid osqp_tic(OSQPTimer *t)\n{\n  clock_gettime(CLOCK_MONOTONIC, &t->tic);\n}\n\n/* return time passed since last call to tic on this timer */\nc_float osqp_toc(OSQPTimer *t)\n{\n  struct timespec temp;\n\n  clock_gettime(CLOCK_MONOTONIC, &t->toc);\n\n  if ((t->toc.tv_nsec - t->tic.tv_nsec) < 0) {\n    temp.tv_sec  = t->toc.tv_sec - t->tic.tv_sec - 1;\n    temp.tv_nsec = 1e9 + t->toc.tv_nsec - t->tic.tv_nsec;\n  } else {\n    temp.tv_sec  = t->toc.tv_sec - t->tic.tv_sec;\n    temp.tv_nsec = t->toc.tv_nsec - t->tic.tv_nsec;\n  }\n  return (c_float)temp.tv_sec + (c_float)temp.tv_nsec / 1e9;\n}\n\n# endif /* ifdef IS_WINDOWS */\n\n#endif // If Profiling end\n\n\n/* ==================== DEBUG FUNCTIONS ======================= */\n\n\n\n// If debug mode enabled\n#ifdef DDEBUG\n\n#ifdef PRINTING\n\nvoid print_csc_matrix(csc *M, const char *name)\n{\n  c_int j, i, row_start, row_stop;\n  c_int k = 0;\n\n  // Print name\n  c_print(\"%s :\\n\", name);\n\n  for (j = 0; j < M->n; j++) {\n    row_start = M->p[j];\n    row_stop  = M->p[j + 1];\n\n    if (row_start == row_stop) continue;\n    else {\n      for (i = row_start; i < row_stop; i++) {\n        c_print(\"\\t[%3u,%3u] = %.3g\\n\", (int)M->i[i], (int)j, M->x[k++]);\n      }\n    }\n  }\n}\n\nvoid dump_csc_matrix(csc *M, const char *file_name) {\n  c_int j, i, row_strt, row_stop;\n  c_int k = 0;\n  FILE *f = fopen(file_name, \"w\");\n\n  if (f != NULL) {\n    for (j = 0; j < M->n; j++) {\n      row_strt = M->p[j];\n      row_stop = M->p[j + 1];\n\n      if (row_strt == row_stop) continue;\n      else {\n        for (i = row_strt; i < row_stop; i++) {\n          fprintf(f, \"%d\\t%d\\t%20.18e\\n\",\n                  (int)M->i[i] + 1, (int)j + 1, M->x[k++]);\n        }\n      }\n    }\n    fprintf(f, \"%d\\t%d\\t%20.18e\\n\", (int)M->m, (int)M->n, 0.0);\n    fclose(f);\n    c_print(\"File %s successfully written.\\n\", file_name);\n  } else {\n    c_eprint(\"Error during writing file %s.\\n\", file_name);\n  }\n}\n\nvoid print_trip_matrix(csc *M, const char *name)\n{\n  c_int k = 0;\n\n  // Print name\n  c_print(\"%s :\\n\", name);\n\n  for (k = 0; k < M->nz; k++) {\n    c_print(\"\\t[%3u, %3u] = %.3g\\n\", (int)M->i[k], (int)M->p[k], M->x[k]);\n  }\n}\n\nvoid print_dns_matrix(c_float *M, c_int m, c_int n, const char *name)\n{\n  c_int i, j;\n\n  c_print(\"%s : \\n\\t\", name);\n\n  for (i = 0; i < m; i++) {   // Cycle over rows\n    for (j = 0; j < n; j++) { // Cycle over columns\n      if (j < n - 1)\n        // c_print(\"% 14.12e,  \", M[j*m+i]);\n        c_print(\"% .3g,  \", M[j * m + i]);\n\n      else\n        // c_print(\"% 14.12e;  \", M[j*m+i]);\n        c_print(\"% .3g;  \", M[j * m + i]);\n    }\n\n    if (i < m - 1) {\n      c_print(\"\\n\\t\");\n    }\n  }\n  c_print(\"\\n\");\n}\n\nvoid print_vec(c_float *v, c_int n, const char *name) {\n  print_dns_matrix(v, 1, n, name);\n}\n\nvoid dump_vec(c_float *v, c_int len, const char *file_name) {\n  c_int i;\n  FILE *f = fopen(file_name, \"w\");\n\n  if (f != NULL) {\n    for (i = 0; i < len; i++) {\n      fprintf(f, \"%20.18e\\n\", v[i]);\n    }\n    fclose(f);\n    c_print(\"File %s successfully written.\\n\", file_name);\n  } else {\n    c_print(\"Error during writing file %s.\\n\", file_name);\n  }\n}\n\nvoid print_vec_int(c_int *x, c_int n, const char *name) {\n  c_int i;\n\n  c_print(\"%s = [\", name);\n\n  for (i = 0; i < n; i++) {\n    c_print(\" %i \", (int)x[i]);\n  }\n  c_print(\"]\\n\");\n}\n\n#endif // PRINTING\n\n#endif // DEBUG MODE\n"
  },
  {
    "path": "osqp/tests/.gitignore",
    "content": "# Data Files\n# -------------------------------------------------------------------\n**/*data.h\n"
  },
  {
    "path": "osqp/tests/CMakeLists.txt",
    "content": "# Add subdirectories of all the tests\nadd_subdirectory(basic_qp)\nadd_subdirectory(basic_qp2)\nadd_subdirectory(lin_alg)\nadd_subdirectory(non_cvx)\nadd_subdirectory(primal_dual_infeasibility)\nadd_subdirectory(primal_infeasibility)\nadd_subdirectory(solve_linsys)\nadd_subdirectory(unconstrained)\nadd_subdirectory(update_matrices)\n\nset(test_headers ${headers} PARENT_SCOPE)\nset(codegen_test_headers ${codegen_headers} PARENT_SCOPE)\n"
  },
  {
    "path": "osqp/tests/README.md",
    "content": "# C unittests\n\nRun\n\n```python\npython generate_tests_data.py\n```\n\nto generate the tests data for each unit test.\n"
  },
  {
    "path": "osqp/tests/__init__.py",
    "content": ""
  },
  {
    "path": "osqp/tests/basic_qp/CMakeLists.txt",
    "content": "get_directory_property(headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION headers)\n                        \nset(headers ${headers}\n    ${CMAKE_CURRENT_SOURCE_DIR}/test_basic_qp.h PARENT_SCOPE)\n\nget_directory_property(codegen_headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION codegen_headers)\n\nset(codegen_headers ${codegen_headers}\n        ${CMAKE_CURRENT_SOURCE_DIR}/data.h PARENT_SCOPE)\n"
  },
  {
    "path": "osqp/tests/basic_qp/__init__.py",
    "content": ""
  },
  {
    "path": "osqp/tests/basic_qp/generate_problem.py",
    "content": "import numpy as np\nfrom scipy import sparse\nimport utils.codegen_utils as cu\n\nP = sparse.triu([[4., 1.], [1., 2.]], format='csc')\nq = np.ones(2)\n\nA = sparse.csc_matrix(np.array([[1., 1.], [1., 0.], [0., 1.], [0., 1.]]))\nl = np.array([1., 0., 0., -np.inf])\nu = np.array([1., 0.7, 0.7, np.inf])\n\nn = P.shape[0]\nm = A.shape[0]\n\n# New data\nq_new = np.array([2.5, 3.2])\nl_new = np.array([0.8, -3.4, -np.inf, 0.5])\nu_new = np.array([1.6, 1.0, np.inf, 0.5])\n\n# Generate problem solutions\nsols_data = {'x_test': np.array([0.3, 0.7]),\n             'y_test': np.array([-2.9, 0.0, 0.2, 0.0]),\n             'obj_value_test': 1.88,\n             'status_test': 'optimal',\n             'q_new': q_new,\n             'l_new': l_new,\n             'u_new': u_new}\n\n# Generate problem data\ncu.generate_problem_data(P, q, A, l, u, 'basic_qp', sols_data)\n"
  },
  {
    "path": "osqp/tests/basic_qp/test_basic_qp.h",
    "content": "#include \"osqp.h\"    // OSQP API\n#include \"auxil.h\"   // Needed for cold_start()\n#include \"cs.h\"      // CSC data structure\n#include \"util.h\"    // Utilities for testing\n#include \"minunit.h\" // Basic testing script header\n\n#include \"basic_qp/data.h\"\n\n\nstatic const char* test_basic_qp_solve()\n{\n  c_int exitflag, tmp_int;\n  c_float tmp_float;\n  csc *tmp_mat, *P_tmp;\n\n  // Problem settings\n  OSQPSettings *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n\n  // Structures\n  OSQPWorkspace *work; // Workspace\n  OSQPData *data;      // Data\n  basic_qp_sols_data *sols_data;\n\n  // Populate data\n  data = generate_problem_basic_qp();\n  sols_data = generate_problem_basic_qp_sols_data();\n\n  // Define Solver settings as default\n  osqp_set_default_settings(settings);\n  settings->max_iter   = 2000;\n  settings->alpha      = 1.6;\n  settings->polish     = 1;\n  settings->scaling    = 0;\n  settings->verbose    = 1;\n  settings->warm_start = 0;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, data, settings);\n\n  // Setup correct\n  mu_assert(\"Basic QP test solve: Setup error!\", exitflag == 0);\n\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Basic QP test solve: Error in solver status!\",\n\t    work->info->status_val == sols_data->status_test);\n\n  // Compare primal solutions\n  mu_assert(\"Basic QP test solve: Error in primal solution!\",\n\t    vec_norm_inf_diff(work->solution->x, sols_data->x_test,\n\t\t\t      data->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  mu_assert(\"Basic QP test solve: Error in dual solution!\",\n\t    vec_norm_inf_diff(work->solution->y, sols_data->y_test,\n\t\t\t      data->m) < TESTS_TOL);\n\n\n  // Compare objective values\n  mu_assert(\"Basic QP test solve: Error in objective value!\",\n\t    c_absval(work->info->obj_val - sols_data->obj_value_test) <\n\t    TESTS_TOL);\n\n  // Try to set wrong settings\n  mu_assert(\"Basic QP test solve: Wrong value of rho not caught!\",\n\t    osqp_update_rho(work, -0.1) == 1);\n\n  mu_assert(\"Basic QP test solve: Wrong value of max_iter not caught!\",\n\t    osqp_update_max_iter(work, -1) == 1);\n\n  mu_assert(\"Basic QP test solve: Wrong value of eps_abs not caught!\",\n\t    osqp_update_eps_abs(work, -1.) == 1);\n\n  mu_assert(\"Basic QP test solve: Wrong value of eps_rel not caught!\",\n\t    osqp_update_eps_rel(work, -1.) == 1);\n\n  mu_assert(\"Basic QP test solve: Wrong value of eps_prim_inf not caught!\",\n\t    osqp_update_eps_prim_inf(work, -0.1) == 1);\n\n  mu_assert(\"Basic QP test solve: Wrong value of eps_dual_inf not caught!\",\n\t    osqp_update_eps_dual_inf(work, -0.1) == 1);\n\n  mu_assert(\"Basic QP test solve: Wrong value of alpha not caught!\",\n\t    osqp_update_alpha(work, 2.0) == 1);\n\n  mu_assert(\"Basic QP test solve: Wrong value of warm_start not caught!\",\n\t    osqp_update_warm_start(work, -1) == 1);\n\n  mu_assert(\"Basic QP test solve: Wrong value of scaled_termination not caught!\",\n\t    osqp_update_scaled_termination(work, 2) == 1);\n\n  mu_assert(\"Basic QP test solve: Wrong value of check_termination not caught!\",\n\t    osqp_update_check_termination(work, -1) == 1);\n\n  mu_assert(\"Basic QP test solve: Wrong value of delta not caught!\",\n\t    osqp_update_delta(work, 0.) == 1);\n\n  mu_assert(\"Basic QP test solve: Wrong value of polish not caught!\",\n\t    osqp_update_polish(work, 2) == 1);\n\n  mu_assert(\"Basic QP test solve: Wrong value of polish_refine_iter not caught!\",\n\t    osqp_update_polish_refine_iter(work, -1) == 1);\n\n  mu_assert(\"Basic QP test solve: Wrong value of verbose not caught!\",\n\t    osqp_update_verbose(work, 2) == 1);\n\n  // Clean workspace\n  osqp_cleanup(work);\n\n  /* =============================\n       SETUP WITH WRONG SETTINGS\n     ============================= */\n\n  // Setup workspace with empty settings\n  exitflag = osqp_setup(&work, data, OSQP_NULL);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to empty settings\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n\n  // Setup workspace with a wrong number of scaling iterations\n  tmp_int = settings->scaling;\n  settings->scaling = -1;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to a negative number of scaling iterations\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->scaling = tmp_int;\n\n  // Setup workspace with wrong settings->adaptive_rho\n  tmp_int = settings->adaptive_rho;\n  settings->adaptive_rho = 2;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to non-boolean settings->adaptive_rho\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->adaptive_rho = tmp_int;\n\n  // Setup workspace with wrong settings->adaptive_rho_interval\n  tmp_int = settings->adaptive_rho_interval;\n  settings->adaptive_rho_interval = -1;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to negative settings->adaptive_rho_interval\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->adaptive_rho_interval = tmp_int;\n\n#ifdef PROFILING\n  // Setup workspace with wrong settings->adaptive_rho_fraction\n  tmp_float = settings->adaptive_rho_fraction;\n  settings->adaptive_rho_fraction = -1.5;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to non-positive settings->adaptive_rho_fraction\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->adaptive_rho_fraction = tmp_float;\n#endif\n\n  // Setup workspace with wrong settings->adaptive_rho_tolerance\n  tmp_float = settings->adaptive_rho_tolerance;\n  settings->adaptive_rho_tolerance = 0.5;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to wrong settings->adaptive_rho_tolerance\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->adaptive_rho_tolerance = tmp_float;\n\n  // Setup workspace with wrong settings->polish_refine_iter\n  tmp_int = settings->polish_refine_iter;\n  settings->polish_refine_iter = -3;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to negative settings->polish_refine_iter\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->polish_refine_iter = tmp_int;\n\n  // Setup workspace with wrong settings->rho\n  tmp_float = settings->rho;\n  settings->rho = 0.0;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to non-positive settings->rho\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->rho = tmp_float;\n\n  // Setup workspace with wrong settings->sigma\n  tmp_float = settings->sigma;\n  settings->sigma = -0.1;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to non-positive settings->sigma\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->sigma = tmp_float;\n\n  // Setup workspace with wrong settings->delta\n  tmp_float = settings->delta;\n  settings->delta = -1.1;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to non-positive settings->delta\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->delta = tmp_float;\n\n  // Setup workspace with wrong settings->max_iter\n  tmp_int = settings->max_iter;\n  settings->max_iter = 0;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to non-positive settings->max_iter\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->max_iter = tmp_int;\n\n  // Setup workspace with wrong settings->eps_abs\n  tmp_float = settings->eps_abs;\n  settings->eps_abs = -1.1;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to negative settings->eps_abs\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->eps_abs = tmp_float;\n\n  // Setup workspace with wrong settings->eps_rel\n  tmp_float = settings->eps_rel;\n  settings->eps_rel = -0.1;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to negative settings->eps_rel\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->eps_rel = tmp_float;\n\n  // Setup workspace with wrong settings->eps_prim_inf\n  tmp_float = settings->eps_prim_inf;\n  settings->eps_prim_inf = -0.1;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to non-positive settings->eps_prim_inf\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->eps_prim_inf = tmp_float;\n\n  // Setup workspace with wrong settings->eps_dual_inf\n  tmp_float = settings->eps_dual_inf;\n  settings->eps_dual_inf = 0.0;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to non-positive settings->eps_dual_inf\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->eps_dual_inf = tmp_float;\n\n  // Setup workspace with wrong settings->alpha\n  tmp_float = settings->alpha;\n  settings->alpha = 2.0;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to wrong settings->alpha\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->alpha = tmp_float;\n\n  // Setup workspace with wrong settings->linsys_solver\n  tmp_int = settings->linsys_solver;\n  settings->linsys_solver = 5;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to wrong settings->linsys_solver\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->linsys_solver = tmp_int;\n\n  // Setup workspace with wrong settings->verbose\n  tmp_int = settings->verbose;\n  settings->verbose = 2;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to non-boolean settings->verbose\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->verbose = tmp_int;\n\n  // Setup workspace with wrong settings->scaled_termination\n  tmp_int = settings->scaled_termination;\n  settings->scaled_termination = 2;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to non-boolean settings->scaled_termination\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->scaled_termination = tmp_int;\n\n  // Setup workspace with wrong settings->check_termination\n  tmp_int = settings->check_termination;\n  settings->check_termination = -1;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to non-boolean settings->check_termination\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->check_termination = tmp_int;\n\n  // Setup workspace with wrong settings->warm_start\n  tmp_int = settings->warm_start;\n  settings->warm_start = 5;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to non-boolean settings->warm_start\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->warm_start = tmp_int;\n\n#ifdef PROFILING\n  // Setup workspace with wrong settings->time_limit\n  tmp_float = settings->time_limit;\n  settings->time_limit = -0.2;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to wrong settings->time_limit\",\n            exitflag == OSQP_SETTINGS_VALIDATION_ERROR);\n  settings->time_limit = tmp_float;\n#endif\n\n\n  /* =========================\n       SETUP WITH WRONG DATA\n     ========================= */\n\n  // Setup workspace with empty data\n  exitflag = osqp_setup(&work, OSQP_NULL, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to empty data\",\n            exitflag == OSQP_DATA_VALIDATION_ERROR);\n\n  // Setup workspace with wrong data->m\n  tmp_int = data->m;\n  data->m = data->m - 1;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to wrong data->m\",\n            exitflag == OSQP_DATA_VALIDATION_ERROR);\n  data->m = tmp_int;\n\n  // Setup workspace with wrong data->n\n  tmp_int = data->n;\n  data->n = data->n + 1;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to wrong data->n\",\n            exitflag == OSQP_DATA_VALIDATION_ERROR);\n\n  // Setup workspace with zero data->n\n  data->n = 0;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to zero data->n\",\n            exitflag == OSQP_DATA_VALIDATION_ERROR);\n  data->n = tmp_int;\n\n  // Setup workspace with wrong P->m\n  tmp_int = data->P->m;\n  data->P->m = data->n + 1;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to wrong P->m\",\n            exitflag == OSQP_DATA_VALIDATION_ERROR);\n  data->P->m = tmp_int;\n\n  // Setup workspace with wrong P->n\n  tmp_int = data->P->n;\n  data->P->n = data->n + 1;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to wrong P->n\",\n            exitflag == OSQP_DATA_VALIDATION_ERROR);\n  data->P->n = tmp_int;\n\n  // Setup workspace with non-upper-triangular P\n  tmp_mat = data->P;\n\n  // Construct non-upper-triangular P\n  P_tmp = (csc*) c_malloc(sizeof(csc));\n  P_tmp->m = 2;\n  P_tmp->n = 2;\n  P_tmp->nz = -1;\n  P_tmp->nzmax = 4;\n  P_tmp->x = (c_float*) c_malloc(4 * sizeof(c_float));\n  P_tmp->x[0] = 4.0;\n  P_tmp->x[1] = 1.0;\n  P_tmp->x[2] = 1.0;\n  P_tmp->x[3] = 2.0;\n  P_tmp->i = (c_int*) c_malloc(4 * sizeof(c_int));\n  P_tmp->i[0] = 0;\n  P_tmp->i[1] = 1;\n  P_tmp->i[2] = 0;\n  P_tmp->i[3] = 1;\n  P_tmp->p = (c_int*) c_malloc((2 + 1) * sizeof(c_int));\n  P_tmp->p[0] = 0;\n  P_tmp->p[1] = 2;\n  P_tmp->p[2] = 4;\n\n  data->P = P_tmp;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to non-triu structure of P\",\n            exitflag == OSQP_DATA_VALIDATION_ERROR);\n  data->P = tmp_mat;\n\n  // Setup workspace with non-consistent bounds\n  data->l[0] = data->u[0] + 1.0;\n  exitflag = osqp_setup(&work, data, settings);\n  mu_assert(\"Basic QP test solve: Setup should result in error due to non-consistent bounds\",\n            exitflag == OSQP_DATA_VALIDATION_ERROR);\n\n\n  // Cleanup data\n  clean_problem_basic_qp(data);\n  clean_problem_basic_qp_sols_data(sols_data);\n\n  // Cleanup\n  c_free(settings);\n  c_free(P_tmp->x);\n  c_free(P_tmp->i);\n  c_free(P_tmp->p);\n  c_free(P_tmp);\n\n  return 0;\n}\n\n#ifdef ENABLE_MKL_PARDISO\nstatic char* test_basic_qp_solve_pardiso()\n{\n  c_int exitflag;\n\n  // Problem settings\n  OSQPSettings *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n\n  // Structures\n  OSQPWorkspace *work; // Workspace\n  OSQPData *data;      // Data\n  basic_qp_sols_data *sols_data;\n\n  // Populate data\n  data = generate_problem_basic_qp();\n  sols_data = generate_problem_basic_qp_sols_data();\n\n\n  // Define Solver settings as default\n  osqp_set_default_settings(settings);\n  settings->max_iter      = 2000;\n  settings->alpha         = 1.6;\n  settings->polish        = 1;\n  settings->scaling       = 0;\n  settings->verbose       = 1;\n  settings->warm_start    = 0;\n  settings->linsys_solver = MKL_PARDISO_SOLVER;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, data, settings);\n\n  // Setup correct\n  mu_assert(\"Basic QP test solve Pardiso: Setup error!\", exitflag == 0);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Basic QP test solve Pardiso: Error in solver status!\",\n            work->info->status_val == sols_data->status_test);\n\n  // Compare primal solutions\n  mu_assert(\"Basic QP test solve Pardiso: Error in primal solution!\",\n            vec_norm_inf_diff(work->solution->x, sols_data->x_test,\n                              data->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  mu_assert(\"Basic QP test solve Pardiso: Error in dual solution!\",\n            vec_norm_inf_diff(work->solution->y, sols_data->y_test,\n                              data->m) < TESTS_TOL);\n\n\n  // Compare objective values\n  mu_assert(\"Basic QP test solve Pardiso: Error in objective value!\",\n            c_absval(work->info->obj_val - sols_data->obj_value_test) <\n            TESTS_TOL);\n\n  // Clean workspace\n  osqp_cleanup(work);\n\n\n  // Cleanup data\n  clean_problem_basic_qp(data);\n  clean_problem_basic_qp_sols_data(sols_data);\n\n  // Cleanup\n  c_free(settings);\n\n  return 0;\n}\n#endif\n\nstatic const char* test_basic_qp_update()\n{\n  c_int exitflag;\n\n  // Problem settings\n  OSQPSettings *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n\n  // Structures\n  OSQPWorkspace *work; // Workspace\n  OSQPData *data;      // Data\n  basic_qp_sols_data *sols_data;\n\n  // Populate data\n  data = generate_problem_basic_qp();\n  sols_data = generate_problem_basic_qp_sols_data();\n\n\n  // Define Solver settings as default\n  osqp_set_default_settings(settings);\n  settings->max_iter   = 200;\n  settings->alpha      = 1.6;\n  settings->polish     = 1;\n  settings->scaling    = 0;\n  settings->verbose    = 1;\n  settings->warm_start = 0;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, data, settings);\n\n  // Setup correct\n  mu_assert(\"Basic QP test update: Setup error!\", exitflag == 0);\n\n\n  // ====================================================================\n  //  Update data\n  // ====================================================================\n\n  // Update linear cost\n  osqp_update_lin_cost(work, sols_data->q_new);\n  mu_assert(\"Basic QP test update: Error in updating linear cost!\",\n            vec_norm_inf_diff(work->data->q, sols_data->q_new,\n                              data->n) < TESTS_TOL);\n\n  // UPDATE BOUND\n  // Try to update with non-consistent values\n  mu_assert(\"Basic QP test update: Error in bounds update ordering not caught!\",\n            osqp_update_bounds(work, sols_data->u_new, sols_data->l_new) == 1);\n\n  // Now update with correct values\n  mu_assert(\"Basic QP test update: Error in bounds update ordering!\",\n            osqp_update_bounds(work, sols_data->l_new, sols_data->u_new) == 0);\n\n  mu_assert(\"Basic QP test update: Error in bounds update, lower bound!\",\n            vec_norm_inf_diff(work->data->l, sols_data->l_new,\n                              data->m) < TESTS_TOL);\n\n  mu_assert(\"Basic QP test update: Error in bounds update, upper bound!\",\n            vec_norm_inf_diff(work->data->u, sols_data->u_new,\n                              data->m) < TESTS_TOL);\n\n  // Return original values\n  osqp_update_bounds(work, data->l, data->u);\n\n\n  // UPDATE LOWER BOUND\n  // Try to update with non-consistent values\n  mu_assert(\n    \"Basic QP test update: Error in lower bound update ordering not caught!\",\n    osqp_update_lower_bound(work, sols_data->u_new) == 1);\n\n  // Now update with correct values\n  mu_assert(\"Basic QP test update: Error in lower bound update ordering!\",\n            osqp_update_lower_bound(work, sols_data->l_new) == 0);\n\n  mu_assert(\"Basic QP test update: Error in updating lower bound!\",\n            vec_norm_inf_diff(work->data->l, sols_data->l_new,\n                              data->m) < TESTS_TOL);\n\n  // Return original values\n  osqp_update_lower_bound(work, data->l);\n\n\n  // UPDATE UPPER BOUND\n  // Try to update with non-consistent values\n  mu_assert(\n    \"Basic QP test update: Error in upper bound update: ordering not caught!\",\n    osqp_update_upper_bound(work, sols_data->l_new) == 1);\n\n  // Now update with correct values\n  mu_assert(\"Basic QP test update: Error in upper bound update: ordering!\",\n            osqp_update_upper_bound(work, sols_data->u_new) == 0);\n\n  mu_assert(\"Basic QP test update: Error in updating upper bound!\",\n            vec_norm_inf_diff(work->data->u, sols_data->u_new,\n                              data->m) < TESTS_TOL);\n\n\n  // Clean workspace\n  osqp_cleanup(work);\n\n\n  // Cleanup data\n  clean_problem_basic_qp(data);\n  clean_problem_basic_qp_sols_data(sols_data);\n\n  // Cleanup\n  c_free(settings);\n\n  return 0;\n}\n\nstatic const char* test_basic_qp_check_termination()\n{\n  c_int exitflag;\n\n  // Problem settings\n  OSQPSettings *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n\n  // Structures\n  OSQPWorkspace *work; // Workspace\n  OSQPData *data;      // Data\n  basic_qp_sols_data *sols_data;\n\n  // Populate data\n  data = generate_problem_basic_qp();\n  sols_data = generate_problem_basic_qp_sols_data();\n\n\n  // Define Solver settings as default\n  osqp_set_default_settings(settings);\n  settings->max_iter          = 200;\n  settings->alpha             = 1.6;\n  settings->polish            = 0;\n  settings->scaling           = 0;\n  settings->verbose           = 1;\n  settings->check_termination = 0;\n  settings->warm_start        = 0;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, data, settings);\n\n  // Setup correct\n  mu_assert(\"Basic QP test solve: Setup error!\", exitflag == 0);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Check if iter == max_iter\n  mu_assert(\n    \"Basic QP test check termination: Error in number of iterations taken!\",\n    work->info->iter == work->settings->max_iter);\n\n  // Compare solver statuses\n  mu_assert(\"Basic QP test check termination: Error in solver status!\",\n            work->info->status_val == sols_data->status_test);\n\n  // Compare primal solutions\n  mu_assert(\"Basic QP test check termination: Error in primal solution!\",\n            vec_norm_inf_diff(work->solution->x, sols_data->x_test,\n                              data->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  // print_vec(work->solution->y, data->m, \"y_sol\");\n  // print_vec(sols_data->y_test, data->m, \"y_test\");\n  mu_assert(\"Basic QP test check termination: Error in dual solution!\",\n            vec_norm_inf_diff(work->solution->y, sols_data->y_test,\n                              data->m) < TESTS_TOL);\n\n  // Compare objective values\n  mu_assert(\"Basic QP test check termination: Error in objective value!\",\n            c_absval(work->info->obj_val - sols_data->obj_value_test) <\n            TESTS_TOL);\n\n  // Clean workspace\n  osqp_cleanup(work);\n\n  // Cleanup data\n  clean_problem_basic_qp(data);\n  clean_problem_basic_qp_sols_data(sols_data);\n\n  // Cleanup\n  c_free(settings);\n\n  return 0;\n}\n\nstatic const char* test_basic_qp_update_rho()\n{\n  c_int extiflag;\n\n  // Problem settings\n  OSQPSettings *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n\n  // Structures\n  OSQPWorkspace *work; // Workspace\n  OSQPData *data;      // Data\n  basic_qp_sols_data *sols_data;\n\n  // Exitflag\n  c_int exitflag;\n\n  // rho to use\n  c_float rho;\n\n  // Define number of iterations to compare\n  c_int n_iter_new_solver, n_iter_update_rho;\n\n  // Populate data\n  data = generate_problem_basic_qp();\n  sols_data = generate_problem_basic_qp_sols_data();\n\n\n  // Define Solver settings as default\n  rho = 0.7;\n  osqp_set_default_settings(settings);\n  settings->rho               = rho;\n  settings->adaptive_rho      = 0; // Disable adaptive rho for this test\n  settings->eps_abs           = 5e-05;\n  settings->eps_rel           = 5e-05;\n  settings->check_termination = 1;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, data, settings);\n\n  // Setup correct\n  mu_assert(\"Basic QP test update rho: Setup error!\", exitflag == 0);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Store number of iterations\n  n_iter_new_solver = work->info->iter;\n\n  // Compare solver statuses\n  mu_assert(\"Update rho test solve: Error in solver status!\",\n            work->info->status_val == sols_data->status_test);\n\n  // Compare primal solutions\n  mu_assert(\"Update rho test solve: Error in primal solution!\",\n            vec_norm_inf_diff(work->solution->x, sols_data->x_test,\n                              data->n)/vec_norm_inf(sols_data->x_test, data->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  mu_assert(\"Update rho test solve: Error in dual solution!\",\n            vec_norm_inf_diff(work->solution->y, sols_data->y_test,\n                              data->m)/vec_norm_inf(sols_data->y_test, data->m) < TESTS_TOL);\n\n  // Compare objective values\n  mu_assert(\"Update rho test solve: Error in objective value!\",\n            c_absval(work->info->obj_val - sols_data->obj_value_test) <\n            TESTS_TOL);\n\n  // Clean workspace\n  osqp_cleanup(work);\n\n\n  // Create new problem with different rho and update it\n  osqp_set_default_settings(settings);\n  settings->rho               = 0.1;\n  settings->adaptive_rho      = 0;\n  settings->check_termination = 1;\n  settings->eps_abs           = 5e-05;\n  settings->eps_rel           = 5e-05;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, data, settings);\n\n  // Setup correct\n  mu_assert(\"Basic QP test update rho: Setup error!\", exitflag == 0);\n\n  // Update rho\n  exitflag = osqp_update_rho(work, rho);\n  mu_assert(\"Basic QP test update rho: Error update rho!\", exitflag == 0);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Basic QP test update rho: Error in solver status!\",\n            work->info->status_val == sols_data->status_test);\n\n  // Compare primal solutions\n  mu_assert(\"Basic QP test update rho: Error in primal solution!\",\n            vec_norm_inf_diff(work->solution->x, sols_data->x_test,\n                              data->n)/vec_norm_inf(sols_data->x_test, data->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  mu_assert(\"Basic QP test update rho: Error in dual solution!\",\n            vec_norm_inf_diff(work->solution->y, sols_data->y_test,\n                              data->m)/vec_norm_inf(sols_data->y_test, data->m)< TESTS_TOL);\n\n  // Compare objective values\n  mu_assert(\"Basic QP test update rho: Error in objective value!\",\n            c_absval(work->info->obj_val - sols_data->obj_value_test) <\n            TESTS_TOL);\n\n  // Get number of iterations\n  n_iter_update_rho = work->info->iter;\n\n  // Assert same number of iterations\n  mu_assert(\"Basic QP test update rho: Error in number of iterations!\",\n            n_iter_new_solver == n_iter_update_rho);\n\n  // Cleanup solver\n  osqp_cleanup(work);\n\n  // Cleanup data\n  clean_problem_basic_qp(data);\n  clean_problem_basic_qp_sols_data(sols_data);\n\n  // Cleanup\n  c_free(settings);\n\n  return 0;\n}\n\n#ifdef PROFILING\nstatic const char* test_basic_qp_time_limit()\n{\n  c_int exitflag;\n\n  // Problem settings\n  OSQPSettings *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n\n  // Structures\n  OSQPWorkspace *work; // Workspace\n  OSQPData *data;      // Data\n  basic_qp_sols_data *sols_data;\n\n  // Populate data\n  data = generate_problem_basic_qp();\n  sols_data = generate_problem_basic_qp_sols_data();\n\n  // Define Solver settings as default\n  osqp_set_default_settings(settings);\n  settings->rho = 20;\n  settings->adaptive_rho = 0;\n\n  // Check default time limit\n  mu_assert(\"Basic QP test time limit: Default not correct\", settings->time_limit == 0);\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, data, settings);\n\n  // Setup correct\n  mu_assert(\"Basic QP test time limit: Setup error!\", exitflag == 0);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Basic QP test time limit: Error in no time limit solver status!\",\n\t    work->info->status_val == sols_data->status_test);\n\n  // Update time limit\n# ifdef PRINTING\n  osqp_update_time_limit(work, 1e-5);\n  osqp_update_eps_rel(work, 1e-09);\n  osqp_update_eps_abs(work, 1e-09);\n# else\n  // Not printing makes the code run a lot faster, so we need to make it work harder\n  // to fail by time limit exceeded\n  osqp_update_time_limit(work, 1e-7);\n  osqp_update_eps_rel(work, 1e-12);\n  osqp_update_eps_abs(work, 1e-12);\n# endif\n  osqp_update_max_iter(work, (c_int)2e9);\n  osqp_update_check_termination(work, 0);\n\n  // Solve Problem\n  cold_start(work);\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Basic QP test time limit: Error in timed out solver status!\",\n\t    work->info->status_val == OSQP_TIME_LIMIT_REACHED);\n\n  // Cleanup solver\n  osqp_cleanup(work);\n\n  // Cleanup data\n  clean_problem_basic_qp(data);\n  clean_problem_basic_qp_sols_data(sols_data);\n\n  // Cleanup\n  c_free(settings);\n\n  return 0;\n}\n#endif // PROFILING\n\n\nstatic const char* test_basic_qp_warm_start()\n{\n  c_int exitflag, iter;\n\n  // Cold started variables\n  c_float x0[2] = { 0.0, 0.0, };\n  c_float y0[4] = { 0.0, 0.0, 0.0, 0.0, };\n\n  // Optimal solution\n  c_float xopt[2] = { 0.3, 0.7, };\n  c_float yopt[4] = {-2.9, 0.0, 0.2, 0.0, };\n\n  // Problem settings\n  OSQPSettings *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n\n  // Structures\n  OSQPWorkspace *work; // Workspace\n  OSQPData *data;      // Data\n  basic_qp_sols_data *sols_data;\n\n  // Populate data\n  data = generate_problem_basic_qp();\n  sols_data = generate_problem_basic_qp_sols_data();\n\n  // Define Solver settings as default\n  osqp_set_default_settings(settings);\n  settings->check_termination = 1;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, data, settings);\n\n  // Solve Problem\n  osqp_solve(work);\n  iter = work->info->iter;\n\n  // Cold start and solve again\n  osqp_warm_start(work, x0, y0);\n  osqp_solve(work);\n\n  // Check if the number of iterations is the same\n  mu_assert(\"Basic QP test warm start: Cold start error!\", work->info->iter == iter);\n\n  // Warm start from the solution and solve again\n  osqp_warm_start_x(work, xopt);\n  osqp_warm_start_y(work, yopt);\n  osqp_solve(work);\n\n  // Check that the number of iterations equals 1\n  mu_assert(\"Basic QP test warm start: Warm start error!\", work->info->iter == 1);\n\n  // Cleanup solver\n  osqp_cleanup(work);\n\n  // Cleanup data\n  clean_problem_basic_qp(data);\n  clean_problem_basic_qp_sols_data(sols_data);\n\n  // Cleanup\n  c_free(settings);\n\n  return 0;\n}\n\n\nstatic const char* test_basic_qp()\n{\n  mu_run_test(test_basic_qp_solve);\n#ifdef ENABLE_MKL_PARDISO\n  mu_run_test(test_basic_qp_solve_pardiso);\n#endif\n  mu_run_test(test_basic_qp_update);\n  mu_run_test(test_basic_qp_check_termination);\n  mu_run_test(test_basic_qp_update_rho);\n#ifdef PROFILING\n  mu_run_test(test_basic_qp_time_limit);\n#endif\n  mu_run_test(test_basic_qp_warm_start);\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/tests/basic_qp2/CMakeLists.txt",
    "content": "get_directory_property(headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION headers)\n\nset(headers ${headers}\n${CMAKE_CURRENT_SOURCE_DIR}/test_basic_qp2.h PARENT_SCOPE)\n\nget_directory_property(codegen_headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION codegen_headers)\n\nset(codegen_headers ${codegen_headers}\n        ${CMAKE_CURRENT_SOURCE_DIR}/data.h PARENT_SCOPE)\n"
  },
  {
    "path": "osqp/tests/basic_qp2/__init__.py",
    "content": ""
  },
  {
    "path": "osqp/tests/basic_qp2/generate_problem.py",
    "content": "import numpy as np\nfrom scipy import sparse\nimport utils.codegen_utils as cu\n\nP = sparse.triu([[11., 0.], [0., 0.]], format='csc')\nq = np.array([3., 4.])\n\nA = sparse.csc_matrix(np.array([[-1., 0.], [0., -1.], [-1., 3.],\n                                [2., 5.], [3., 4]]))\nl = -np.inf * np.ones(A.shape[0])\nu = np.array([0., 0., -15., 100., 80.])\n\nn = P.shape[0]\nm = A.shape[0]\n\n# New data\nq_new = np.array([1., 1.])\nu_new = np.array([-2., 0., -20., 100., 80.])\n\n# Generate problem solutions\nsols_data = {'x_test': np.array([15., -0.]),\n             'y_test': np.array([0., 508., 168., 0., 0.]),\n             'obj_value_test': 1282.5,\n             'status_test': 'optimal',\n             'q_new': q_new,\n             'u_new': u_new,\n             'x_test_new': np.array([20., -0.]),\n             'y_test_new': np.array([0., 664., 221., 0., 0.]),\n             'obj_value_test_new': 2220.0,\n             'status_test_new': 'optimal'}\n\n\n# Generate problem data\ncu.generate_problem_data(P, q, A, l, u, 'basic_qp2', sols_data)\n"
  },
  {
    "path": "osqp/tests/basic_qp2/test_basic_qp2.h",
    "content": "#include \"osqp.h\"    // OSQP API\n#include \"minunit.h\" // Basic testing script header\n\n\n#include \"basic_qp2/data.h\"\n\n\nstatic const char* test_basic_qp2_solve()\n{\n  c_int exitflag;\n\n  // Problem settings\n  OSQPSettings *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n\n  // Structures\n  OSQPWorkspace *work; // Workspace\n  OSQPData *data;      // Data\n  basic_qp2_sols_data *sols_data;\n\n\n  // Populate data\n  data = generate_problem_basic_qp2();\n  sols_data = generate_problem_basic_qp2_sols_data();\n\n\n  // Define Solver settings as default\n  osqp_set_default_settings(settings);\n  settings->alpha   = 1.6;\n  settings->rho     = 0.1;\n  settings->polish  = 1;\n  settings->verbose = 1;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, data, settings);\n\n  // Setup correct\n  mu_assert(\"Basic QP 2 test solve: Setup error!\", exitflag == 0);\n\n  // Solve Problem first time\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Basic QP 2 test solve: Error in solver status!\",\n            work->info->status_val == sols_data->status_test);\n\n  // Compare primal solutions\n  mu_assert(\"Basic QP 2 test solve: Error in primal solution!\",\n            vec_norm_inf_diff(work->solution->x, sols_data->x_test,\n                              data->n) /\n            vec_norm_inf(sols_data->x_test_new, data->n) < TESTS_TOL);\n\n\n  // Compare dual solutions\n  mu_assert(\"Basic QP 2 test solve: Error in dual solution!\",\n            vec_norm_inf_diff(work->solution->y, sols_data->y_test,\n                              data->m) /\n            vec_norm_inf(sols_data->y_test_new, data->m) < TESTS_TOL);\n\n  // Compare objective values\n  mu_assert(\"Basic QP 2 test solve: Error in objective value!\",\n            c_absval(work->info->obj_val - sols_data->obj_value_test)/(c_absval(sols_data->obj_value_test)) < TESTS_TOL);\n\n  // Clean workspace\n  osqp_cleanup(work);\n\n  // Cleanup settings and data\n  c_free(settings);\n  clean_problem_basic_qp2(data);\n  clean_problem_basic_qp2_sols_data(sols_data);\n\n  return 0;\n}\n\n#ifdef ENABLE_MKL_PARDISO\nstatic char* test_basic_qp2_solve_pardiso()\n{\n  c_int exitflag;\n\n  // Problem settings\n  OSQPSettings *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n\n  // Structures\n  OSQPWorkspace *work; // Workspace\n  OSQPData *data;      // Data\n  basic_qp2_sols_data *sols_data;\n\n\n  // Populate data\n  data = generate_problem_basic_qp2();\n  sols_data = generate_problem_basic_qp2_sols_data();\n\n\n  // Define Solver settings as default\n  osqp_set_default_settings(settings);\n  settings->alpha         = 1.6;\n  settings->rho           = 0.1;\n  settings->polish        = 1;\n  settings->verbose       = 1;\n  settings->linsys_solver = MKL_PARDISO_SOLVER;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, data, settings);\n\n  // Setup correct\n  mu_assert(\"Basic QP 2 test solve: Setup error!\", exitflag == 0);\n\n  // Solve Problem first time\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Basic QP 2 test solve: Error in solver status!\",\n            work->info->status_val == sols_data->status_test);\n\n  // Compare primal solutions\n  mu_assert(\"Basic QP 2 test solve: Error in primal solution!\",\n            vec_norm_inf_diff(work->solution->x, sols_data->x_test,\n                              data->n) /\n            vec_norm_inf(sols_data->x_test_new, data->n) < TESTS_TOL);\n\n\n  // Compare dual solutions\n  mu_assert(\"Basic QP 2 test solve: Error in dual solution!\",\n            vec_norm_inf_diff(work->solution->y, sols_data->y_test,\n                              data->m) /\n            vec_norm_inf(sols_data->y_test_new, data->m) < TESTS_TOL);\n\n\n  // Compare objective values\n  mu_assert(\"Basic QP 2 test solve: Error in objective value!\",\n            c_absval(work->info->obj_val - sols_data->obj_value_test) <\n            TESTS_TOL);\n\n\n  // Clean workspace\n  osqp_cleanup(work);\n\n  // Cleanup settings and data\n  c_free(settings);\n  clean_problem_basic_qp2(data);\n  clean_problem_basic_qp2_sols_data(sols_data);\n\n  return 0;\n}\n#endif\n\nstatic const char* test_basic_qp2_update()\n{\n  c_int exitflag;\n\n  // Problem settings\n  OSQPSettings *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n\n  // Structures\n  OSQPWorkspace *work; // Workspace\n  OSQPData *data;      // Data\n  basic_qp2_sols_data *sols_data;\n\n\n  // Populate data\n  data = generate_problem_basic_qp2();\n  sols_data = generate_problem_basic_qp2_sols_data();\n\n\n  // Define Solver settings as default\n  osqp_set_default_settings(settings);\n  settings->alpha = 1.6;\n\n  settings->warm_start = 1;\n  settings->polish     = 1;\n  settings->verbose    = 1;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, data, settings);\n\n  // Setup correct\n  mu_assert(\"Basic QP 2 test update: Setup error!\", exitflag == 0);\n\n\n  // Modify linear cost and upper bound\n  osqp_update_lin_cost(work, sols_data->q_new);\n  osqp_update_upper_bound(work, sols_data->u_new);\n\n  // Solve Problem second time(with different data now)\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Basic QP 2 test update: Error in solver status!\",\n            work->info->status_val == sols_data->status_test_new);\n\n  // Compare primal solutions\n  mu_assert(\"Basic QP 2 test update: Error in primal solution!\",\n            vec_norm_inf_diff(work->solution->x, sols_data->x_test_new,\n                              data->n) /\n            vec_norm_inf(sols_data->x_test_new, data->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  mu_assert(\"Basic QP 2 test update: Error in dual solution!\",\n            vec_norm_inf_diff(work->solution->y, sols_data->y_test_new,\n                              data->m) /\n            vec_norm_inf(sols_data->y_test_new, data->m) < TESTS_TOL);\n\n\n  // Compare objective values\n  mu_assert(\"Basic QP 2 test update: Error in objective value!\",\n            c_absval(\n              work->info->obj_val - sols_data->obj_value_test_new)/(c_absval(sols_data->obj_value_test_new)) < TESTS_TOL);\n\n  // Clean workspace\n  osqp_cleanup(work);\n\n  // Cleanup settings and data\n  c_free(settings);\n  clean_problem_basic_qp2(data);\n  clean_problem_basic_qp2_sols_data(sols_data);\n\n  return 0;\n}\n\nstatic const char* test_basic_qp2()\n{\n  mu_run_test(test_basic_qp2_solve);\n#ifdef ENABLE_MKL_PARDISO\n  mu_run_test(test_basic_qp2_solve_pardiso);\n#endif\n  mu_run_test(test_basic_qp2_update);\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/tests/custom_memory/custom_memory.c",
    "content": "#  include \"custom_memory.h\"\n#  include <stdlib.h>\n#  include <stdio.h>\n\n//Make a global counter and track the net number of allocations\n//by user defined allocators.   Should go to zero on exit if no leaks.\nlong int alloc_counter = 0;\n\nvoid* my_malloc(size_t size) {\n  void *m = malloc(size);\n  alloc_counter++;\n  /* printf(\"OSQP allocator  (malloc): %zu bytes, %ld allocations \\n\",size, alloc_counter); */\n  return m;\n}\n\nvoid* my_calloc(size_t num, size_t size) {\n  void *m = calloc(num, size);\n  alloc_counter++;\n  /* printf(\"OSQP allocator  (calloc): %zu bytes, %ld allocations \\n\",num*size, alloc_counter); */\n  return m;\n}\n\nvoid* my_realloc(void *ptr, size_t size) {\n  void *m = realloc(ptr,size);\n  /* printf(\"OSQP allocator (realloc) : %zu bytes, %ld allocations \\n\",size, alloc_counter); */\n  return m;\n}\n\nvoid my_free(void *ptr) {\n  if(ptr != NULL){\n    free(ptr);\n    alloc_counter--;\n    /* printf(\"OSQP allocator   (free) : %ld allocations \\n\", alloc_counter); */\n  }\n}\n"
  },
  {
    "path": "osqp/tests/custom_memory/custom_memory.h",
    "content": "#ifndef OSQP_CUSTOM_MEMORY_H\n\n#define OSQP_CUSTOM_MEMORY_H\n\n# ifdef __cplusplus\nextern \"C\" {\n# endif /* ifdef __cplusplus */\n\n#  include <stdio.h> //for size_t\n\n#  define c_malloc  my_malloc\n#  define c_calloc  my_calloc\n#  define c_free    my_free\n#  define c_realloc my_realloc\n\n/* functions should have the same\nsignatures as the standard ones */\nvoid* my_malloc(size_t size);\nvoid* my_calloc(size_t num, size_t size);\nvoid* my_realloc(void *ptr, size_t size);\nvoid  my_free(void *ptr);\n\n# ifdef __cplusplus\n}\n# endif /* ifdef __cplusplus */\n\n#endif /* ifndef  OSQP_CUSTOM_MEMORY_H */\n"
  },
  {
    "path": "osqp/tests/demo/CMakeLists.txt",
    "content": "get_directory_property(headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION headers)\n                        \nset(headers ${headers}\n    ${CMAKE_CURRENT_SOURCE_DIR}/demo.h PARENT_SCOPE)\n\n"
  },
  {
    "path": "osqp/tests/demo/test_demo.h",
    "content": "#include \"osqp.h\"    // OSQP API\n#include \"minunit.h\" // Basic testing script header\n\nstatic const char* test_demo_solve()\n{\n  // Load problem data\n  c_float P_x[3] = { 4.0, 1.0, 2.0, };\n  c_int   P_nnz  = 3;\n  c_int   P_i[3] = { 0, 0, 1, };\n  c_int   P_p[3] = { 0, 1, 3, };\n  c_float q[2]   = { 1.0, 1.0, };\n  c_float A_x[4] = { 1.0, 1.0, 1.0, 1.0, };\n  c_int   A_nnz  = 4;\n  c_int   A_i[4] = { 0, 1, 0, 2, };\n  c_int   A_p[3] = { 0, 2, 4, };\n  c_float l[3]   = { 1.0, 0.0, 0.0, };\n  c_float u[3]   = { 1.0, 0.7, 0.7, };\n  c_int n = 2;\n  c_int m = 3;\n\n  c_int exitflag;\n\n  // Problem settings\n  OSQPSettings *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n\n  // Structures\n  OSQPWorkspace *work; // Workspace\n  OSQPData *data;      // OSQPData\n\n  // Populate data\n  data = (OSQPData *)c_malloc(sizeof(OSQPData));\n  data->n = n;\n  data->m = m;\n  data->P = csc_matrix(data->n, data->n, P_nnz, P_x, P_i, P_p);\n  data->q = q;\n  data->A = csc_matrix(data->m, data->n, A_nnz, A_x, A_i, A_p);\n  data->l = l;\n  data->u = u;\n\n  // Define solver settings as default\n  osqp_set_default_settings(settings);\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, data, settings);\n\n  // Setup correct\n  mu_assert(\"Demo test solve: Setup error!\", exitflag == 0);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Demo test solve: Error in solver status!\",\n\twork->info->status_val == OSQP_SOLVED);\n\n  // Clean workspace\n  osqp_cleanup(work);\n  c_free(data->A);\n  c_free(data->P);\n  c_free(data);\n  c_free(settings);\n\n  return 0;\n}\n\n\nstatic const char* test_demo()\n{\n  mu_run_test(test_demo_solve);\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/tests/generate_tests_data.py",
    "content": "# Code to generate the unittests for OSQP C code\n\nimport basic_qp.generate_problem\nimport basic_qp2.generate_problem\nimport lin_alg.generate_problem\nimport non_cvx.generate_problem\nimport primal_dual_infeasibility.generate_problem\nimport primal_infeasibility.generate_problem\nimport solve_linsys.generate_problem\nimport unconstrained.generate_problem\nimport update_matrices.generate_problem\n"
  },
  {
    "path": "osqp/tests/lin_alg/CMakeLists.txt",
    "content": "get_directory_property(headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION headers)\n\nset(headers ${headers}\n${CMAKE_CURRENT_SOURCE_DIR}/test_lin_alg.h PARENT_SCOPE)\n\nget_directory_property(codegen_headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION codegen_headers)\n\nset(codegen_headers ${codegen_headers}\n        ${CMAKE_CURRENT_SOURCE_DIR}/data.h PARENT_SCOPE)\n"
  },
  {
    "path": "osqp/tests/lin_alg/__init__.py",
    "content": ""
  },
  {
    "path": "osqp/tests/lin_alg/generate_problem.py",
    "content": "import numpy as np\nfrom scipy import sparse\nimport scipy.sparse.linalg as sla\nimport utils.codegen_utils as cu\n\n# Set numpy seed for reproducibility\nnp.random.seed(2)\n\n# Test sparse matrix construction vs dense\ntest_sp_matrix_Adns = np.around(.6*np.random.rand(5, 6)) + np.random.randn(5,6)\ntest_sp_matrix_A = sparse.csc_matrix(test_sp_matrix_Adns)\n\n\n# Test vector operations\ntest_vec_ops_n = 10\ntest_vec_ops_v1 = np.random.randn(test_vec_ops_n)\ntest_vec_ops_v2 = np.random.randn(test_vec_ops_n)\ntest_vec_ops_sc = np.random.randn()\ntest_vec_ops_norm_inf = np.linalg.norm(test_vec_ops_v1, np.inf)\ntest_vec_ops_norm_inf_diff = np.linalg.norm(test_vec_ops_v1 - test_vec_ops_v2,\n                                            np.inf)\ntest_vec_ops_add_scaled = test_vec_ops_v1 + test_vec_ops_sc * test_vec_ops_v2\ntest_vec_ops_ew_reciprocal = np.reciprocal(test_vec_ops_v1)\ntest_vec_ops_vec_prod = test_vec_ops_v1.dot(test_vec_ops_v2)\ntest_vec_ops_ew_max_vec = np.maximum(test_vec_ops_v1, test_vec_ops_v2)\ntest_vec_ops_ew_min_vec = np.minimum(test_vec_ops_v1, test_vec_ops_v2)\n\n\n# Test matrix operations\ntest_mat_ops_n = 2\ntest_mat_ops_A = sparse.random(test_mat_ops_n, test_mat_ops_n, density=0.8, format='csc')\ntest_mat_ops_d = np.random.randn(test_mat_ops_n)\nD = sparse.diags(test_mat_ops_d, format='csc')\ntest_mat_ops_prem_diag = D.dot(test_mat_ops_A).tocoo().tocsc()  # Force matrix reordering\ntest_mat_ops_postm_diag = test_mat_ops_A.dot(D).tocoo().tocsc()  # Force matrix reordering\ntest_mat_ops_inf_norm_cols = np.amax(np.abs(\n    np.asarray(test_mat_ops_A.todense())), axis=0)\ntest_mat_ops_inf_norm_rows = np.amax(np.abs(\n    np.asarray(test_mat_ops_A.todense())), axis=1)\n\n# Test matrix vector operations\nm = 5\nn = 4\np = 0.4\n\ntest_mat_vec_n = n\ntest_mat_vec_m = m\ntest_mat_vec_A = sparse.random(m, n, density=1.0, format='csc')\ntest_mat_vec_P = sparse.random(n, n, density=0.8, format='csc')\ntest_mat_vec_P = test_mat_vec_P + test_mat_vec_P.T\ntest_mat_vec_Pu = sparse.triu(test_mat_vec_P, format='csc')\ntest_mat_vec_x = np.random.randn(n)\ntest_mat_vec_y = np.random.randn(m)\ntest_mat_vec_Ax = test_mat_vec_A.dot(test_mat_vec_x)\ntest_mat_vec_Ax_cum = test_mat_vec_A.dot(test_mat_vec_x) + test_mat_vec_y\ntest_mat_vec_ATy = test_mat_vec_A.T.dot(test_mat_vec_y)\ntest_mat_vec_ATy_cum = test_mat_vec_A.T.dot(test_mat_vec_y) + test_mat_vec_x\ntest_mat_vec_Px = test_mat_vec_P.dot(test_mat_vec_x)\ntest_mat_vec_Px_cum = test_mat_vec_P.dot(test_mat_vec_x) + test_mat_vec_x\n\n\n# Test extract upper triangular\ntest_mat_extr_triu_n = 5\ntest_mat_extr_triu_P = sparse.random(test_mat_extr_triu_n, test_mat_extr_triu_n, density=0.8, format='csc')\ntest_mat_extr_triu_P = test_mat_extr_triu_P + test_mat_extr_triu_P.T\ntest_mat_extr_triu_Pu = sparse.triu(test_mat_extr_triu_P, format='csc')\ntest_mat_extr_triu_P_inf_norm_cols = np.amax(np.abs(\n    np.asarray(test_mat_extr_triu_P.todense())), axis=0)\n\n\n# Test compute quad form\ntest_qpform_n = 4\ntest_qpform_P = sparse.random(test_qpform_n, test_qpform_n, density=0.8, format='csc')\ntest_qpform_P = test_qpform_P + test_qpform_P.T\ntest_qpform_Pu = sparse.triu(test_qpform_P, format='csc')\ntest_qpform_x = np.random.randn(test_qpform_n)\ntest_qpform_value = .5 * test_qpform_x.T.dot(test_qpform_P.dot(test_qpform_x))\n\n\n# Generate test data and solutions\ndata = {'test_sp_matrix_A': test_sp_matrix_A,\n        'test_sp_matrix_Adns': test_sp_matrix_Adns,\n        'test_vec_ops_n': test_vec_ops_n,\n        'test_vec_ops_v1': test_vec_ops_v1,\n        'test_vec_ops_v2': test_vec_ops_v2,\n        'test_vec_ops_sc': test_vec_ops_sc,\n        'test_vec_ops_norm_inf': test_vec_ops_norm_inf,\n        'test_vec_ops_norm_inf_diff': test_vec_ops_norm_inf_diff,\n        'test_vec_ops_add_scaled': test_vec_ops_add_scaled,\n        'test_vec_ops_ew_reciprocal': test_vec_ops_ew_reciprocal,\n        'test_vec_ops_vec_prod': test_vec_ops_vec_prod,\n        'test_vec_ops_ew_max_vec': test_vec_ops_ew_max_vec,\n        'test_vec_ops_ew_min_vec': test_vec_ops_ew_min_vec,\n        'test_mat_ops_n': test_mat_ops_n,\n        'test_mat_ops_A': test_mat_ops_A,\n        'test_mat_ops_d': test_mat_ops_d,\n        'test_mat_ops_prem_diag': test_mat_ops_prem_diag,\n        'test_mat_ops_postm_diag': test_mat_ops_postm_diag,\n        'test_mat_ops_inf_norm_cols': test_mat_ops_inf_norm_cols,\n        'test_mat_ops_inf_norm_rows': test_mat_ops_inf_norm_rows,\n        'test_mat_vec_n': test_mat_vec_n,\n        'test_mat_vec_m': test_mat_vec_m,\n        'test_mat_vec_A': test_mat_vec_A,\n        'test_mat_vec_Pu': test_mat_vec_Pu,\n        'test_mat_vec_x': test_mat_vec_x,\n        'test_mat_vec_y': test_mat_vec_y,\n        'test_mat_vec_Ax': test_mat_vec_Ax,\n        'test_mat_vec_Ax_cum': test_mat_vec_Ax_cum,\n        'test_mat_vec_ATy': test_mat_vec_ATy,\n        'test_mat_vec_ATy_cum': test_mat_vec_ATy_cum,\n        'test_mat_vec_Px': test_mat_vec_Px,\n        'test_mat_vec_Px_cum': test_mat_vec_Px_cum,\n        'test_mat_extr_triu_n': test_mat_extr_triu_n,\n        'test_mat_extr_triu_P': test_mat_extr_triu_P,\n        'test_mat_extr_triu_Pu': test_mat_extr_triu_Pu,\n        'test_mat_extr_triu_P_inf_norm_cols':\n        test_mat_extr_triu_P_inf_norm_cols,\n        'test_qpform_n': test_qpform_n,\n        'test_qpform_Pu': test_qpform_Pu,\n        'test_qpform_x': test_qpform_x,\n        'test_qpform_value': test_qpform_value,\n        }\n\n# Generate test data\ncu.generate_data('lin_alg', data)\n"
  },
  {
    "path": "osqp/tests/lin_alg/test_lin_alg.h",
    "content": "#include <stdio.h>\n#include \"osqp.h\"\n#include \"minunit.h\"\n\n#include \"lin_alg/data.h\"\n\nstatic const char* test_constr_sparse_mat() {\n  c_float *Adns; // Conversion to dense matrix\n\n  lin_alg_sols_data *data = generate_problem_lin_alg_sols_data();\n\n  // Convert sparse to dense\n  Adns = csc_to_dns(data->test_sp_matrix_A);\n\n  // Compute norm of the elementwise difference with\n  mu_assert(\"Linear algebra tests: error in constructing sparse/dense matrix!\",\n            vec_norm_inf_diff(Adns, data->test_sp_matrix_Adns,\n                              data->test_sp_matrix_A->m *\n                              data->test_sp_matrix_A->n) < TESTS_TOL);\n\n  // Free memory\n  c_free(Adns); // because of vars from file matrices.h\n  clean_problem_lin_alg_sols_data(data);\n\n  return 0;\n}\n\nstatic const char* test_vec_operations() {\n  c_float  norm_inf, vecprod; // normInf;\n  c_float *ew_reciprocal;\n  c_float *add_scaled;\n  c_float *vec_ew_max_vec_test, *vec_ew_min_vec_test;\n\n  lin_alg_sols_data *data = generate_problem_lin_alg_sols_data();\n\n\n  // Add scaled\n  add_scaled = vec_copy(data->test_vec_ops_v1, data->test_vec_ops_n);\n  vec_add_scaled(add_scaled,\n                 add_scaled,\n                 data->test_vec_ops_v2,\n                 data->test_vec_ops_n,\n                 data->test_vec_ops_sc);\n  mu_assert(\n    \"Linear algebra tests: error in vector operation, adding scaled vector\",\n    vec_norm_inf_diff(add_scaled, data->test_vec_ops_add_scaled,\n                      data->test_vec_ops_n) < TESTS_TOL);\n\n  // Norm_inf of the difference\n  mu_assert(\n    \"Linear algebra tests: error in vector operation, norm_inf of difference\",\n    c_absval(vec_norm_inf_diff(data->test_vec_ops_v1,\n                               data->test_vec_ops_v2,\n                               data->test_vec_ops_n) -\n             data->test_vec_ops_norm_inf_diff) <\n    TESTS_TOL);\n\n  // norm_inf\n  norm_inf = vec_norm_inf(data->test_vec_ops_v1, data->test_vec_ops_n);\n  mu_assert(\"Linear algebra tests: error in vector operation, norm_inf\",\n            c_absval(norm_inf - data->test_vec_ops_norm_inf) < TESTS_TOL);\n\n  // Elementwise reciprocal\n  ew_reciprocal = (c_float *)c_malloc(data->test_vec_ops_n * sizeof(c_float));\n  vec_ew_recipr(data->test_vec_ops_v1, ew_reciprocal, data->test_vec_ops_n);\n  mu_assert(\n    \"Linear algebra tests: error in vector operation, elementwise reciprocal\",\n    vec_norm_inf_diff(ew_reciprocal, data->test_vec_ops_ew_reciprocal,\n                      data->test_vec_ops_n) < TESTS_TOL);\n\n\n  // Vector product\n  vecprod = vec_prod(data->test_vec_ops_v1,\n                     data->test_vec_ops_v2,\n                     data->test_vec_ops_n);\n  mu_assert(\"Linear algebra tests: error in vector operation, vector product\",\n            c_absval(vecprod - data->test_vec_ops_vec_prod) < TESTS_TOL);\n\n  // Elementwise maximum between two vectors\n  vec_ew_max_vec_test =\n    (c_float *)c_malloc(data->test_vec_ops_n * sizeof(c_float));\n  vec_ew_max_vec(data->test_vec_ops_v1,\n                 data->test_vec_ops_v2,\n                 vec_ew_max_vec_test,\n                 data->test_vec_ops_n);\n  mu_assert(\n    \"Linear algebra tests: error in vector operation, elementwise maximum between vectors\",\n    vec_norm_inf_diff(vec_ew_max_vec_test, data->test_vec_ops_ew_max_vec,\n                      data\n                      ->test_vec_ops_n) < TESTS_TOL);\n\n  // Elementwise minimum between two vectors\n  vec_ew_min_vec_test =\n    (c_float *)c_malloc(data->test_vec_ops_n * sizeof(c_float));\n  vec_ew_min_vec(data->test_vec_ops_v1,\n                 data->test_vec_ops_v2,\n                 vec_ew_min_vec_test,\n                 data->test_vec_ops_n);\n  mu_assert(\n    \"Linear algebra tests: error in vector operation, elementwise minimum between vectors\",\n    vec_norm_inf_diff(vec_ew_min_vec_test, data->test_vec_ops_ew_min_vec,\n                      data\n                      ->test_vec_ops_n) < TESTS_TOL);\n\n  // cleanup\n  c_free(add_scaled);\n  c_free(ew_reciprocal);\n  c_free(vec_ew_min_vec_test);\n  c_free(vec_ew_max_vec_test);\n  clean_problem_lin_alg_sols_data(data);\n\n  return 0;\n}\n\nstatic const char* test_mat_operations() {\n  csc *Ad, *dA; // Matrices used for tests\n  // csc *A_ewsq, *A_ewabs;     // Matrices used for tests\n  c_int exitflag = 0;\n\n  // c_float trace, fro_sq;\n  c_float *inf_norm_cols_rows_test;\n\n\n  lin_alg_sols_data *data = generate_problem_lin_alg_sols_data();\n\n\n  // Copy matrices\n  Ad = copy_csc_mat(data->test_mat_ops_A);\n  dA = copy_csc_mat(data->test_mat_ops_A);\n\n\n  // Premultiply matrix A\n  mat_premult_diag(dA, data->test_mat_ops_d);\n  mu_assert(\n    \"Linear algebra tests: error in matrix operation, premultiply diagonal\",\n    is_eq_csc(dA, data->test_mat_ops_prem_diag, TESTS_TOL));\n\n\n  // Postmultiply matrix A\n  mat_postmult_diag(Ad, data->test_mat_ops_d);\n  mu_assert(\n    \"Linear algebra tests: error in matrix operation, postmultiply diagonal\",\n    is_eq_csc(Ad, data->test_mat_ops_postm_diag, TESTS_TOL));\n\n  // Maximum norm over columns\n  inf_norm_cols_rows_test =\n    (c_float *)c_malloc(data->test_mat_ops_n * sizeof(c_float));\n  mat_inf_norm_cols(data->test_mat_ops_A, inf_norm_cols_rows_test);\n  mu_assert(\n    \"Linear algebra tests: error in matrix operation, max norm over columns\",\n    vec_norm_inf_diff(inf_norm_cols_rows_test, data->test_mat_ops_inf_norm_cols,\n                      data\n                      ->test_mat_ops_n) < TESTS_TOL);\n\n  // Maximum norm over rows\n  mat_inf_norm_rows(data->test_mat_ops_A, inf_norm_cols_rows_test);\n  mu_assert(\"Linear algebra tests: error in matrix operation, max norm over rows\",\n            vec_norm_inf_diff(inf_norm_cols_rows_test,\n                              data->test_mat_ops_inf_norm_rows,\n                              data\n                              ->test_mat_ops_n) < TESTS_TOL);\n\n\n  // cleanup\n  c_free(inf_norm_cols_rows_test);\n  csc_spfree(Ad);\n  csc_spfree(dA);\n  clean_problem_lin_alg_sols_data(data);\n\n  return 0;\n}\n\nstatic const char* test_mat_vec_multiplication() {\n  c_float *Ax, *ATy, *Px, *Ax_cum, *ATy_cum, *Px_cum;\n\n  lin_alg_sols_data *data = generate_problem_lin_alg_sols_data();\n\n\n  // Allocate vectors\n  Ax  = (c_float *)c_malloc(data->test_mat_vec_m * sizeof(c_float));\n  ATy = (c_float *)c_malloc(data->test_mat_vec_n * sizeof(c_float));\n  Px  = (c_float *)c_malloc(data->test_mat_vec_n * sizeof(c_float));\n\n\n  // Matrix-vector multiplication:  y = Ax\n  mat_vec(data->test_mat_vec_A, data->test_mat_vec_x, Ax, 0);\n  mu_assert(\n    \"Linear algebra tests: error in matrix-vector operation, matrix-vector multiplication\",\n    vec_norm_inf_diff(Ax, data->test_mat_vec_Ax,\n                      data->test_mat_vec_m) < TESTS_TOL);\n\n  // Cumulative matrix-vector multiplication:  y += Ax\n  Ax_cum = vec_copy(data->test_mat_vec_y, data->test_mat_vec_m);\n  mat_vec(data->test_mat_vec_A, data->test_mat_vec_x, Ax_cum, 1);\n  mu_assert(\n    \"Linear algebra tests: error in matrix-vector operation, cumulative matrix-vector multiplication\",\n    vec_norm_inf_diff(Ax_cum, data->test_mat_vec_Ax_cum,\n                      data->test_mat_vec_m) < TESTS_TOL);\n\n  // Matrix-transpose-vector multiplication:  x = A'*y\n  mat_tpose_vec(data->test_mat_vec_A, data->test_mat_vec_y, ATy, 0, 0);\n  mu_assert(\n    \"Linear algebra tests: error in matrix-vector operation, matrix-transpose-vector multiplication\",\n    vec_norm_inf_diff(ATy, data->test_mat_vec_ATy,\n                      data->test_mat_vec_n) < TESTS_TOL);\n\n  // Cumulative matrix-transpose-vector multiplication:  x += A'*y\n  ATy_cum = vec_copy(data->test_mat_vec_x, data->test_mat_vec_n);\n  mat_tpose_vec(data->test_mat_vec_A, data->test_mat_vec_y, ATy_cum, 1, 0);\n  mu_assert(\n    \"Linear algebra tests: error in matrix-vector operation, cumulative matrix-transpose-vector multiplication\",\n    vec_norm_inf_diff(ATy_cum, data->test_mat_vec_ATy_cum,\n                      data->test_mat_vec_n) < TESTS_TOL);\n\n  // Symmetric-matrix-vector multiplication (only upper part is stored)\n  mat_vec(data->test_mat_vec_Pu, data->test_mat_vec_x, Px, 0);          // upper\n                                                                        // traingular\n                                                                        // part\n  mat_tpose_vec(data->test_mat_vec_Pu, data->test_mat_vec_x, Px, 1, 1); // lower\n                                                                        // traingular\n                                                                        // part\n                                                                        // (without\n                                                                        // diagonal)\n  mu_assert(\n    \"Linear algebra tests: error in matrix-vector operation, symmetric matrix-vector multiplication\",\n    vec_norm_inf_diff(Px, data->test_mat_vec_Px,\n                      data->test_mat_vec_n) < TESTS_TOL);\n\n\n  // Cumulative symmetric-matrix-vector multiplication\n  Px_cum = vec_copy(data->test_mat_vec_x, data->test_mat_vec_n);\n  mat_vec(data->test_mat_vec_Pu, data->test_mat_vec_x, Px_cum, 1);          // upper\n                                                                            // traingular\n                                                                            // part\n  mat_tpose_vec(data->test_mat_vec_Pu, data->test_mat_vec_x, Px_cum, 1, 1); // lower\n                                                                            // traingular\n                                                                            // part\n                                                                            // (without\n                                                                            // diagonal)\n  mu_assert(\n    \"Linear algebra tests: error in matrix-vector operation, cumulative symmetric matrix-vector multiplication\",\n    vec_norm_inf_diff(Px_cum, data->test_mat_vec_Px_cum,\n                      data->test_mat_vec_n) < TESTS_TOL);\n\n\n  // cleanup\n  c_free(Ax);\n  c_free(ATy);\n  c_free(Px);\n  c_free(Ax_cum);\n  c_free(ATy_cum);\n  c_free(Px_cum);\n  clean_problem_lin_alg_sols_data(data);\n\n  return 0;\n}\n\nstatic const char* test_extract_upper_triangular() {\n  c_float *inf_norm_cols_test;\n  lin_alg_sols_data *data = generate_problem_lin_alg_sols_data();\n\n  // Extract upper triangular part\n  csc *Ptriu = csc_to_triu(data->test_mat_extr_triu_P);\n\n  mu_assert(\"Linear algebra tests: error in forming upper triangular matrix!\",\n            is_eq_csc(data->test_mat_extr_triu_Pu, Ptriu, TESTS_TOL));\n\n  // Compute infinity norm over columns of the original matrix by using the\n  // upper triangular part only\n  inf_norm_cols_test = (c_float *)c_malloc(data->test_mat_extr_triu_n\n                                           * sizeof(c_float));\n  mat_inf_norm_cols_sym_triu(Ptriu, inf_norm_cols_test);\n  mu_assert(\n    \"Linear algebra tests: error in forming upper triangular matrix, infinity norm over columns\",\n    vec_norm_inf_diff(inf_norm_cols_test,\n                      data->test_mat_extr_triu_P_inf_norm_cols,\n                      data->test_mat_extr_triu_n) < TESTS_TOL);\n\n  // Cleanup\n  c_free(inf_norm_cols_test);\n  csc_spfree(Ptriu);\n  clean_problem_lin_alg_sols_data(data);\n\n  return 0;\n}\n\nstatic const char* test_quad_form_upper_triang() {\n  c_float quad_form_t;\n\n  lin_alg_sols_data *data = generate_problem_lin_alg_sols_data();\n\n  // Compute quadratic form\n  quad_form_t = quad_form(data->test_qpform_Pu, data->test_qpform_x);\n\n  mu_assert(\n    \"Linear algebra tests: error in computing quadratic form using upper triangular matrix!\",\n    (c_absval(quad_form_t - data->test_qpform_value) < TESTS_TOL));\n\n  // cleanup\n  clean_problem_lin_alg_sols_data(data);\n\n  return 0;\n}\n\nstatic const char* test_lin_alg()\n{\n  mu_run_test(test_constr_sparse_mat);\n  mu_run_test(test_vec_operations);\n  mu_run_test(test_mat_operations);\n  mu_run_test(test_mat_vec_multiplication);\n  mu_run_test(test_extract_upper_triangular);\n  mu_run_test(test_quad_form_upper_triang);\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/tests/minunit.h",
    "content": "/* OSQP TESTER MODULE */\n\n/* THE CODE FOR MINIMAL UNIT TESTING HAS BEEN TAKEN FROM\n   http://www.jera.com/techinfo/jtns/jtn002.html */\n\n#define mu_assert(message, test) \\\n  do { if (!(test)) return message; } while (0)\n#define mu_run_test(test)                   \\\n  do { const char *message = test(); tests_run++; \\\n       if (message) return message; } while (0)\nextern int tests_run;\n\n#define TESTS_TOL 1e-4 // Define tests tolerance\n"
  },
  {
    "path": "osqp/tests/non_cvx/CMakeLists.txt",
    "content": "get_directory_property(headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION headers)\n\nset(headers ${headers}\n${CMAKE_CURRENT_SOURCE_DIR}/test_non_cvx.h PARENT_SCOPE)\n\nget_directory_property(codegen_headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION codegen_headers)\n\nset(codegen_headers ${codegen_headers}\n        ${CMAKE_CURRENT_SOURCE_DIR}/data.h PARENT_SCOPE)\n"
  },
  {
    "path": "osqp/tests/non_cvx/__init__.py",
    "content": ""
  },
  {
    "path": "osqp/tests/non_cvx/generate_problem.py",
    "content": "import numpy as np\nfrom scipy import sparse\nimport utils.codegen_utils as cu\n\nP = sparse.triu([[2., 5.], [5., 1.]], format='csc')\nq = np.array([3., 4.])\n\nA = sparse.csc_matrix([[-1., 0.], [0., -1.], [-1., 3.], [2., 5.], [3., 4]])\nl = -np.inf * np.ones(A.shape[0])\nu = np.array([0., 0., -15., 100., 80.])\n\nsols_data = {'sigma_new': 5}\n\n# Generate problem data\ncu.generate_problem_data(P, q, A, l, u, 'non_cvx', sols_data)\n"
  },
  {
    "path": "osqp/tests/non_cvx/test_non_cvx.h",
    "content": "#include \"osqp.h\"    // OSQP API\n#include \"minunit.h\" // Basic testing script header\n\n\n#include \"non_cvx/data.h\"\n\n\nstatic const char* test_non_cvx_solve()\n{\n  c_int exitflag;\n\n  // Problem settings\n  OSQPSettings *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n\n  // Structures\n  OSQPWorkspace *work; // Workspace\n  OSQPData *data;      // Data\n  non_cvx_sols_data *sols_data;\n\n\n  // Populate data\n  data = generate_problem_non_cvx();\n  sols_data = generate_problem_non_cvx_sols_data();\n\n\n  // Define Solver settings as default\n  osqp_set_default_settings(settings);\n  settings->verbose = 1;\n  settings->sigma = 1e-6;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, data, settings);\n\n  // Setup should fail due to (P + sigma I) having a negative eigenvalue\n  mu_assert(\"Non Convex test solve: Setup should have failed!\",\n            exitflag == OSQP_NONCVX_ERROR);\n\n  osqp_cleanup(work);\n\n  // Update Solver settings\n  settings->sigma = sols_data->sigma_new;\n\n  // Setup workspace again\n  exitflag = osqp_setup(&work, data, settings);\n\n  // Setup should work this time because (P + sigma I) is positive definite\n  mu_assert(\"Non Convex test solve: Setup error!\", exitflag == 0);\n\n  // Solve Problem first time\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Non Convex test solve: Error in solver status!\",\n            work->info->status_val == OSQP_NON_CVX);\n\n  // Compare objective values\n  mu_assert(\"Non Convex test solve: Error in objective value!\",\n            work->info->obj_val == OSQP_NAN);\n\n  // Clean workspace\n  osqp_cleanup(work);\n\n  // Cleanup settings and data\n  c_free(settings);\n  clean_problem_non_cvx(data);\n  clean_problem_non_cvx_sols_data(sols_data);\n\n  return 0;\n}\n\nstatic const char* test_non_cvx()\n{\n  mu_run_test(test_non_cvx_solve);\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/tests/osqp_tester.c",
    "content": "/* OSQP TESTER MODULE */\n\n/* THE CODE FOR MINIMAL UNIT TESTING HAS BEEN TAKEN FROM\n   http://www.jera.com/techinfo/jtns/jtn002.html */\n\n#include <stdio.h>\n\n#include \"minunit.h\"\n#include \"osqp.h\"\n#include \"osqp_tester.h\"\n\n// Include tests\n#include \"lin_alg/test_lin_alg.h\"\n#include \"solve_linsys/test_solve_linsys.h\"\n#include \"demo/test_demo.h\"\n#include \"basic_qp/test_basic_qp.h\"\n#include \"basic_qp2/test_basic_qp2.h\"\n#include \"non_cvx/test_non_cvx.h\"\n#include \"primal_dual_infeasibility/test_primal_dual_infeasibility.h\"\n#include \"primal_infeasibility/test_primal_infeasibility.h\"\n#include \"unconstrained/test_unconstrained.h\"\n#include \"update_matrices/test_update_matrices.h\"\n\n\nint tests_run = 0;\n\n\nstatic const char* all_tests() {\n  mu_run_test(test_lin_alg);\n  mu_run_test(test_solve_linsys);\n  mu_run_test(test_demo);\n  mu_run_test(test_basic_qp);\n  mu_run_test(test_basic_qp2);\n  mu_run_test(test_non_cvx);\n  mu_run_test(test_primal_infeasibility);\n  mu_run_test(test_primal_dual_infeasibility);\n  mu_run_test(test_unconstrained);\n  mu_run_test(test_update_matrices);\n  return 0;\n}\n\nint main(void) {\n  const char *result = all_tests();\n\n  if (result != 0) {\n    printf(\"%s\\n\", result);\n  }\n  else {\n    printf(\"ALL TESTS PASSED\\n\");\n  }\n  printf(\"Tests run: %d\\n\", tests_run);\n\n  return result != 0;\n}\n"
  },
  {
    "path": "osqp/tests/osqp_tester.h",
    "content": "// Utilities for testing\n\n#ifndef EMBEDDED\n\nc_float* csc_to_dns(csc *M)\n{\n  c_int i, j = 0; // Predefine row index and column index\n  c_int idx;\n\n  // Initialize matrix of zeros\n  c_float *A = (c_float *)c_calloc(M->m * M->n, sizeof(c_float));\n\n  // Allocate elements\n  for (idx = 0; idx < M->p[M->n]; idx++)\n  {\n    // Get row index i (starting from 1)\n    i = M->i[idx];\n\n    // Get column index j (increase if necessary) (starting from 1)\n    while (M->p[j + 1] <= idx) {\n      j++;\n    }\n\n    // Assign values to A\n    A[j * (M->m) + i] = M->x[idx];\n  }\n  return A;\n}\n\nc_int is_eq_csc(csc *A, csc *B, c_float tol) {\n  c_int j, i;\n\n  // If number of columns does not coincide, they are not equal.\n  if (A->n != B->n) return 0;\n\n  for (j = 0; j < A->n; j++) { // Cycle over columns j\n    // if column pointer does not coincide, they are not equal\n    if (A->p[j] != B->p[j]) return 0;\n\n    for (i = A->p[j]; i < A->p[j + 1]; i++) { // Cycle rows i in column j\n      if ((A->i[i] != B->i[i]) ||             // Different row indices\n          (c_absval(A->x[i] - B->x[i]) > tol)) {\n        return 0;\n      }\n    }\n  }\n  return 1;\n}\n\n#endif // #ifndef EMBEDDED\n\n"
  },
  {
    "path": "osqp/tests/primal_dual_infeasibility/CMakeLists.txt",
    "content": "get_directory_property(headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION headers)\n\nset(headers ${headers}\n    ${CMAKE_CURRENT_SOURCE_DIR}/test_primal_dual_infeasibility.h PARENT_SCOPE)\n\nget_directory_property(codegen_headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION codegen_headers)\n\nset(codegen_headers ${codegen_headers}\n        ${CMAKE_CURRENT_SOURCE_DIR}/data.h PARENT_SCOPE)\n"
  },
  {
    "path": "osqp/tests/primal_dual_infeasibility/__init__.py",
    "content": ""
  },
  {
    "path": "osqp/tests/primal_dual_infeasibility/generate_problem.py",
    "content": "import numpy as np\nfrom scipy import sparse\nimport utils.codegen_utils as cu\n\nP = sparse.diags([1., 0.], format='csc')\nq = np.array([1., -1.])\n\nA12 = sparse.csc_matrix([[1., 1.], [1., 0.], [0., 1.]])\nA34 = sparse.csc_matrix([[1., 0.], [1., 0.], [0., 1.]])\nl = np.array([0., 1., 1.])\nu1 = np.array([5., 3., 3.])\nu2 = np.array([0., 3., 3.])\nu3 = np.array([2., 3., np.inf])\nu4 = np.array([0., 3., np.inf])\n\n# Generate problem solutions\ndata = {'P': P,\n        'q': q,\n        'A12': A12,\n        'A34': A34,\n        'l': l,\n        'u1': u1,\n        'u2': u2,\n        'u3': u3,\n        'u4': u4,\n        'x1': np.array([1., 3.]),\n        'y1': np.array([0., -2., 1.]),\n        'obj_value1': -1.5,\n        'status1': 'optimal',\n        'status2': 'primal_infeasible',\n        'status3': 'dual_infeasible',\n        'status4': 'primal_infeasible'\n        }\n\n# Generate problem data\ncu.generate_data('primal_dual_infeasibility', data)\n"
  },
  {
    "path": "osqp/tests/primal_dual_infeasibility/test_primal_dual_infeasibility.h",
    "content": "#include \"osqp.h\"\n#include \"cs.h\"\n#include \"util.h\"\n#include \"minunit.h\"\n\n#include \"primal_dual_infeasibility/data.h\"\n\n\nstatic const char* test_optimal()\n{\n  c_int exitflag;\n\n  // Structures\n  OSQPWorkspace *work;    // Workspace\n  OSQPData *problem;      // Problem data\n  OSQPSettings *settings; // Settings\n  primal_dual_infeasibility_sols_data *data;\n\n  // Load problem data\n  data = generate_problem_primal_dual_infeasibility_sols_data();\n\n  // Populate problem data\n  problem    = (OSQPData*) c_malloc(sizeof(OSQPData));\n  problem->P = data->P;\n  problem->q = data->q;\n  problem->A = data->A12;\n  problem->l = data->l;\n  problem->u = data->u1;\n  problem->n = data->P->n;\n  problem->m = data->A12->m;\n\n  // Define Solver settings as default\n  settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n  osqp_set_default_settings(settings);\n  settings->max_iter = 2000;\n  settings->alpha    = 1.6;\n  settings->polish   = 1;\n  settings->scaling  = 0;\n  settings->verbose  = 1;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, problem, settings);\n\n  // Setup correct\n  mu_assert(\"Primal dual infeasibility test 1: Setup error!\", exitflag == 0);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Primal dual infeasibility test 1: Error in solver status!\",\n            work->info->status_val == OSQP_SOLVED);\n\n  // Compare primal solutions\n  mu_assert(\"Primal dual infeasibility test 1: Error in primal solution!\",\n            vec_norm_inf_diff(work->solution->x, data->x1,\n                              problem->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  mu_assert(\"Primal dual infeasibility test 1: Error in dual solution!\",\n            vec_norm_inf_diff(work->solution->y, data->y1,\n                              problem->m) < TESTS_TOL);\n\n\n  // Compare objective values\n  mu_assert(\"Primal dual infeasibility test 1: Error in objective value!\",\n            c_absval(work->info->obj_val - data->obj_value1) < TESTS_TOL);\n\n\n  // Cleanup\n  osqp_cleanup(work);\n  clean_problem_primal_dual_infeasibility_sols_data(data);\n  c_free(problem);\n  c_free(settings);\n\n  return 0;\n}\n\nstatic const char* test_prim_infeas()\n{\n  c_int exitflag;\n\n  // Structures\n  OSQPWorkspace *work;    // Workspace\n  OSQPData *problem;      // Problem data\n  OSQPSettings *settings; // Settings\n  primal_dual_infeasibility_sols_data *data;\n\n  // Load problem data\n  data = generate_problem_primal_dual_infeasibility_sols_data();\n\n  // Populate problem data\n  problem    = (OSQPData*) c_malloc(sizeof(OSQPData));\n  problem->P = data->P;\n  problem->q = data->q;\n  problem->A = data->A12;\n  problem->l = data->l;\n  problem->u = data->u2;\n  problem->n = data->P->n;\n  problem->m = data->A12->m;\n\n  // Define Solver settings as default\n  settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n  osqp_set_default_settings(settings);\n  settings->max_iter = 2000;\n  settings->alpha    = 1.6;\n  settings->polish   = 0;\n  settings->scaling  = 0;\n  settings->verbose  = 1;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, problem, settings);\n\n  // Setup correct\n  mu_assert(\"Primal dual infeasibility test 2: Setup error!\", exitflag == 0);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Primal dual infeasibility test 2: Error in solver status!\",\n            work->info->status_val == OSQP_PRIMAL_INFEASIBLE);\n\n  // Cleanup\n  osqp_cleanup(work);\n  clean_problem_primal_dual_infeasibility_sols_data(data);\n  c_free(problem);\n  c_free(settings);\n\n  return 0;\n}\n\nstatic const char* test_dual_infeas()\n{\n  c_int exitflag;\n\n  // Structures\n  OSQPWorkspace *work;    // Workspace\n  OSQPData *problem;      // Problem data\n  OSQPSettings *settings; // Settings\n  primal_dual_infeasibility_sols_data *data;\n\n  // Load problem data\n  data = generate_problem_primal_dual_infeasibility_sols_data();\n\n  // Populate problem data\n  problem    = (OSQPData*) c_malloc(sizeof(OSQPData));\n  problem->P = data->P;\n  problem->q = data->q;\n  problem->A = data->A34;\n  problem->l = data->l;\n  problem->u = data->u3;\n  problem->n = data->P->n;\n  problem->m = data->A34->m;\n\n  // Define Solver settings as default\n  settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n  osqp_set_default_settings(settings);\n  settings->max_iter = 2000;\n  settings->alpha    = 1.6;\n  settings->polish   = 0;\n  settings->scaling  = 0;\n  settings->verbose  = 1;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, problem, settings);\n\n  // Setup correct\n  mu_assert(\"Primal dual infeasibility test 3: Setup error!\", exitflag == 0);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Primal dual infeasibility test 3: Error in solver status!\",\n            work->info->status_val == OSQP_DUAL_INFEASIBLE);\n\n  // Cleanup\n  osqp_cleanup(work);\n  clean_problem_primal_dual_infeasibility_sols_data(data);\n  c_free(problem);\n  c_free(settings);\n\n  return 0;\n}\n\nstatic const char* test_primal_dual_infeas()\n{\n  c_int exitflag;\n\n  // Structures\n  OSQPWorkspace *work;    // Workspace\n  OSQPData *problem;      // Problem data\n  OSQPSettings *settings; // Settings\n  primal_dual_infeasibility_sols_data *data;\n\n  // Load problem data\n  data = generate_problem_primal_dual_infeasibility_sols_data();\n\n  // Populate problem data\n  problem    = (OSQPData*) c_malloc(sizeof(OSQPData));\n  problem->P = data->P;\n  problem->q = data->q;\n  problem->A = data->A34;\n  problem->l = data->l;\n  problem->u = data->u4;\n  problem->n = data->P->n;\n  problem->m = data->A34->m;\n\n  // Define Solver settings as default\n  settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n  osqp_set_default_settings(settings);\n  settings->max_iter = 2000;\n  settings->alpha    = 1.6;\n  settings->polish   = 0;\n  settings->scaling  = 0;\n  settings->verbose  = 1;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, problem, settings);\n\n  // Setup correct\n  mu_assert(\"Primal dual infeasibility test 4: Setup error!\", exitflag == 0);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Primal dual infeasibility test 4: Error in solver status!\",\n            ((work->info->status_val == OSQP_PRIMAL_INFEASIBLE) ||\n             (work->info->status_val == OSQP_DUAL_INFEASIBLE)));\n\n  // Cleanup\n  osqp_cleanup(work);\n  clean_problem_primal_dual_infeasibility_sols_data(data);\n  c_free(problem);\n  c_free(settings);\n\n  return 0;\n}\n\nstatic const char* test_primal_dual_infeasibility()\n{\n  mu_run_test(test_optimal);\n  mu_run_test(test_prim_infeas);\n  mu_run_test(test_dual_infeas);\n  mu_run_test(test_primal_dual_infeas);\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/tests/primal_infeasibility/CMakeLists.txt",
    "content": "get_directory_property(headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION headers)\n\nset(headers ${headers}\n${CMAKE_CURRENT_SOURCE_DIR}/test_primal_infeasibility.h PARENT_SCOPE)\n\nget_directory_property(codegen_headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION codegen_headers)\n\nset(codegen_headers ${codegen_headers}\n        ${CMAKE_CURRENT_SOURCE_DIR}/data.h PARENT_SCOPE)\n"
  },
  {
    "path": "osqp/tests/primal_infeasibility/__init__.py",
    "content": ""
  },
  {
    "path": "osqp/tests/primal_infeasibility/generate_problem.py",
    "content": "import numpy as np\nfrom scipy import sparse\nimport scipy as sp\nimport utils.codegen_utils as cu\n\n# Set numpy seed for reproducibility\nnp.random.seed(2)\n\nn = 50\nm = 150\n\n# Generate random Matrices\nPt = sparse.random(n, n)\nP = Pt.T.dot(Pt) + sparse.eye(n)\nP = sparse.triu(P, format='csc')\nq = sp.randn(n)\nA = sparse.random(m, n).tolil()  # Lil for efficiency\nu = 3 + sp.randn(m)\nl = -3 + sp.randn(m)\n\n# Make random problem primal infeasible\nA[int(n/2), :] = A[int(n/2)+1, :]\nl[int(n/2)] = u[int(n/2)+1] + 10 * sp.rand()\nu[int(n/2)] = l[int(n/2)] + 0.5\n\n# Convert A to csc\nA = A.tocsc()\n\n# Generate problem solutions\nsols_data = {'status_test': 'primal_infeasible'}\n\n\n# Generate problem data\ncu.generate_problem_data(P, q, A, l, u, 'primal_infeasibility', sols_data)\n"
  },
  {
    "path": "osqp/tests/primal_infeasibility/test_primal_infeasibility.h",
    "content": "#include \"osqp.h\"    // OSQP API\n#include \"cs.h\"      // CSC data structure\n#include \"util.h\"    // Utilities for testing\n#include \"minunit.h\" // Basic testing script header\n\n#include \"primal_infeasibility/data.h\"\n\n\nstatic const char* test_primal_infeasible_qp_solve()\n{\n  c_int exitflag;\n\n  // Problem settings\n  OSQPSettings *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n\n  // Structures\n  OSQPWorkspace *work; // Workspace\n  OSQPData *data;      // Data\n  primal_infeasibility_sols_data *sols_data;\n\n  // Populate data\n  data      = generate_problem_primal_infeasibility();\n  sols_data = generate_problem_primal_infeasibility_sols_data();\n\n\n  // Define Solver settings as default\n  osqp_set_default_settings(settings);\n  settings->max_iter   = 10000;\n  settings->alpha      = 1.6;\n  settings->polish     = 1;\n  settings->scaling    = 0;\n  settings->verbose    = 1;\n  settings->warm_start = 0;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, data, settings);\n\n  // Setup correct\n  mu_assert(\"Primal infeasible QP test solve: Setup error!\", exitflag == 0);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Primal infeasible QP test solve: Error in solver status!\",\n            work->info->status_val == sols_data->status_test);\n\n\n  // Clean workspace\n  osqp_cleanup(work);\n\n\n  // Cleanup data\n  clean_problem_primal_infeasibility(data);\n  clean_problem_primal_infeasibility_sols_data(sols_data);\n\n  // Cleanup\n  c_free(settings);\n\n  return 0;\n}\n\nstatic const char* test_primal_infeasibility()\n{\n  mu_run_test(test_primal_infeasible_qp_solve);\n\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/tests/solve_linsys/CMakeLists.txt",
    "content": "get_directory_property(headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION headers)\n\nset(headers ${headers}\n${CMAKE_CURRENT_SOURCE_DIR}/test_solve_linsys.h PARENT_SCOPE)\n\nget_directory_property(codegen_headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION codegen_headers)\n\nset(codegen_headers ${codegen_headers}\n        ${CMAKE_CURRENT_SOURCE_DIR}/data.h PARENT_SCOPE)\n"
  },
  {
    "path": "osqp/tests/solve_linsys/__init__.py",
    "content": ""
  },
  {
    "path": "osqp/tests/solve_linsys/generate_problem.py",
    "content": "import numpy as np\nfrom scipy import sparse\nimport scipy.sparse.linalg as spla\nimport utils.codegen_utils as cu\n\n# Set numpy seed for reproducibility\nnp.random.seed(2)\n\n# Simple case\ntest_solve_KKT_n = 3\ntest_solve_KKT_m = 4\n\ntest_solve_KKT_P = sparse.random(test_solve_KKT_n, test_solve_KKT_n,\n                                 density=0.4, format='csc')\ntest_solve_KKT_P = test_solve_KKT_P.dot(test_solve_KKT_P.T).tocsc()\ntest_solve_KKT_A = sparse.random(test_solve_KKT_m, test_solve_KKT_n,\n                                 density=0.4, format='csc')\ntest_solve_KKT_Pu = sparse.triu(test_solve_KKT_P, format='csc')\n\ntest_solve_KKT_rho = 4.0\ntest_solve_KKT_sigma = 1.0\ntest_solve_KKT_KKT = sparse.vstack([\n                        sparse.hstack([test_solve_KKT_P + test_solve_KKT_sigma *\n                        sparse.eye(test_solve_KKT_n), test_solve_KKT_A.T]),\n                        sparse.hstack([test_solve_KKT_A,\n                        -1./test_solve_KKT_rho * sparse.eye(test_solve_KKT_m)])\n                        ], format='csc')\ntest_solve_KKT_rhs = np.random.randn(test_solve_KKT_m + test_solve_KKT_n)\ntest_solve_KKT_x = spla.splu(test_solve_KKT_KKT).solve(test_solve_KKT_rhs)\n\ntest_solve_KKT_x[test_solve_KKT_n:] = test_solve_KKT_rhs[test_solve_KKT_n:] + \\\n                                      test_solve_KKT_x[test_solve_KKT_n:] / test_solve_KKT_rho\n\n# Generate test data and solutions\ndata = {'test_solve_KKT_n': test_solve_KKT_n,\n        'test_solve_KKT_m': test_solve_KKT_m,\n        'test_solve_KKT_A': test_solve_KKT_A,\n        'test_solve_KKT_Pu': test_solve_KKT_Pu,\n        'test_solve_KKT_rho': test_solve_KKT_rho,\n        'test_solve_KKT_sigma': test_solve_KKT_sigma,\n        'test_solve_KKT_KKT': test_solve_KKT_KKT,\n        'test_solve_KKT_rhs': test_solve_KKT_rhs,\n        'test_solve_KKT_x': test_solve_KKT_x\n        }\n\n# Generate test data\ncu.generate_data('solve_linsys', data)\n"
  },
  {
    "path": "osqp/tests/solve_linsys/test_solve_linsys.h",
    "content": "#include <stdio.h>\n#include \"osqp.h\"\n#include \"cs.h\"\n#include \"util.h\"\n#include \"minunit.h\"\n#include \"lin_sys.h\"\n\n\n#include \"solve_linsys/data.h\"\n\n\nstatic const char* test_solveKKT() {\n  c_int m, exitflag = 0;\n  c_float *rho_vec;\n  LinSysSolver *s;  // Private structure to form KKT factorization\n  OSQPSettings *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings)); // Settings\n  solve_linsys_sols_data *data = generate_problem_solve_linsys_sols_data();\n\n  // Settings\n  settings->rho   = data->test_solve_KKT_rho;\n  settings->sigma = data->test_solve_KKT_sigma;\n\n  // Set rho_vec\n  m       = data->test_solve_KKT_A->m;\n  rho_vec = (c_float*) c_calloc(m, sizeof(c_float));\n  vec_add_scalar(rho_vec, settings->rho, m);\n\n  // Form and factorize KKT matrix\n  exitflag = init_linsys_solver(&s, data->test_solve_KKT_Pu, data->test_solve_KKT_A,\n                                settings->sigma, rho_vec, LINSYS_SOLVER, 0);\n\n  // Solve  KKT x = b via LDL given factorization\n  s->solve(s, data->test_solve_KKT_rhs);\n\n  mu_assert(\n    \"Linear systems solve tests: error in forming and solving KKT system!\",\n    vec_norm_inf_diff(data->test_solve_KKT_rhs, data->test_solve_KKT_x,\n                      data->test_solve_KKT_m + data->test_solve_KKT_n) < TESTS_TOL);\n\n\n  // Cleanup\n  s->free(s);\n  c_free(settings);\n  c_free(rho_vec);\n  clean_problem_solve_linsys_sols_data(data);\n\n  return 0;\n}\n\n#ifdef ENABLE_MKL_PARDISO\nstatic char* test_solveKKT_pardiso() {\n  c_int m, exitflag = 0;\n  c_float *rho_vec;\n  LinSysSolver *s;  // Private  structure  to  form  KKT  factorization\n  OSQPSettings *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings)); // Settings\n\n  solve_linsys_sols_data *data = generate_problem_solve_linsys_sols_data();\n\n  // Settings\n  settings->rho   = data->test_solve_KKT_rho;\n  settings->sigma = data->test_solve_KKT_sigma;\n\n  // Set rho_vec\n  m = data->test_solve_KKT_A->m;\n  rho_vec = c_calloc(m, sizeof(c_float));\n  vec_add_scalar(rho_vec, settings->rho, m);\n\n  // Load Pardiso shared library\n  exitflag = load_linsys_solver(MKL_PARDISO_SOLVER);\n  mu_assert(\"Linear system solve test: error in loading Pardiso shared library\",\n            exitflag == 0);\n\n  // Form and factorize KKT matrix\n  exitflag = init_linsys_solver(&s, data->test_solve_KKT_Pu, data->test_solve_KKT_A,\n                                settings->sigma, rho_vec, MKL_PARDISO_SOLVER, 0);\n\n  // Solve  KKT x = b via LDL given factorization\n  s->solve(s, data->test_solve_KKT_rhs);\n\n  mu_assert(\n    \"Linear systems solve tests: error in forming and solving KKT system with PARDISO!\",\n    vec_norm_inf_diff(data->test_solve_KKT_rhs, data->test_solve_KKT_x,\n                      data->test_solve_KKT_m + data->test_solve_KKT_n) < TESTS_TOL);\n\n\n  // Cleanup\n  s->free(s);\n  c_free(settings);\n  c_free(rho_vec);\n  clean_problem_solve_linsys_sols_data(data);\n\n  // Unload Pardiso shared library\n  exitflag = unload_linsys_solver(MKL_PARDISO_SOLVER);\n\n  return 0;\n}\n#endif\n\nstatic const char* test_solve_linsys()\n{\n  mu_run_test(test_solveKKT);\n#ifdef ENABLE_MKL_PARDISO\n  mu_run_test(test_solveKKT_pardiso);\n#endif\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/tests/unconstrained/CMakeLists.txt",
    "content": "get_directory_property(headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION headers)\n\nset(headers ${headers}\n${CMAKE_CURRENT_SOURCE_DIR}/test_unconstrained.h PARENT_SCOPE)\n\nget_directory_property(codegen_headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION codegen_headers)\n\nset(codegen_headers ${codegen_headers}\n        ${CMAKE_CURRENT_SOURCE_DIR}/data.h PARENT_SCOPE)\n"
  },
  {
    "path": "osqp/tests/unconstrained/__init__.py",
    "content": ""
  },
  {
    "path": "osqp/tests/unconstrained/generate_problem.py",
    "content": "import numpy as np\nfrom scipy import sparse\nimport utils.codegen_utils as cu\n\nP = sparse.diags([0.617022, 0.92032449, 0.20011437, 0.50233257, 0.34675589], format='csc')\nq = np.array([-1.10593508, -1.65451545, -2.3634686, 1.13534535, -1.01701414])\nA = sparse.csc_matrix((0,5))\nl = np.array([])\nu = np.array([])\n\n# Generate problem solutions\nsols_data = {'x_test': np.array([1.79237542, 1.79775228, 11.81058885, -2.26014678, 2.93293975]),\n             'obj_value_test': -19.209752026813277,\n             'status_test': 'optimal'}\n\n# Generate problem data\ncu.generate_problem_data(P, q, A, l, u, 'unconstrained', sols_data)"
  },
  {
    "path": "osqp/tests/unconstrained/test_unconstrained.h",
    "content": "#include \"osqp.h\"    // OSQP API\n#include \"minunit.h\" // Basic testing script header\n\n\n#include \"unconstrained/data.h\"\n\n\nstatic const char* test_unconstrained_solve()\n{\n  c_int exitflag;\n\n  // Problem settings\n  OSQPSettings *settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n\n  // Structures\n  OSQPWorkspace *work; // Workspace\n  OSQPData *data;      // Data\n  unconstrained_sols_data *sols_data;\n\n\n  // Populate data\n  data = generate_problem_unconstrained();\n  sols_data = generate_problem_unconstrained_sols_data();\n\n\n  // Define Solver settings as default\n  osqp_set_default_settings(settings);\n  settings->verbose = 1;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, data, settings);\n\n  // Setup correct\n  mu_assert(\"Unconstrained test solve: Setup error!\", exitflag == 0);\n\n  // Solve Problem first time\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Unconstrained test solve: Error in solver status!\",\n            work->info->status_val == sols_data->status_test);\n\n  // Compare primal solutions\n  mu_assert(\"Unconstrained test solve: Error in primal solution!\",\n            vec_norm_inf_diff(work->solution->x, sols_data->x_test,\n                              data->n) < TESTS_TOL);\n\n  // Compare objective values\n  mu_assert(\"Unconstrained test solve: Error in objective value!\",\n            c_absval(work->info->obj_val - sols_data->obj_value_test) <\n            TESTS_TOL);\n\n  // Clean workspace\n  osqp_cleanup(work);\n\n  // Cleanup settings and data\n  c_free(settings);\n  clean_problem_unconstrained(data);\n  clean_problem_unconstrained_sols_data(sols_data);\n\n  return 0;\n}\n\nstatic const char* test_unconstrained()\n{\n  mu_run_test(test_unconstrained_solve);\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/tests/update_matrices/CMakeLists.txt",
    "content": "get_directory_property(headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION headers)\n\nset(headers ${headers}\n${CMAKE_CURRENT_SOURCE_DIR}/test_update_matrices.h PARENT_SCOPE)\n\nget_directory_property(codegen_headers\n                        DIRECTORY ${PROJECT_SOURCE_DIR}/tests\n                        DEFINITION codegen_headers)\n\nset(codegen_headers ${codegen_headers}\n        ${CMAKE_CURRENT_SOURCE_DIR}/data.h PARENT_SCOPE)\n"
  },
  {
    "path": "osqp/tests/update_matrices/__init__.py",
    "content": ""
  },
  {
    "path": "osqp/tests/update_matrices/generate_problem.py",
    "content": "import numpy as np\nfrom scipy import sparse\nimport utils.codegen_utils as cu\n\n# Set numpy seed for reproducibility\nnp.random.seed(2)\n\n\n# Define tests\nn = 5\nm = 8\ntest_form_KKT_n = n\ntest_form_KKT_m = m\np = 0.7\n\ntest_form_KKT_A = sparse.random(test_form_KKT_m, test_form_KKT_n, density=p, format='csc')\ntest_form_KKT_P = sparse.random(n, n, density=p)\ntest_form_KKT_P = test_form_KKT_P.dot(test_form_KKT_P.T).tocsc() + sparse.eye(n, format='csc')\ntest_form_KKT_Pu = sparse.triu(test_form_KKT_P, format='csc')\ntest_form_KKT_rho = 1.6\ntest_form_KKT_sigma = 0.1\ntest_form_KKT_KKT = sparse.vstack([\n                        sparse.hstack([test_form_KKT_P + test_form_KKT_sigma *\n                        sparse.eye(test_form_KKT_n), test_form_KKT_A.T]),\n                        sparse.hstack([test_form_KKT_A,\n                        -1./test_form_KKT_rho * sparse.eye(test_form_KKT_m)])\n                        ], format='csc')\ntest_form_KKT_KKTu = sparse.triu(test_form_KKT_KKT, format='csc')\n\n\n# Create new P, A and KKT\ntest_form_KKT_A_new = test_form_KKT_A.copy()\ntest_form_KKT_A_new.data += np.random.randn(test_form_KKT_A_new.nnz)\ntest_form_KKT_Pu_new = test_form_KKT_Pu.copy()\ntest_form_KKT_Pu_new.data += 0.1 * np.random.randn(test_form_KKT_Pu_new.nnz)\ntest_form_KKT_P_new = test_form_KKT_Pu_new + test_form_KKT_Pu_new.T - sparse.diags(test_form_KKT_Pu_new.diagonal())\n\ntest_form_KKT_KKT_new = sparse.vstack([\n                        sparse.hstack([test_form_KKT_P_new + test_form_KKT_sigma *\n                        sparse.eye(test_form_KKT_n), test_form_KKT_A_new.T]),\n                        sparse.hstack([test_form_KKT_A_new,\n                        -1./test_form_KKT_rho * sparse.eye(test_form_KKT_m)])\n                        ], format='csc')\ntest_form_KKT_KKTu_new = sparse.triu(test_form_KKT_KKT_new, format='csc')\n\n\n# Test solve problem with initial P and A\ntest_solve_P = test_form_KKT_P.copy()\ntest_solve_Pu = test_form_KKT_Pu.copy()\ntest_solve_q = np.random.randn(n)\ntest_solve_A = test_form_KKT_A.copy()\ntest_solve_l = -30 + np.random.randn(m)\ntest_solve_u = 30 + np.random.randn(m)\n\n\n# Define new P\ntest_solve_P_new = test_form_KKT_P_new.copy()\ntest_solve_Pu_new = test_form_KKT_Pu_new.copy()\n\n\n# Define new A\ntest_solve_A_new = test_form_KKT_A_new.copy()\n\n\n# Generate test data and solutions\ndata = {'test_form_KKT_n': test_form_KKT_n,\n        'test_form_KKT_m': test_form_KKT_m,\n        'test_form_KKT_A': test_form_KKT_A,\n        'test_form_KKT_Pu': test_form_KKT_Pu,\n        'test_form_KKT_rho': test_form_KKT_rho,\n        'test_form_KKT_sigma': test_form_KKT_sigma,\n        'test_form_KKT_KKT': test_form_KKT_KKT,\n        'test_form_KKT_KKTu': test_form_KKT_KKTu,\n        'test_form_KKT_A_new': test_form_KKT_A_new,\n        'test_form_KKT_Pu_new': test_form_KKT_Pu_new,\n        'test_form_KKT_KKT_new': test_form_KKT_KKT_new,\n        'test_form_KKT_KKTu_new': test_form_KKT_KKTu_new,\n        'test_solve_Pu': test_solve_Pu,\n        'test_solve_q': test_solve_q,\n        'test_solve_A': test_solve_A,\n        'test_solve_l': test_solve_l,\n        'test_solve_u': test_solve_u,\n        'n': n,\n        'm': m,\n        'test_solve_x': np.array([-0.34967513, 1.20460722, -0.46259805,\n                                  0.59083905, -0.87685541]),\n        'test_solve_y': np.zeros(m),\n        'test_solve_obj_value': -1.7665127080483103,\n        'test_solve_status': 'optimal',\n        'test_solve_Pu_new': test_solve_Pu_new,\n        'test_solve_P_new_x': np.array([-0.28228879, 1.3527703, -0.69277181,\n                                        0.82445911, -1.11688134]),\n        'test_solve_P_new_y': np.zeros(m),\n        'test_solve_P_new_obj_value': -2.1490899311728526,\n        'test_solve_P_new_status': 'optimal',\n        'test_solve_A_new': test_solve_A_new,\n        'test_solve_A_new_x': np.array([-0.34967513, 1.20460722, -0.46259805,\n                                        0.59083905, -0.87685541]),\n        'test_solve_A_new_y': np.zeros(m),\n        'test_solve_A_new_obj_value': -1.7665127080484808,\n        'test_solve_A_new_status': 'optimal',\n        'test_solve_P_A_new_x': np.array([-0.28228879, 1.3527703, -0.69277181,\n                                          0.82445911, -1.11688134]),\n        'test_solve_P_A_new_y': np.zeros(m),\n        'test_solve_P_A_new_obj_value': -2.1490899311726253,\n        'test_solve_P_A_new_status': 'optimal'\n        }\n\n\n# Generate test data\ncu.generate_data('update_matrices', data)\n"
  },
  {
    "path": "osqp/tests/update_matrices/test_update_matrices.h",
    "content": "#include <stdio.h>\n#include \"osqp.h\"\n#include \"cs.h\"\n#include \"util.h\"\n#include \"minunit.h\"\n#include \"kkt.h\"\n#include \"lin_sys.h\"\n\n\n#include \"update_matrices/data.h\"\n\n\nstatic const char* test_form_KKT() {\n  update_matrices_sols_data *data;\n  c_float sigma, *rho_vec, *rho_inv_vec;\n  c_int   m, *PtoKKT, *AtoKKT, *Pdiag_idx, Pdiag_n;\n  csc    *KKT;\n\n  // Load problem data\n  data = generate_problem_update_matrices_sols_data();\n\n  // Define rho_vec and sigma to form KKT\n  sigma       = data->test_form_KKT_sigma;\n  m           = data->test_form_KKT_A->m;\n  rho_vec     = (c_float*) c_calloc(m, sizeof(c_float));\n  rho_inv_vec = (c_float*) c_calloc(m, sizeof(c_float));\n  vec_add_scalar(rho_vec, data->test_form_KKT_rho, m);\n  vec_ew_recipr(rho_vec, rho_inv_vec, m);\n\n  // Allocate vectors of indices\n  PtoKKT = (c_int*) c_malloc((data->test_form_KKT_Pu->p[data->test_form_KKT_Pu->n]) *\n                    sizeof(c_int));\n  AtoKKT = (c_int*) c_malloc((data->test_form_KKT_A->p[data->test_form_KKT_A->n]) *\n                    sizeof(c_int));\n\n  // Form KKT matrix storing the index vectors\n  KKT = form_KKT(data->test_form_KKT_Pu,\n                 data->test_form_KKT_A,\n                 0,\n                 sigma,\n                 rho_inv_vec,\n                 PtoKKT,\n                 AtoKKT,\n                 &Pdiag_idx,\n                 &Pdiag_n,\n                 OSQP_NULL);\n\n  // Assert if KKT matrix is the same as predicted one\n  mu_assert(\"Update matrices: error in forming KKT matrix!\",\n            is_eq_csc(KKT, data->test_form_KKT_KKTu, TESTS_TOL));\n\n  // Update KKT matrix with new P and new A\n  update_KKT_P(KKT, data->test_form_KKT_Pu_new, PtoKKT, sigma, Pdiag_idx,\n               Pdiag_n);\n  update_KKT_A(KKT, data->test_form_KKT_A_new, AtoKKT);\n\n\n  // Assert if KKT matrix is the same as predicted one\n  mu_assert(\"Update matrices: error in updating KKT matrix!\",\n            is_eq_csc(KKT, data->test_form_KKT_KKTu_new, TESTS_TOL));\n\n\n  // Cleanup\n  clean_problem_update_matrices_sols_data(data);\n  c_free(Pdiag_idx);\n  csc_spfree(KKT);\n  c_free(rho_vec);\n  c_free(rho_inv_vec);\n  c_free(AtoKKT);\n  c_free(PtoKKT);\n  return 0;\n}\n\nstatic const char* test_update() {\n  c_int i, nnzP, nnzA;\n  update_matrices_sols_data *data;\n  OSQPData *problem;\n  OSQPWorkspace *work;\n  OSQPSettings  *settings;\n  c_int exitflag;\n\n  // Update matrix P\n  c_int *Px_new_idx;\n\n  // Update matrix A\n  c_int *Ax_new_idx;\n\n  // Load problem data\n  data = generate_problem_update_matrices_sols_data();\n\n  // Generate first problem data\n  problem    = (OSQPData*) c_malloc(sizeof(OSQPData));\n  problem->P = data->test_solve_Pu;\n  problem->q = data->test_solve_q;\n  problem->A = data->test_solve_A;\n  problem->l = data->test_solve_l;\n  problem->u = data->test_solve_u;\n  problem->n = data->test_solve_Pu->n;\n  problem->m = data->test_solve_A->m;\n\n\n  // Define Solver settings as default\n  // Problem settings\n  settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n  osqp_set_default_settings(settings);\n  settings->max_iter = 1000;\n  settings->alpha    = 1.6;\n  settings->verbose  = 1;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, problem, settings);\n\n  // Setup correct\n  mu_assert(\"Update matrices: original problem, setup error!\", exitflag == 0);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Update matrices: original problem, error in solver status!\",\n            work->info->status_val == data->test_solve_status);\n\n  // Compare primal solutions\n  mu_assert(\"Update matrices: original problem, error in primal solution!\",\n            vec_norm_inf_diff(work->solution->x, data->test_solve_x,\n                              data->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  mu_assert(\"Update matrices: original problem, error in dual solution!\",\n            vec_norm_inf_diff(work->solution->y, data->test_solve_y,\n                              data->m) < TESTS_TOL);\n\n\n  // Update P\n  nnzP       = data->test_solve_Pu->p[data->test_solve_Pu->n];\n  Px_new_idx = (c_int*) c_malloc(nnzP * sizeof(c_int));\n\n  // Generate indices going from beginning to end of P\n  for (i = 0; i < nnzP; i++) {\n    Px_new_idx[i] = i;\n  }\n\n  osqp_update_P(work, data->test_solve_Pu_new->x, Px_new_idx, nnzP);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Update matrices: problem with updating P, error in solver status!\",\n            work->info->status_val == data->test_solve_P_new_status);\n\n  // Compare primal solutions\n  mu_assert(\"Update matrices: problem with updating P, error in primal solution!\",\n            vec_norm_inf_diff(work->solution->x, data->test_solve_P_new_x,\n                              data->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  mu_assert(\"Update matrices: problem with updating P, error in dual solution!\",\n            vec_norm_inf_diff(work->solution->y, data->test_solve_P_new_y,\n                              data->m) < TESTS_TOL);\n\n  // Cleanup and setup workspace\n  osqp_cleanup(work);\n  exitflag = osqp_setup(&work, problem, settings);\n\n\n  // Update P (all indices)\n  osqp_update_P(work, data->test_solve_Pu_new->x, OSQP_NULL, nnzP);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Update matrices: problem with updating P (all indices), error in solver status!\",\n            work->info->status_val == data->test_solve_P_new_status);\n\n  // Compare primal solutions\n  mu_assert(\"Update matrices: problem with updating P (all indices), error in primal solution!\",\n            vec_norm_inf_diff(work->solution->x, data->test_solve_P_new_x,\n                              data->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  mu_assert(\"Update matrices: problem with updating P (all indices), error in dual solution!\",\n            vec_norm_inf_diff(work->solution->y, data->test_solve_P_new_y,\n                              data->m) < TESTS_TOL);\n\n  // Cleanup and setup workspace\n  osqp_cleanup(work);\n  exitflag = osqp_setup(&work, problem, settings);\n\n\n  // Update A\n  nnzA       = data->test_solve_A->p[data->test_solve_A->n];\n  Ax_new_idx = (c_int*) c_malloc(nnzA * sizeof(c_int));\n\n  // Generate indices going from beginning to end of A\n  for (i = 0; i < nnzA; i++) {\n    Ax_new_idx[i] = i;\n  }\n\n  osqp_update_A(work, data->test_solve_A_new->x, Ax_new_idx, nnzA);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Update matrices: problem with updating A, error in solver status!\",\n            work->info->status_val == data->test_solve_A_new_status);\n\n  // Compare primal solutions\n  mu_assert(\"Update matrices: problem with updating A, error in primal solution!\",\n            vec_norm_inf_diff(work->solution->x, data->test_solve_A_new_x,\n                              data->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  mu_assert(\"Update matrices: problem with updating A, error in dual solution!\",\n            vec_norm_inf_diff(work->solution->y, data->test_solve_A_new_y,\n                              data->m) < TESTS_TOL);\n\n  // Cleanup and setup workspace\n  osqp_cleanup(work);\n  exitflag = osqp_setup(&work, problem, settings);\n\n\n  // Update A (all indices)\n  osqp_update_A(work, data->test_solve_A_new->x, OSQP_NULL, nnzA);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Update matrices: problem with updating A (all indices), error in solver status!\",\n            work->info->status_val == data->test_solve_A_new_status);\n\n  // Compare primal solutions\n  mu_assert(\"Update matrices: problem with updating A (all indices), error in primal solution!\",\n            vec_norm_inf_diff(work->solution->x, data->test_solve_A_new_x,\n                              data->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  mu_assert(\"Update matrices: problem with updating A (all indices), error in dual solution!\",\n            vec_norm_inf_diff(work->solution->y, data->test_solve_A_new_y,\n                              data->m) < TESTS_TOL);\n\n\n  // Cleanup and setup workspace\n  osqp_cleanup(work);\n  exitflag = osqp_setup(&work, problem, settings);\n\n  // Update P and A\n  osqp_update_P_A(work, data->test_solve_Pu_new->x, Px_new_idx, nnzP,\n                  data->test_solve_A_new->x, Ax_new_idx, nnzA);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\n    \"Update matrices: problem with updating P and A, error in solver status!\",\n    work->info->status_val == data->test_solve_P_A_new_status);\n\n  // Compare primal solutions\n  mu_assert(\n    \"Update matrices: problem with updating P and A, error in primal solution!\",\n    vec_norm_inf_diff(work->solution->x, data->test_solve_P_A_new_x,\n                      data->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  mu_assert(\n    \"Update matrices: problem with updating P and A, error in dual solution!\",\n    vec_norm_inf_diff(work->solution->y, data->test_solve_P_A_new_y,\n                      data->m) < TESTS_TOL * TESTS_TOL);\n\n  // Cleanup and setup workspace\n  osqp_cleanup(work);\n  exitflag = osqp_setup(&work, problem, settings);\n\n\n  // Update P and A (all indices)\n  osqp_update_P_A(work, data->test_solve_Pu_new->x, OSQP_NULL, nnzP,\n                  data->test_solve_A_new->x, OSQP_NULL, nnzA);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\n    \"Update matrices: problem with updating P and A (all indices), error in solver status!\",\n    work->info->status_val == data->test_solve_P_A_new_status);\n\n  // Compare primal solutions\n  mu_assert(\n    \"Update matrices: problem with updating P and A (all indices), error in primal solution!\",\n    vec_norm_inf_diff(work->solution->x, data->test_solve_P_A_new_x,\n                      data->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  mu_assert(\n    \"Update matrices: problem with updating P and A (all indices), error in dual solution!\",\n    vec_norm_inf_diff(work->solution->y, data->test_solve_P_A_new_y,\n                      data->m) < TESTS_TOL * TESTS_TOL);\n\n\n  // Cleanup problems\n  osqp_cleanup(work);\n  clean_problem_update_matrices_sols_data(data);\n  c_free(problem);\n  c_free(settings);\n  c_free(Ax_new_idx);\n  c_free(Px_new_idx);\n\n  return 0;\n}\n\n#ifdef ENABLE_MKL_PARDISO\nstatic char* test_update_pardiso() {\n  c_int i, nnzP, nnzA, exitflag;\n  update_matrices_sols_data *data;\n  OSQPData *problem;\n  OSQPWorkspace *work;\n  OSQPSettings  *settings;\n\n  // Update matrix P\n  c_int *Px_new_idx;\n\n  // Update matrix A\n  c_int *Ax_new_idx;\n\n  // Load problem data\n  data = generate_problem_update_matrices_sols_data();\n\n  // Generate first problem data\n  problem    = c_malloc(sizeof(OSQPData));\n  problem->P = data->test_solve_Pu;\n  problem->q = data->test_solve_q;\n  problem->A = data->test_solve_A;\n  problem->l = data->test_solve_l;\n  problem->u = data->test_solve_u;\n  problem->n = data->test_solve_Pu->n;\n  problem->m = data->test_solve_A->m;\n\n\n  // Define Solver settings as default\n  // Problem settings\n  settings = (OSQPSettings *)c_malloc(sizeof(OSQPSettings));\n  osqp_set_default_settings(settings);\n  settings->max_iter      = 1000;\n  settings->alpha         = 1.6;\n  settings->verbose       = 1;\n  settings->linsys_solver = MKL_PARDISO_SOLVER;\n\n  // Setup workspace\n  exitflag = osqp_setup(&work, problem, settings);\n\n  // Setup correct\n  mu_assert(\"Update matrices: original problem, setup error!\", exitflag == 0);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Update matrices: original problem, error in solver status!\",\n            work->info->status_val == data->test_solve_status);\n\n  // Compare primal solutions\n  mu_assert(\"Update matrices: original problem, error in primal solution!\",\n            vec_norm_inf_diff(work->solution->x, data->test_solve_x,\n                              data->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  mu_assert(\"Update matrices: original problem, error in dual solution!\",\n            vec_norm_inf_diff(work->solution->y, data->test_solve_y,\n                              data->m) < TESTS_TOL);\n\n\n  // Update P\n  nnzP       = data->test_solve_Pu->p[data->test_solve_Pu->n];\n  Px_new_idx = c_malloc(nnzP * sizeof(c_int)); // Generate indices going from\n                                               // beginning to end of P\n\n  for (i = 0; i < nnzP; i++) {\n    Px_new_idx[i] = i;\n  }\n\n  osqp_update_P(work, data->test_solve_Pu_new->x, Px_new_idx, nnzP);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Update matrices: problem with P updated, error in solver status!\",\n            work->info->status_val == data->test_solve_P_new_status);\n\n  // Compare primal solutions\n  mu_assert(\"Update matrices: problem with P updated, error in primal solution!\",\n            vec_norm_inf_diff(work->solution->x, data->test_solve_P_new_x,\n                              data->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  mu_assert(\"Update matrices: problem with P updated, error in dual solution!\",\n            vec_norm_inf_diff(work->solution->y, data->test_solve_P_new_y,\n                              data->m) < TESTS_TOL);\n\n\n  // Update A\n  nnzA       = data->test_solve_A->p[data->test_solve_A->n];\n  Ax_new_idx = c_malloc(nnzA * sizeof(c_int)); // Generate indices going from\n                                               // beginning to end of P\n\n  for (i = 0; i < nnzA; i++) {\n    Ax_new_idx[i] = i;\n  }\n\n  // Cleanup and setup workspace\n  osqp_cleanup(work);\n  exitflag = osqp_setup(&work, problem, settings);\n\n  osqp_update_A(work, data->test_solve_A_new->x, Ax_new_idx, nnzA);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\"Update matrices: problem with A updated, error in solver status!\",\n            work->info->status_val == data->test_solve_A_new_status);\n\n  // Compare primal solutions\n  mu_assert(\"Update matrices: problem with A updated, error in primal solution!\",\n            vec_norm_inf_diff(work->solution->x, data->test_solve_A_new_x,\n                              data->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  mu_assert(\"Update matrices: problem with A updated, error in dual solution!\",\n            vec_norm_inf_diff(work->solution->y, data->test_solve_A_new_y,\n                              data->m) < TESTS_TOL);\n\n\n  // Cleanup and setup workspace\n  osqp_cleanup(work);\n  exitflag = osqp_setup(&work, problem, settings);\n\n  osqp_update_P_A(work, data->test_solve_Pu_new->x, Px_new_idx, nnzP,\n                  data->test_solve_A_new->x, Ax_new_idx, nnzA);\n\n  // Solve Problem\n  osqp_solve(work);\n\n  // Compare solver statuses\n  mu_assert(\n    \"Update matrices: problem with P and A updated, error in solver status!\",\n    work->info->status_val == data->test_solve_P_A_new_status);\n\n  // Compare primal solutions\n  mu_assert(\n    \"Update matrices: problem with P and A updated, error in primal solution!\",\n    vec_norm_inf_diff(work->solution->x, data->test_solve_P_A_new_x,\n                      data->n) < TESTS_TOL);\n\n  // Compare dual solutions\n  mu_assert(\n    \"Update matrices: problem with P and A updated, error in dual solution!\",\n    vec_norm_inf_diff(work->solution->y, data->test_solve_P_A_new_y,\n                      data->m) < TESTS_TOL * TESTS_TOL);\n\n\n  // Cleanup problems\n  osqp_cleanup(work);\n  clean_problem_update_matrices_sols_data(data);\n  c_free(problem);\n  c_free(settings);\n  c_free(Ax_new_idx);\n  c_free(Px_new_idx);\n\n  return 0;\n}\n#endif\n\nstatic const char* test_update_matrices()\n{\n  mu_run_test(test_form_KKT);\n  mu_run_test(test_update);\n\n#ifdef ENABLE_MKL_PARDISO\n  mu_run_test(test_update_pardiso);\n#endif\n\n  return 0;\n}\n"
  },
  {
    "path": "osqp/tests/utils/__init__.py",
    "content": ""
  },
  {
    "path": "osqp/tests/utils/codegen_utils.py",
    "content": "# Compatibility with Python 2\nfrom __future__ import print_function\n\nfrom scipy import sparse\nimport numpy as np\n\n\ndef write_int(f, x, name, *args):\n    if any(args):\n        for arg in args:\n            f.write(\"%s->\" % arg)\n        f.write(\"%s = %i;\\n\" % (name, x))\n    else:\n        f.write(\"c_int %s = %i;\\n\" % (name, x))\n\n\ndef write_float(f, x, name, *args):\n    if any(args):\n        for arg in args:\n            f.write(\"%s->\" % arg)\n        f.write(\"%s = %.20f;\\n\" % (name, x))\n    else:\n        f.write(\"c_float %s = %.20f;\\n\" % (name, x))\n\n\ndef write_vec_int(f, x, name, *args):\n    n = len(x)\n    if any(args):\n        for arg in args:\n            f.write(\"%s->\" % arg)\n    else:\n        f.write(\"c_int * \")\n    f.write(\"%s = (c_int*) c_malloc(%i * sizeof(c_int));\\n\" % (name, n))\n\n    for i in range(n):\n        for arg in args:\n            f.write(\"%s->\" % arg)\n        f.write(\"%s[%i] = \" % (name, i))\n        f.write(\"%i;\\n\" % x[i])\n\n\ndef write_vec_float(f, x, name, *args):\n    n = len(x)\n    if any(args):\n        for arg in args:\n            f.write(\"%s->\" % arg)\n    else:\n        f.write(\"c_float * \")\n    f.write(\"%s = (c_float*) c_malloc(%i * sizeof(c_float));\\n\" % (name, n))\n\n    for i in range(n):\n        for arg in args:\n            f.write(\"%s->\" % arg)\n        f.write(\"%s[%i] = \" % (name, i))\n        if x[i] == np.inf:\n            f.write(\"OSQP_INFTY;\\n\")\n        elif x[i] == -np.inf:\n            f.write(\"-OSQP_INFTY;\\n\")\n        else:\n            f.write(\"%.20f;\\n\" % x[i])\n\n\ndef clean_vec(f, name, *args):\n    f.write(\"c_free(\")\n    if any(args):\n        for arg in args:\n            f.write(\"%s->\" % arg)\n    # else:\n        # f.write(\"c_float * \")\n    f.write(\"%s);\\n\" % name)\n\n\ndef write_mat_sparse(f, A, name, *args):\n    m = A.shape[0]\n    n = A.shape[1]\n\n    f.write(\"\\n// Matrix \" + name + \"\\n\")\n    f.write(\"//\")\n    f.write(\"-\"*(len(\"Matrix  \") + len(name)) + \"\\n\")\n\n    # Allocate Matrix\n    if any(args):\n        for arg in args:\n            f.write(\"%s->\" % arg)\n    else:\n        f.write(\"csc * \")\n    f.write(name + \" = (csc*) c_malloc(sizeof(csc));\\n\")\n\n    # Write dimensions and number of nonzeros\n    if any(args):\n        write_int(f, m, \"m\", args, name)\n        write_int(f, n, \"n\", args, name)\n        write_int(f, -1, \"nz\", args, name)\n        write_int(f, A.nnz, \"nzmax\", args, name)\n    else:\n        write_int(f, m, \"m\", name)\n        write_int(f, n, \"n\", name)\n        write_int(f, -1, \"nz\", name)\n        write_int(f, A.nnz, \"nzmax\", name)\n\n    for arg in args:\n        f.write(\"%s->\" % arg)\n    if min(m,n) == 0:\n        f.write(\"%s->x = OSQP_NULL;\\n\" % name)\n    else:\n        f.write(\"%s->\" % name)\n        f.write(\"x = (c_float*) c_malloc(%i * sizeof(c_float));\\n\" % A.nnz)\n        for i in range(A.nnz):\n            for arg in args:\n                f.write(\"%s->\" % arg)\n            f.write(\"%s->\" % name)\n            f.write(\"x[%i] = %.20f;\\n\" % (i, A.data[i]))\n\n    for arg in args:\n        f.write(\"%s->\" % arg)\n    if min(m,n) == 0:\n        f.write(\"%s->i = OSQP_NULL;\\n\" % name)\n    else:\n        f.write(\"%s->\" % name)\n        f.write(\"i = (c_int*) c_malloc(%i * sizeof(c_int));\\n\" % A.nnz)\n        for i in range(A.nnz):\n            for arg in args:\n                f.write(\"%s->\" % arg)\n            f.write(\"%s->\" % name)\n            f.write(\"i[%i] = %i;\\n\" % (i, A.indices[i]))\n\n    for arg in args:\n        f.write(\"%s->\" % arg)\n    f.write(\"%s->\" % name)\n    f.write(\"p = (c_int*) c_malloc((%i + 1) * sizeof(c_int));\\n\" % n)\n    for i in range(A.shape[1] + 1):\n        for arg in args:\n            f.write(\"%s->\" % arg)\n        f.write(\"%s->\" % name)\n        f.write(\"p[%i] = %i;\\n\" % (i, A.indptr[i]))\n\n    # Do the same for i and p\n    f.write(\"\\n\")\n\n\ndef clean_mat(f, name, *args):\n\n    # Clean data vector\n    f.write(\"c_free(\")\n    if any(args):\n        for arg in args:\n            f.write(\"%s->\" % arg)\n    f.write(\"%s->x);\\n\" % name)\n\n    # Clean index vector\n    f.write(\"c_free(\")\n    if any(args):\n        for arg in args:\n            f.write(\"%s->\" % arg)\n    f.write(\"%s->i);\\n\" % name)\n\n    # Clean col pointer vector\n    f.write(\"c_free(\")\n    if any(args):\n        for arg in args:\n            f.write(\"%s->\" % arg)\n    f.write(\"%s->p);\\n\" % name)\n\n    # Clean matrix\n    f.write(\"c_free(\")\n    if any(args):\n        for arg in args:\n            f.write(\"%s->\" % arg)\n    f.write(\"%s);\\n\" % name)\n\n\ndef generate_problem_data(P, q, A, l, u, problem_name, sols_data={}):\n    \"\"\"\n    Generate test problem data.\n\n    The additional structure sols_data defines the additional vectors/scalars\n    we need to perform the tests\n    \"\"\"\n    # Get problem dimension\n    n = P.shape[0]\n    m = A.shape[0]\n\n    #\n    # GENERATE HEADER FILE\n    #\n    f = open(problem_name + \"/data.h\", \"w\")\n\n    # Add definition check\n    f.write(\"#ifndef \" + problem_name.upper() + \"_DATA_H\\n\")\n    f.write(\"#define \" + problem_name.upper() + \"_DATA_H\\n\")\n\n    # Add Includes\n    f.write(\"#include \\\"osqp.h\\\"\\n\")\n    f.write(\"\\n\\n\")\n\n    #\n    # Create additional data structure\n    #\n    f.write(\"/* create additional data and solutions structure */\\n\")\n    f.write(\"typedef struct {\\n\")\n    # Generate further data and solutions\n    for key, value in sols_data.items():\n        if isinstance(value, str):\n            # Status test get from C code\n            f.write(\"c_int %s;\\n\" % key)\n        # Check if it is an array or a scalar\n        elif isinstance(value, np.ndarray):\n            if isinstance(value.flatten(order='F')[0], int):\n                f.write(\"c_int * %s;\\n\" % key)\n            elif isinstance(value.flatten(order='F')[0], float):\n                f.write(\"c_float * %s;\\n\" % key)\n        else:\n            if isinstance(value, int):\n                f.write(\"c_int %s;\\n\" % key)\n            elif isinstance(value, float):\n                f.write(\"c_float %s;\\n\" % key)\n    f.write(\"} %s_sols_data;\\n\\n\" % problem_name)\n\n    # prototypes\n    f.write(\"/* function prototypes */\\n\")\n    f.write(\"OSQPData * generate_problem_%s();\\n\" % problem_name)\n    f.write(\"void clean_problem_%s(OSQPData * data);\\n\" % problem_name)\n    f.write(\"%s_sols_data *  generate_problem_%s_sols_data();\\n\" % (problem_name, problem_name))\n    f.write(\"void clean_problem_%s_sols_data(%s_sols_data * data);\\n\" % (problem_name, problem_name))\n    f.write(\"\\n\\n\")\n\n    #\n    # Generate QP problem data\n    #\n    f.write(\"/* function to generate QP problem data */\\n\")\n    f.write(\"OSQPData * generate_problem_%s(){\\n\\n\" % problem_name)\n\n    # Initialize structure data\n    f.write(\"OSQPData * data = (OSQPData *)c_malloc(sizeof(OSQPData));\\n\\n\")\n\n    # Write problem dimensions\n    f.write(\"// Problem dimensions\\n\")\n    write_int(f, n, \"n\", \"data\")\n    write_int(f, m, \"m\", \"data\")\n    f.write(\"\\n\")\n\n    # Write problem vectors\n    f.write(\"// Problem vectors\\n\")\n    write_vec_float(f, l, \"l\", \"data\")\n    write_vec_float(f, u, \"u\", \"data\")\n    write_vec_float(f, q, \"q\", \"data\")\n    f.write(\"\\n\")\n\n    # Write matrix A\n    write_mat_sparse(f, A, \"A\", \"data\")\n    write_mat_sparse(f, P, \"P\", \"data\")\n\n    # Return data and end function\n    f.write(\"return data;\\n\\n\")\n\n    f.write(\"}\\n\\n\")\n\n\n    #\n    # Generate QP problem data\n    #\n    f.write(\"/* function to clean problem data structure */\\n\")\n    f.write(\"void clean_problem_%s(OSQPData * data){\\n\\n\" % problem_name)\n\n    # Free vectors\n    f.write(\"// Clean vectors\\n\")\n    clean_vec(f, \"l\", \"data\")\n    clean_vec(f, \"u\", \"data\")\n    clean_vec(f, \"q\", \"data\")\n    f.write(\"\\n\")\n\n    # Free matrices\n    f.write(\"//Clean Matrices\\n\")\n    clean_mat(f, \"A\", \"data\")\n    clean_mat(f, \"P\", \"data\")\n    f.write(\"\\n\")\n\n    f.write(\"c_free(data);\\n\\n\")\n\n    f.write(\"}\\n\\n\")\n\n\n\n    #\n    # Generate additional problem data for solutions\n    #\n    f.write(\"/* function to define solutions and additional data struct */\\n\")\n    f.write(\"%s_sols_data *  generate_problem_%s_sols_data(){\\n\\n\" % (problem_name, problem_name))\n\n    # Initialize structure data\n    f.write(\"%s_sols_data * data = (%s_sols_data *)c_malloc(sizeof(%s_sols_data));\\n\\n\" % (problem_name, problem_name, problem_name))\n\n\n    # Generate further data and solutions\n    for key, value in sols_data.items():\n        if isinstance(value, str):\n            # Status test get from C code\n            if value == 'optimal':\n                f.write(\"data->%s = %s;\\n\" % (key, 'OSQP_SOLVED'))\n            elif value == 'optimal_inaccurate':\n                f.write(\"data->%s = %s;\\n\" % (key, 'OSQP_SOLVED_INACCURATE'))\n            elif value == 'primal_infeasible':\n                f.write(\"data->%s = %s;\\n\" % (key, 'OSQP_PRIMAL_INFEASIBLE'))\n            elif value == 'primal_infeasible_inaccurate':\n                f.write(\"data->%s = %s;\\n\" %\n                        (key, 'OSQP_PRIMAL_INFEASIBLE_INACCURATE'))\n            elif value == 'dual_infeasible':\n                f.write(\"data->%s = %s;\\n\" % (key, 'OSQP_DUAL_INFEASIBLE'))\n            elif value == 'dual_infeasible_inaccurate':\n                f.write(\"data->%s = %s;\\n\" % (key, 'OSQP_DUAL_INFEASIBLE_INACCURATE'))\n\n        # Check if it is an array or a scalar\n        if type(value) is np.ndarray:\n            if isinstance(value.flatten(order='F')[0], int):\n                write_vec_int(f, value.flatten(order='F'), key, \"data\")\n            elif isinstance(value.flatten(order='F')[0], float):\n                write_vec_float(f, value.flatten(order='F'), key, \"data\")\n        else:\n            if isinstance(value, int):\n                write_int(f, value, key, \"data\")\n            elif isinstance(value, float):\n                write_float(f, value, key, \"data\")\n\n    # Return data and end function\n    f.write(\"\\nreturn data;\\n\\n\")\n\n    f.write(\"}\\n\\n\")\n\n\n\n    #\n    # Clean additional problem data for solutions\n    #\n    f.write(\"/* function to clean solutions and additional data struct */\\n\")\n    f.write(\"void clean_problem_%s_sols_data(%s_sols_data * data){\\n\\n\" % (problem_name, problem_name))\n    # Generate further data and solutions\n    for key, value in sols_data.items():\n        # Check if it is an array or a scalar\n        if type(value) is np.ndarray:\n            clean_vec(f, key, \"data\")\n\n    f.write(\"\\nc_free(data);\\n\\n\")\n\n    f.write(\"}\\n\\n\")\n\n    f.write(\"#endif\\n\")\n\n    f.close()\n\n\ndef generate_data(problem_name, sols_data):\n    \"\"\"\n    Generate test data vectors.\n\n    The additional structure sols_data defines the additional vectors/scalars\n    we need to perform the tests\n    \"\"\"\n\n    #\n    # GENERATE HEADER FILE\n    #\n    f = open(problem_name + \"/data.h\", \"w\")\n\n    # Add definition check\n    f.write(\"#ifndef \" + problem_name.upper() + \"_DATA_H\\n\")\n    f.write(\"#define \" + problem_name.upper() + \"_DATA_H\\n\")\n\n    # Add Includes\n    f.write(\"#include \\\"osqp.h\\\"\\n\")\n    f.write(\"\\n\\n\")\n\n    #\n    # Create additional data structure\n    #\n    f.write(\"/* create data and solutions structure */\\n\")\n    f.write(\"typedef struct {\\n\")\n    # Generate further data and solutions\n    for key, value in sols_data.items():\n        if isinstance(value, str):\n            # Status test get from C code\n            f.write(\"c_int %s;\\n\" % key)\n        # Check if it is an array or a scalar\n        elif sparse.issparse(value):  # Sparse matrix\n            f.write(\"csc * %s;\\n\" % key)\n        elif isinstance(value, np.ndarray):\n            if isinstance(value.flatten(order='F')[0], int):\n                f.write(\"c_int * %s;\\n\" % key)\n            elif isinstance(value.flatten(order='F')[0], float):\n                f.write(\"c_float * %s;\\n\" % key)\n        else:\n            if isinstance(value, int):\n                f.write(\"c_int %s;\\n\" % key)\n            elif isinstance(value, float):\n                f.write(\"c_float %s;\\n\" % key)\n    f.write(\"} %s_sols_data;\\n\\n\" % problem_name)\n\n    # prototypes\n    f.write(\"/* function prototypes */\\n\")\n    f.write(\"%s_sols_data *  generate_problem_%s_sols_data();\\n\" % (problem_name, problem_name))\n    f.write(\"void clean_problem_%s_sols_data(%s_sols_data * data);\\n\" % (problem_name, problem_name))\n    f.write(\"\\n\\n\")\n\n    #\n    # Generate additional problem data for solutions\n    #\n    f.write(\"/* function to define problem data */\\n\")\n    f.write(\"%s_sols_data *  generate_problem_%s_sols_data(){\\n\\n\" % (problem_name, problem_name))\n\n    # Initialize structure data\n    f.write(\"%s_sols_data * data = (%s_sols_data *)c_malloc(sizeof(%s_sols_data));\\n\\n\" % (problem_name, problem_name, problem_name))\n\n    # Generate further data and solutions\n    for key, value in sols_data.items():\n        if isinstance(value, str):\n            # Status test get from C code\n            if value == 'optimal':\n                f.write(\"data->%s = %s;\\n\" % (key, 'OSQP_SOLVED'))\n            elif value == 'primal_infeasible':\n                f.write(\"data->%s = %s;\\n\" % (key, 'OSQP_PRIMAL_INFEASIBLE'))\n            elif value == 'dual_infeasible':\n                f.write(\"data->%s = %s;\\n\" % (key, 'OSQP_DUAL_INFEASIBLE'))\n        # Check if it is an array or a scalar\n        elif sparse.issparse(value):  # Sparse matrix\n            write_mat_sparse(f, value, key, \"data\")\n        elif type(value) is np.ndarray:\n            if isinstance(value.flatten(order='F')[0], int):\n                write_vec_int(f, value.flatten(order='F'), key, \"data\")\n            elif isinstance(value.flatten(order='F')[0], float):\n                write_vec_float(f, value.flatten(order='F'), key, \"data\")\n        else:\n            if isinstance(value, int):\n                write_int(f, value, key, \"data\")\n            elif isinstance(value, float):\n                write_float(f, value, key, \"data\")\n\n    # Return data and end function\n    f.write(\"\\nreturn data;\\n\\n\")\n\n    f.write(\"}\\n\\n\")\n\n\n    #\n    # Clean  data\n    #\n    f.write(\"/* function to clean data struct */\\n\")\n    f.write(\"void clean_problem_%s_sols_data(%s_sols_data * data){\\n\\n\" % (problem_name, problem_name))\n    # Generate further data and solutions\n    for key, value in sols_data.items():\n        # Check if it is an array or a scalar\n        if sparse.issparse(value):  # Sparse matrix\n            clean_mat(f, key, \"data\")\n        elif type(value) is np.ndarray:\n            clean_vec(f, key, \"data\")\n\n    f.write(\"\\nc_free(data);\\n\\n\")\n\n    f.write(\"}\\n\\n\")\n\n    f.write(\"#endif\\n\")\n\n    f.close()\n"
  },
  {
    "path": "simulation.sh",
    "content": "python key2joy.py & sleep 1;\nroslaunch uav_simulator uav_simulator.launch & sleep 1;\nwait\n"
  },
  {
    "path": "src/cmpcc/CMakeLists.txt",
    "content": "cmake_minimum_required(VERSION 2.8.3)\nproject(cmpcc)\n\nset(CMAKE_CXX_STANDARD 14)\nset(CMAKE_BUILD_TYPE \"Release\")\nset(CMAKE_CXX_FLAGS_RELEASE \"-O3 -w -g -fPIC\")\n\nfind_package(catkin REQUIRED COMPONENTS\n        message_generation\n        message_runtime\n        # decomp_ros_msgs\n        quadrotor_msgs\n        roscpp\n        roslib\n        )\n\nfind_package(Eigen3)\nfind_package(yaml-cpp)\nfind_package(osqp REQUIRED)\n\ncatkin_package()\n\ninclude_directories(\n  include\n  ${catkin_INCLUDE_DIRS}\n  ${EIGEN3_INCLUDE_DIR}\n  ${YAML_CPP_INCLUDE_DIR}\n)\n\nadd_executable(osqp_test\n    src/osqp_test.cpp\n    src/osqp_interface.cpp\n)\ntarget_link_libraries(osqp_test osqp::osqp)\n\nadd_executable(corridor_test\n    src/corridor_test.cpp\n    src/map.cpp\n    src/bezier_base.cpp\n    src/my_spline.cpp\n    src/corridor.cpp)\nadd_dependencies(corridor_test ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})\ntarget_link_libraries(corridor_test ${catkin_LIBRARIES} yaml-cpp)\n\nadd_executable(solver_test\n    src/display_msg.cpp\n    src/solver_test.cpp\n    src/mpc_solver.cpp\n    src/osqp_interface.cpp\n    src/model.cpp\n    src/map.cpp\n    src/bezier_base.cpp\n    src/my_spline.cpp\n    src/corridor.cpp)\nadd_dependencies(solver_test ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})\ntarget_link_libraries(solver_test\n    ${catkin_LIBRARIES} \n    yaml-cpp\n    osqp::osqp\n)\n\nadd_executable(simulation_node\n    src/simulation_node.cpp\n    src/display_msg.cpp\n    src/mpc_solver.cpp\n    src/osqp_interface.cpp\n    src/model.cpp\n    src/map.cpp\n    src/bezier_base.cpp\n    src/my_spline.cpp\n    src/corridor.cpp) \nadd_dependencies(simulation_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})\ntarget_link_libraries(simulation_node\n    ${catkin_LIBRARIES}\n    yaml-cpp\n    osqp::osqp\n)   \n\nadd_executable(flyfly_node\n    src/flyfly_node.cpp\n    src/display_msg.cpp\n    src/mpc_solver.cpp\n    src/osqp_interface.cpp\n    src/model.cpp\n    src/map.cpp\n    src/bezier_base.cpp\n    src/my_spline.cpp\n    src/corridor.cpp) \nadd_dependencies(flyfly_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})\ntarget_link_libraries(flyfly_node\n    ${catkin_LIBRARIES}\n    yaml-cpp\n    osqp::osqp\n)   \n\nadd_executable(plot_bag_node\n    src/plot_bag_node.cpp\n    src/display_msg.cpp\n    src/mpc_solver.cpp\n    src/osqp_interface.cpp\n    src/model.cpp\n    src/map.cpp\n    src/bezier_base.cpp\n    src/my_spline.cpp\n    src/corridor.cpp) \nadd_dependencies(plot_bag_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})\ntarget_link_libraries(plot_bag_node\n    ${catkin_LIBRARIES}\n    yaml-cpp\n    osqp::osqp\n)   \n\nadd_executable(fly_trr_node\n    src/fly_trr_node.cpp\n    src/display_msg.cpp\n    src/mpc_solver.cpp\n    src/osqp_interface.cpp\n    src/model.cpp\n    src/map.cpp\n    src/bezier_base.cpp\n    src/my_spline.cpp\n    src/corridor.cpp) \nadd_dependencies(fly_trr_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})\ntarget_link_libraries(fly_trr_node\n    ${catkin_LIBRARIES}\n    yaml-cpp\n    osqp::osqp\n)   \n\n"
  },
  {
    "path": "src/cmpcc/config/3d.rviz",
    "content": "Panels:\n  - Class: rviz/Displays\n    Help Height: 78\n    Name: Displays\n    Property Tree Widget:\n      Expanded:\n        - /Global Options1\n        - /Status1\n        - /Path2\n        - /PolyhedronArray1\n        - /PolyhedronArray2\n      Splitter Ratio: 0.460905343\n    Tree Height: 776\n  - Class: rviz/Selection\n    Name: Selection\n  - Class: rviz/Tool Properties\n    Expanded:\n      - /2D Pose Estimate1\n      - /2D Nav Goal1\n      - /Publish Point1\n    Name: Tool Properties\n    Splitter Ratio: 0.588679016\n  - Class: rviz/Views\n    Expanded:\n      - /Current View1\n    Name: Views\n    Splitter Ratio: 0.5\n  - Class: rviz/Time\n    Experimental: false\n    Name: Time\n    SyncMode: 0\n    SyncSource: \"\"\nToolbars:\n  toolButtonStyle: 2\nVisualization Manager:\n  Class: \"\"\n  Displays:\n    - Alpha: 0.5\n      Cell Size: 1\n      Class: rviz/Grid\n      Color: 160; 160; 164\n      Enabled: false\n      Line Style:\n        Line Width: 0.0299999993\n        Value: Lines\n      Name: Grid\n      Normal Cell Count: 0\n      Offset:\n        X: 0\n        Y: 0\n        Z: 0\n      Plane: XY\n      Plane Cell Count: 10\n      Reference Frame: <Fixed Frame>\n      Value: false\n    - Alpha: 1\n      Buffer Length: 1\n      Class: rviz/Path\n      Color: 25; 255; 0\n      Enabled: true\n      Head Diameter: 0.300000012\n      Head Length: 0.200000003\n      Length: 0.300000012\n      Line Style: Lines\n      Line Width: 0.0299999993\n      Name: Path\n      Offset:\n        X: 0\n        Y: 0\n        Z: 0\n      Pose Color: 255; 85; 255\n      Pose Style: None\n      Radius: 0.0299999993\n      Shaft Diameter: 0.100000001\n      Shaft Length: 0.100000001\n      Topic: /refer_path\n      Unreliable: false\n      Value: true\n    - Class: rviz/Marker\n      Enabled: true\n      Marker Topic: /drone_pose\n      Name: Marker\n      Namespaces:\n        \"\": true\n      Queue Size: 100\n      Value: true\n    - Class: rviz/Marker\n      Enabled: true\n      Marker Topic: /theta_pose\n      Name: Marker\n      Namespaces:\n        \"\": true\n      Queue Size: 100\n      Value: true\n    - Alpha: 1\n      Buffer Length: 1\n      Class: rviz/Path\n      Color: 255; 0; 0\n      Enabled: true\n      Head Diameter: 0.300000012\n      Head Length: 0.200000003\n      Length: 0.300000012\n      Line Style: Lines\n      Line Width: 0.0299999993\n      Name: Path\n      Offset:\n        X: 0\n        Y: 0\n        Z: 0\n      Pose Color: 255; 85; 255\n      Pose Style: None\n      Radius: 0.0299999993\n      Shaft Diameter: 0.100000001\n      Shaft Length: 0.100000001\n      Topic: /predict_path\n      Unreliable: false\n      Value: true\n    - Alpha: 0.100000001\n      BoundColor: 255; 0; 0\n      Class: decomp_rviz_plugins/PolyhedronArray\n      Enabled: true\n      MeshColor: 0; 170; 255\n      Name: PolyhedronArray\n      Scale: 0.100000001\n      State: Mesh\n      Topic: /polyhedron_corridor_mesh\n      Unreliable: false\n      Value: true\n      VsColor: 0; 255; 0\n      VsScale: 1\n    - Alpha: 0.0500000007\n      BoundColor: 255; 0; 0\n      Class: decomp_rviz_plugins/PolyhedronArray\n      Enabled: true\n      MeshColor: 255; 255; 0\n      Name: PolyhedronArray\n      Scale: 0.00999999978\n      State: Mesh\n      Topic: /flight_tunnel\n      Unreliable: false\n      Value: true\n      VsColor: 0; 255; 0\n      VsScale: 1\n  Enabled: true\n  Global Options:\n    Background Color: 48; 48; 48\n    Default Light: true\n    Fixed Frame: map\n    Frame Rate: 30\n  Name: root\n  Tools:\n    - Class: rviz/Interact\n      Hide Inactive Objects: true\n    - Class: rviz/MoveCamera\n    - Class: rviz/Select\n    - Class: rviz/FocusCamera\n    - Class: rviz/Measure\n    - Class: rviz/SetInitialPose\n      Topic: /initialpose\n    - Class: rviz/SetGoal\n      Topic: /move_base_simple/goal\n    - Class: rviz/PublishPoint\n      Single click: true\n      Topic: /clicked_point\n  Value: true\n  Views:\n    Current:\n      Class: rviz/Orbit\n      Distance: 32.8972092\n      Enable Stereo Rendering:\n        Stereo Eye Separation: 0.0599999987\n        Stereo Focal Distance: 1\n        Swap Stereo Eyes: false\n        Value: false\n      Focal Point:\n        X: 0\n        Y: 0\n        Z: 0\n      Focal Shape Fixed Size: true\n      Focal Shape Size: 0.0500000007\n      Invert Z Axis: false\n      Name: Current View\n      Near Clip Distance: 0.00999999978\n      Pitch: 1.05479693\n      Target Frame: <Fixed Frame>\n      Value: Orbit (rviz)\n      Yaw: 0.38222295\n    Saved:\n      - Class: rviz/Orbit\n        Distance: 9.07741165\n        Enable Stereo Rendering:\n          Stereo Eye Separation: 0.0599999987\n          Stereo Focal Distance: 1\n          Swap Stereo Eyes: false\n          Value: false\n        Focal Point:\n          X: 0\n          Y: 0\n          Z: 0\n        Focal Shape Fixed Size: true\n        Focal Shape Size: 0.0500000007\n        Invert Z Axis: false\n        Name: Orbit\n        Near Clip Distance: 0.00999999978\n        Pitch: 1.03039789\n        Target Frame: <Fixed Frame>\n        Value: Orbit (rviz)\n        Yaw: 1.56039751\nWindow Geometry:\n  Displays:\n    collapsed: false\n  Height: 1056\n  Hide Left Dock: false\n  Hide Right Dock: false\n  QMainWindow State: 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\n  Selection:\n    collapsed: false\n  Time:\n    collapsed: false\n  Tool Properties:\n    collapsed: false\n  Views:\n    collapsed: false\n  Width: 1855\n  X: 65\n  Y: 24\n"
  },
  {
    "path": "src/cmpcc/config/corridor.yaml",
    "content": "header: ssh\npolyhedrons: \n  - \n    points: \n      - \n        x: -5.535\n        y: -6.105\n        z: 0.0\n      - \n        x: -5.63\n        y: -6.105\n        z: 0.095\n      - \n        x: -5.535\n        y: -10.0\n        z: 0.095\n      - \n        x: -5.535\n        y: -9.905\n        z: 2.85\n      - \n        x: -0.025\n        y: -6.01\n        z: 0.095\n      - \n        x: 0.07\n        y: -6.105\n        z: 2.755\n    normals: \n      - \n        x: -0.0\n        y: -0.0\n        z: -1.0\n      - \n        x: -1.0\n        y: -0.0\n        z: -0.0\n      - \n        x: -0.0\n        y: -1.0\n        z: -0.0\n      - \n        x: -0.0\n        y: -0.0\n        z: 1.0\n      - \n        x: 2.22336421478e-16\n        y: 1.0\n        z: 3.50021052099e-16\n      - \n        x: 1.0\n        y: 4.67462326158e-16\n        z: 5.07530525543e-16\n  - \n    points: \n      - \n        x: -3.445\n        y: -4.585\n        z: 0.0\n      - \n        x: -3.92\n        y: -9.905\n        z: 0.095\n      - \n        x: -3.445\n        y: -4.585\n        z: 0.095\n      - \n        x: -2.495\n        y: -3.635\n        z: 0.095\n      - \n        x: -2.305\n        y: -4.205\n        z: 0.095\n      - \n        x: -1.925\n        y: -10.0\n        z: 0.095\n      - \n        x: -3.825\n        y: -8.765\n        z: 2.85\n      - \n        x: -1.925\n        y: -8.385\n        z: 0.095\n      - \n        x: -1.83\n        y: -8.385\n        z: 0.095\n    normals: \n      - \n        x: 2.33731163079e-15\n        y: -0.0\n        z: -1.0\n      - \n        x: -1.0\n        y: -0.0\n        z: -0.0\n      - \n        x: -0.995893206468\n        y: 0.0905357460425\n        z: -0.0\n      - \n        x: -0.707106781187\n        y: 0.707106781187\n        z: 2.86697871081e-15\n      - \n        x: 0.948683298051\n        y: 0.316227766017\n        z: 2.47733903315e-15\n      - \n        x: -0.0\n        y: -1.0\n        z: -0.0\n      - \n        x: -1.55431223448e-15\n        y: 2.33731163079e-15\n        z: 1.0\n      - \n        x: 0.995893206468\n        y: 0.0905357460425\n        z: 1.42761194408e-15\n      - \n        x: 1.0\n        y: -1.55820775386e-16\n        z: 1.31571446877e-15\n  - \n    points: \n      - \n        x: 1.305\n        y: -1.545\n        z: 0.0\n      - \n        x: -3.35\n        y: -4.205\n        z: 0.095\n      - \n        x: 1.115\n        y: -4.3\n        z: 0.095\n      - \n        x: 1.305\n        y: -1.925\n        z: 0.095\n      - \n        x: 1.115\n        y: -4.205\n        z: 2.85\n      - \n        x: 1.305\n        y: -1.545\n        z: 0.095\n      - \n        x: 1.4\n        y: -1.545\n        z: 0.095\n    normals: \n      - \n        x: -0.0\n        y: -0.0\n        z: -1.0\n      - \n        x: -1.0\n        y: -0.0\n        z: -0.0\n      - \n        x: -1.15849359091e-16\n        y: -1.0\n        z: -0.0\n      - \n        x: 0.996545758245\n        y: -0.0830454798537\n        z: -0.0\n      - \n        x: -1.23714037168e-16\n        y: -4.74237142479e-16\n        z: 1.0\n      - \n        x: -0.316227766017\n        y: 0.948683298051\n        z: -0.0\n      - \n        x: 1.0\n        y: -8.14329318174e-15\n        z: -0.0\n  - \n    points: \n      - \n        x: 3.775\n        y: -4.205\n        z: 1.235\n      - \n        x: 3.775\n        y: -4.205\n        z: 1.235\n      - \n        x: -0.215\n        y: -4.68\n        z: 0.095\n      - \n        x: 3.395\n        y: -4.015\n        z: 1.995\n      - \n        x: 3.395\n        y: -4.585\n        z: 1.995\n      - \n        x: 3.395\n        y: -4.015\n        z: 1.995\n      - \n        x: 1.875\n        y: -4.395\n        z: 2.375\n      - \n        x: 3.205\n        y: -4.395\n        z: 0.475\n      - \n        x: 1.875\n        y: -4.585\n        z: 0.285\n      - \n        x: 2.825\n        y: -4.585\n        z: 2.185\n      - \n        x: 1.875\n        y: -4.395\n        z: 2.375\n      - \n        x: 1.495\n        y: -4.395\n        z: 0.285\n      - \n        x: -1.165\n        y: -3.825\n        z: 0.475\n      - \n        x: -0.975\n        y: -4.585\n        z: 2.565\n      - \n        x: -0.595\n        y: -3.73\n        z: 2.185\n      - \n        x: 0.925\n        y: -4.585\n        z: 2.565\n      - \n        x: 1.875\n        y: -4.585\n        z: 0.285\n      - \n        x: 1.495\n        y: -4.395\n        z: 0.285\n      - \n        x: 1.495\n        y: -4.395\n        z: 0.285\n      - \n        x: 3.395\n        y: -4.585\n        z: 0.665\n      - \n        x: 3.585\n        y: -4.205\n        z: 1.805\n      - \n        x: 3.585\n        y: -4.205\n        z: 1.805\n      - \n        x: 3.585\n        y: -4.205\n        z: 1.805\n      - \n        x: 3.775\n        y: -4.205\n        z: 1.425\n      - \n        x: 3.395\n        y: -4.015\n        z: 0.665\n    normals: \n      - \n        x: 0.872871560944\n        y: -0.218217890236\n        z: -0.436435780472\n      - \n        x: 0.832050294338\n        y: -3.28160913038e-14\n        z: -0.554700196225\n      - \n        x: 1.55820775386e-15\n        y: -1.0\n        z: 1.59816179883e-16\n      - \n        x: 0.0475651494154\n        y: 0.998868137724\n        z: 5.70126035636e-18\n      - \n        x: 0.316227766017\n        y: -1.03456585623e-14\n        z: 0.948683298051\n      - \n        x: 0.19245008973\n        y: 0.19245008973\n        z: 0.962250448649\n      - \n        x: 0.0660098361984\n        y: 0.561083607687\n        z: 0.825122952481\n      - \n        x: 0.141421356237\n        y: -9.29660008431e-15\n        z: -0.989949493661\n      - \n        x: 0.107832773203\n        y: 0.215665546407\n        z: -0.970494958831\n      - \n        x: 0.196116135138\n        y: -3.59958398935e-15\n        z: 0.980580675691\n      - \n        x: 0.0599760143904\n        y: 0.539784129514\n        z: 0.839664201466\n      - \n        x: 0.0465242105199\n        y: 0.465242105199\n        z: -0.883959999879\n      - \n        x: -1.01883201649e-15\n        y: 0.4472135955\n        z: -0.894427191\n      - \n        x: -0.970142500145\n        y: -0.242535625036\n        z: -0.0\n      - \n        x: -6.75015598972e-16\n        y: 1.0\n        z: -0.0\n      - \n        x: -3.4842584607e-16\n        y: 0.4472135955\n        z: 0.894427191\n      - \n        x: 0.0890870806375\n        y: 0.178174161275\n        z: -0.979957887012\n      - \n        x: 0.0653720450461\n        y: 0.522976360368\n        z: -0.849836585599\n      - \n        x: 0.101015254455\n        y: 0.404061017821\n        z: -0.909137290097\n      - \n        x: 0.707106781187\n        y: -3.8802469489e-14\n        z: -0.707106781187\n      - \n        x: 0.707106781187\n        y: -1.17512025931e-14\n        z: 0.707106781187\n      - \n        x: 0.816496580928\n        y: 0.408248290464\n        z: 0.408248290464\n      - \n        x: 0.872871560944\n        y: -0.218217890236\n        z: 0.436435780472\n      - \n        x: 0.894427191\n        y: -0.4472135955\n        z: 6.0884510888e-16\n      - \n        x: 0.4472135955\n        y: 0.894427191\n        z: 2.2943849975e-16\n  - \n    points: \n      - 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  },
  {
    "path": "src/cmpcc/config/mpcParameters.yaml",
    "content": "horizon: 20\ncorridorConstrains: 1\nqVTheta: 0.005\n# 2 order:\n# stateUpper: [ 100,  3,  100,  3,  100,  3,  100,  100]\n# stateLower: [-100, -3, -100, -3, -100, -3, -0.1, -0.1]\n# 3 order:\nstateUpper: [ 100,  10,  10,  100,  10,  10,  100,  10,  10,  100,     1,  20]\nstateLower: [-100, -10, -10, -100, -10, -10, -100, -10, -10, -0.1,  -0.1, -20]\ninputUpper: [  8,  8,  8,  15]\ninputLower: [ -8, -8, -8, -15]\n\n\n# horizon: 30\n# corridorConstrains: 1\n# qVTheta: 0.2\n# # 2 order:\n# # stateUpper: [ 100,  3,  100,  3,  100,  3,  100,  100]\n# # stateLower: [-100, -3, -100, -3, -100, -3, -0.1, -0.1]\n# # 3 order:\n# stateUpper: [ 100,  10,  10,  100,  10,  10,  100,  10,  10,  100,   15,  15]\n# stateLower: [-100, -10, -10, -100, -10, -10, -100, -10, -5, -0.1,  -0.1, -15]\n# inputUpper: [  10,  10,  10,  10]\n# inputLower: [ -10, -10, -10, -10]"
  },
  {
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  },
  {
    "path": "src/cmpcc/include/bezier_base.h",
    "content": "/*\nThis header file is used to provide some basic mathematic support for the Bernstein-basis trajectory generation optimization problem. Includes:\n1-: Mapping matrix maps the coefficients of the Bernstein basis (ie. control points) to Monomial basis. The mapping matrix range from order 3 to order 10\n2-: Modulus list of the Bernstein basis to a given order. That is, pre-compute the constant-modulus (the 'n choose k' combinatorial) of the basis std::vector. \n\tTo save computation cost of frequently call this value. \n\nThe class should be initialized to a instance before the trajectory generator called. \nSeveral initializer are provided, and the instance is initialized according to the given order of the control points.\n*/\n#ifndef _BEZIER_BASE_H_\n#define _BEZIER_BASE_H_\n\n#include <stdio.h>\n#include <iostream>\n#include <string>\n#include <Eigen/Dense>\n#include <vector>\n\nclass Bernstein\n{\n\tprivate:\n\t\t\n\t\tstd::vector<Eigen::MatrixXd> MQMList_l, MQMList_u, MList, MQMList_arc, FMList;\n\t\tstd::vector<Eigen::VectorXd> CList, CvList, CaList, CjList;\n\n\t\tEigen::MatrixXd FM, MQM_l, MQM_u, M, MQM_arc;\n\t\tEigen::VectorXd C, Cv, Ca, Cj;\n\n\t\tint order_min = 3, order_max = 12;  // The order of the polynomial in each segment, also the number of control points used in each segment\n\t\tint traj_order, ctrl_num1D;\n\t\tdouble min_order;                    // The order to which we minimize.   1 -- velocity, 2 -- acceleration, 3 -- jerk, 4 -- snap    \n\n\tpublic:\n\t\tBernstein(){}; // Empty Constructor\n\t\tBernstein(double min_order); // Constructor with fixed objective order\n\t\t~Bernstein(){};\n\n\t\t//void setOrder(double min_order);\n\t\tEigen::MatrixXd CholeskyDecomp(Eigen::MatrixXd Q); // return square root F of Q; Q = F' * F\n\n\t\tstd::vector<Eigen::MatrixXd> getMs(){ return MList; };\n\t\tstd::vector<Eigen::MatrixXd> getMQM_ls(){ return MQMList_l; };\n\t\tstd::vector<Eigen::MatrixXd> getMQM_us(){ return MQMList_u; };\n\t\tstd::vector<Eigen::MatrixXd> getMQM_arcs(){ return MQMList_arc; };\n\t\tstd::vector<Eigen::MatrixXd> getFMs(){ return FMList; };\n\t\tstd::vector<Eigen::VectorXd> getCs(){ return CList; };\n\t\tstd::vector<Eigen::VectorXd> getC_vs(){ return CvList; };\n\t\tstd::vector<Eigen::VectorXd> getC_as(){ return CaList; };\n\t\tstd::vector<Eigen::VectorXd> getC_js(){ return CjList; };\n\n\t\tEigen::MatrixXd getM(){ return M; };\n\t\tEigen::MatrixXd getMQM_l(){ return MQM_l; };\n\t\tEigen::MatrixXd getMQM_u(){ return MQM_u; };\n\t\tEigen::MatrixXd getMQM_arc(){ return MQM_arc; };\n\t\tEigen::MatrixXd getFM(){ return FM; };\n\t\tEigen::VectorXd getC(){ return C; };\n\t\tEigen::VectorXd getC_v(){ return Cv; };\n\t\tEigen::VectorXd getC_a(){ return Ca; };\n\t\tEigen::VectorXd getC_j(){ return Cj; };\n\n\t\tvoid setFixedOrder(int traj_order_)\n\t\t{\n\t\t\ttraj_order = traj_order_;\n\t\t    ctrl_num1D = traj_order + 1;\n\n\t\t\tMQM_l = getMQM_ls()[traj_order];\n\t\t    MQM_u = getMQM_us()[traj_order];\n\t\t    C     = getCs()  [traj_order];\n\t\t    Cv    = getC_vs()[traj_order];\n\t\t    Ca    = getC_as()[traj_order];\n\t\t    Cj    = getC_js()[traj_order];\n\t\t};\n\n\t\tinline Eigen::Vector3d getPos(const Eigen::MatrixXd & Coeff, const int & k, const double & s)\n\t\t{\n\t\t    Eigen::Vector3d ret = Eigen::VectorXd::Zero(3);\n\n\t\t    for(int i = 0; i < 3; i++)\n\t\t        for(int j = 0; j < ctrl_num1D; j++)\n\t\t            ret(i) += C(j) * Coeff(k, i * ctrl_num1D + j) * pow(s, j) * pow((1 - s), (traj_order - j) ); \n\n\t\t    return ret;\n\t\t};\n\n\n\t\tinline Eigen::Vector3d getVel(const Eigen::MatrixXd & Coeff, const int & k, const double & s)\n\t\t{\n\t\t    Eigen::Vector3d ret = Eigen::VectorXd::Zero(3);\n\n\t\t    for(int i = 0; i < 3; i++)\n\t\t        for(int j = 0; j < ctrl_num1D; j++)\n\t\t            if(j < ctrl_num1D - 1 )\n\t\t                ret(i) += Cv(j) * traj_order \n\t\t                        * ( Coeff(k, i * ctrl_num1D + j + 1) - Coeff(k, i * ctrl_num1D + j))\n\t\t                          * pow(s, j) * pow((1 - s), (traj_order - j - 1) ); \n\n\t\t    return ret;\n\t\t};\n\n\t\tinline Eigen::Vector3d getAcc(const Eigen::MatrixXd & Coeff, const int & k, const double & s)\n\t\t{\n\t\t    Eigen::Vector3d ret = Eigen::VectorXd::Zero(3);\n\n\t\t    for(int i = 0; i < 3; i++)\n\t\t        for(int j = 0; j < ctrl_num1D; j++)\n\t\t            if(j < ctrl_num1D - 2 )\n\t\t              ret(i) += Ca(j) * traj_order * (traj_order - 1) \n\t\t                        * ( Coeff(k, i * ctrl_num1D + j + 2) - 2 * Coeff(k, i * ctrl_num1D + j + 1) + Coeff(k, i * ctrl_num1D + j))\n\t\t                          * pow(s, j) * pow((1 - s), (traj_order - j - 2) );                         \n\n\t\t    return ret;\n\t\t};\n\n\t\tinline Eigen::Vector3d getPosFromBezier(const Eigen::MatrixXd & polyCoeff, const double & t_now, const int & seg_now )\n\t\t{\n\t\t    Eigen::Vector3d ret = Eigen::VectorXd::Zero(3);\n\n\t\t    for(int i = 0; i < 3; i++)\n\t\t        for(int j = 0; j < ctrl_num1D; j++)\n\t\t            ret(i) += C(j) * polyCoeff(seg_now, i * ctrl_num1D + j) * pow(t_now, j) * pow((1 - t_now), (traj_order - j) ); \n\n\t\t    return ret;  \n\t\t};\n};\n\n#endif"
  },
  {
    "path": "src/cmpcc/include/corridor.h",
    "content": "#ifndef PROJECT_CORRIDOR_H\n#define PROJECT_CORRIDOR_H\n#include <Eigen/Core>\n#include <yaml-cpp/yaml.h>\n\nnamespace ft{\n    struct Poly{\n        std::vector<Eigen::Vector3d> points;\n        std::vector<Eigen::Vector3d> normals;\n        std::vector<double> starter, ender;\n    };\n\n    struct PolyLine{\n        Eigen::Matrix<double, 1, 6> line;\n        std::vector<Eigen::Vector3d> points;  // intersecting point\n        std::vector<int> intersecting_line_id;  // \n    };\n\n    typedef Eigen::MatrixX4d Polyhedron;\n\n    class Corridor{\n    public:\n        std::vector<Poly> polys;\n        std::vector<Eigen::MatrixXd> Cn;\n        std::vector<Eigen::MatrixXd> dn;\n        std::vector<Eigen::MatrixXd> dn_neg;\n\n        // tmp variables:\n        Polyhedron tunnel;\n        double tunnelArea;\n\n        Corridor(/* args */);\n\n        void FindPolygon(const Eigen::Vector3d position, const Eigen::Vector3d tangent_line, const int corridor_id);\n    };\n\n}//namespace ft\n#endif\n"
  },
  {
    "path": "src/cmpcc/include/display_msgs.h",
    "content": "#ifndef PROJECT_DISPLAT_MSGS_H\n#define PROJECT_DISPLAT_MSGS_H\n\n#include \"map.h\"\n#include <ros/ros.h>\n#include <nav_msgs/Path.h>\n#include <decomp_ros_msgs/PolyhedronArray.h>\n#include <decomp_ros_msgs/Polyhedron.h>\n#include <geometry_msgs/PoseStamped.h>\n#include <visualization_msgs/Marker.h>\n#include <visualization_msgs/MarkerArray.h>\n#include <tf2_geometry_msgs/tf2_geometry_msgs.h>\n#include <Eigen/Sparse>\n\nnamespace ft{\n    class DisplayMsgs\n    {\n    private:\n        int horizon;\n        geometry_msgs::Point pt, nm;\n        decomp_ros_msgs::Polyhedron poly_msg;\n    public:\n        ft::Map &map;\n        nav_msgs::Path refTraj_msg;\n        nav_msgs::Path trajPred_msg;\n        decomp_ros_msgs::PolyhedronArray corridor_array_msg;\n        decomp_ros_msgs::PolyhedronArray tunnel_array_msg;\n        decomp_ros_msgs::PolyhedronArray empty_poly_msg;\n        visualization_msgs::Marker drone_msg;\n        visualization_msgs::Marker theta_msg;\n\n        DisplayMsgs(Map &map_, int horizon_);\n        void displayRefTraj();\n        void displayCorridors();\n        void displayOneTunnel(int horizon_, double theta_);\n        void displayDrone(Eigen::SparseMatrix<double> &state);\n        void displayTheta(Eigen::SparseMatrix<double> &state);\n        void displayPredict(Eigen::SparseMatrix<double> &statePredict);\n        void clearTunnels();\n        void pubTunnels(ros::Publisher& pub);\n    };\n    \n}//namespace ft\n\n#endif"
  },
  {
    "path": "src/cmpcc/include/map.h",
    "content": "#ifndef PROJECT_MAP_H\n#define PROJECT_MAP_H\n\n#include <Eigen/Core>\n#include <cmath>\n#include <iostream>\n#include <yaml-cpp/yaml.h>\n#include \"corridor.h\"\n#include \"bezier_base.h\"\n\nnamespace ft {\n//    class Map\n    class Map {\n    private:\n        int num_segment, traj_order, K_max;\n        double s_step, global_traj_time;\n        std::vector<double> range, K_data;\n        std::vector<double> theta_sample;\n        std::vector<Eigen::Vector3d> pos_sample;\n        Eigen::MatrixXd coef, time, time_acc, a_data, b_data, s_data;\n\n        Bernstein bezier_basis = Bernstein(3.0);\n        \n    public:\n        double thetaMax;\n        Corridor corridor;\n\n        Map();\n        void findRangeForEachPoly();\n        double findNearestTheta(Eigen::Vector3d & position);\n        double findNearestTheta(double theta, Eigen::Vector3d & position);\n        \n        void getGlobalCommand(double t, \n            Eigen::Vector3d & position);\n        void getGlobalCommand(double t, \n            Eigen::Vector3d & position, \n            Eigen::Vector3d & velocity);\n        void getGlobalCommand(double t,\n            Eigen::Vector3d & position, \n            Eigen::Vector3d & velocity, \n            Eigen::Vector3d & acceleration);\n    };\n}\n\n#endif //PROJECT_MAP_H\n"
  },
  {
    "path": "src/cmpcc/include/model.h",
    "content": "//\n// Created by soviet on 19-11-17.\n//\n\n#ifndef PROJECT_MODEL_H\n#define PROJECT_MODEL_H\n\n#include <Eigen/Sparse>\n\nnamespace ft{\n\n// class Model\n    class Model{\n        public:\n\n        static constexpr unsigned long freq = 20; // frequency of discretization\n        static constexpr double ts = 1.0/freq;\n        static constexpr int numOrder = 3;        // number of orders\n        static constexpr int numDimention = 3;    // number of dimentions\n        static constexpr int numState = numOrder*(numDimention+1); // number of states\n        static constexpr int numInput = numDimention+1; // number of inputs\n\n        Eigen::SparseMatrix<double> Ad;\n        Eigen::SparseMatrix<double> Bd;\n\n        Model (double T = ts);\n    };\n\n}//namespace ft -> flight_tunnel\n\n\n\n#endif //PROJECT_MODEL_H\n"
  },
  {
    "path": "src/cmpcc/include/mpc_solver.h",
    "content": "#ifndef PROJECT_MPC_SOLVER_H\n#define PROJECT_MPC_SOLVER_H\n#include \"model.h\"\n#include \"map.h\"\n#include \"osqp_interface.h\"\n#include \"display_msgs.h\"\n\nnamespace ft{\n    class MpcSolver\n    {\n    private:\n        int corridorConstrains; // set to 1 if considering corridor constrains\n        double qVTheta; //cost for v_theta : set in config/qVTheta.yaml\n        std::vector<double> stateUpper;\n        std::vector<double> stateLower;\n        std::vector<double> inputUpper;\n        std::vector<double> inputLower;\n        Eigen::SparseMatrix<double> Inu;\n        int numState = Model::numState; // ns\n        int numInput = Model::numInput; // ni\n        /**\n         * Q = BB'(Qk)BB\n         * c = BB'(Qk'AA x0 + qk)\n         * X = BB * U + AA * x0\n         * **/\n        // matrices for qpsolver: \n        Eigen::SparseMatrix<double> Q ; // N*ns * N*ns\n        Eigen::SparseMatrix<double> c ; // N*ni * 1\n        Eigen::SparseMatrix<double> A ;\n        Eigen::SparseMatrix<double> b ;\n        Eigen::SparseMatrix<double> C ;\n        Eigen::SparseMatrix<double> clow;\n        Eigen::SparseMatrix<double> cupp;\n        // xlow : ul; xupp : uu;\n        // const matrices: \n        Eigen::SparseMatrix<double> AA; // N*ns * ns\n        Eigen::SparseMatrix<double> BB; // N*ns * N*ni\n        Eigen::SparseMatrix<double> AAT;//AA.transpose()\n        Eigen::SparseMatrix<double> BBT;//BB.transpose()\n        Eigen::SparseMatrix<double> In; // ns   *   ns : tiny diag\n        Eigen::SparseMatrix<double> xu; // N*ns *    1 : state upper\n        Eigen::SparseMatrix<double> xl; // N*ns *    1 : state lower\n        Eigen::SparseMatrix<double> uu; // N*ns *    1 : input upper\n        Eigen::SparseMatrix<double> ul; // N*ns *    1 : input lower\n        // temp matrices for each horizon: \n        Eigen::SparseMatrix<double> Qn; // ns   *   ns\n        Eigen::SparseMatrix<double> qn; // ns   *    1\n        Eigen::SparseMatrix<double> Cn; // n?   *   ns\n        Eigen::SparseMatrix<double> Cnb;// n?   *    1 \n        Eigen::SparseMatrix<double> Qk; // N*ns * N*ns : blkdiag(Q1,Q2,Q3,...)\n        Eigen::SparseMatrix<double> qk; // N*ns *    1 : [q1;q2;q3;...]\n        Eigen::SparseMatrix<double> Ck; // n??  *   ns : blkdiag(C1,C2,C3,...)\n        Eigen::SparseMatrix<double> Ckb;// n??  *    1\n\n        osqp::OSQPInterface osqpInterface; // qpSolver interface\n\n    public:\n        bool initStatus;\n        int horizon; // N : set in config/qVTheta.yaml\n        ft::Map map;\n        ft::Model model;\n        // solution: \n        Eigen::SparseMatrix<double> inputPredict;\n        Eigen::SparseMatrix<double> statePredict;\n        DisplayMsgs * displayPtr;\n\n        MpcSolver();\n        ~MpcSolver();\n        void calculateCost(double theta, int horizon_);\n        void calculateConstrains(Eigen::SparseMatrix<double> &stateTmp, int horizon_);\n        int solveMpcQp(Eigen::SparseMatrix<double> &stateTmp);\n        void printMatrices(); // just for debug\n    };\n}//namespace ft\n\n#endif"
  },
  {
    "path": "src/cmpcc/include/my_spline.h",
    "content": "//\n// Created by soviet on 19-11-10.\n//\n\n#ifndef PROJECT_MY_SPLINE_H\n#define PROJECT_MY_SPLINE_H\n\n#include <cstdio>\n#include <cassert>\n#include <vector>\n#include <algorithm>\n#include <cmath>\n\nnamespace ft\n{\n    namespace tk\n    {\n        // band matrix solver\n        class band_matrix\n        {\n        private:\n            std::vector< std::vector<double> > m_upper;  // upper band\n            std::vector< std::vector<double> > m_lower;  // lower band\n        public:\n            band_matrix() {};                             // constructor\n            band_matrix(int dim, int n_u, int n_l);       // constructor\n            ~band_matrix() {};                            // destructor\n            void resize(int dim, int n_u, int n_l);      // init with dim,n_u,n_l\n            int dim() const;                             // matrix dimension\n            int num_upper() const\n            {\n                return m_upper.size()-1;\n            }\n            int num_lower() const\n            {\n                return m_lower.size()-1;\n            }\n            // access operator\n            double & operator () (int i, int j);            // write\n            double   operator () (int i, int j) const;      // read\n            // we can store an additional diogonal (in m_lower)\n            double& saved_diag(int i);\n            double  saved_diag(int i) const;\n            void lu_decompose();\n            std::vector<double> r_solve(const std::vector<double>& b) const;\n            std::vector<double> l_solve(const std::vector<double>& b) const;\n            std::vector<double> lu_solve(const std::vector<double>& b,\n                                         bool is_lu_decomposed=false);\n\n        };\n\n\n        // spline interpolation\n        class spline\n        {\n        public:\n            enum bd_type {\n                first_deriv = 1,\n                second_deriv = 2\n            };\n\n        private:\n            std::vector<double> m_x,m_y;            // x,y coordinates of points\n            // interpolation parameters\n            // f(x) = a*(x-x_i)^3 + b*(x-x_i)^2 + c*(x-x_i) + y_i\n            std::vector<double> m_a,m_b,m_c;        // spline coefficients\n            double  m_b0, m_c0;                     // for left extrapol\n            bd_type m_left, m_right;\n            double  m_left_value, m_right_value;\n            bool    m_force_linear_extrapolation;\n\n        public:\n            // set default boundary condition to be zero curvature at both ends\n            spline();\n\n            // optional, but if called it has to come be before set_points()\n            void set_boundary(bd_type left, double left_value,\n                              bd_type right, double right_value,\n                              bool force_linear_extrapolation=false);\n            void set_points(const std::vector<double>& x,\n                            const std::vector<double>& y, bool cubic_spline=true);\n            // double operator() (double x) const;\n            double operator() (double x, int dd=0) const;\n        };\n\n    } // namespace tk\n    \n} // namespace ft\n\n#endif //PROJECT_MY_SPLINE_H"
  },
  {
    "path": "src/cmpcc/include/osqp_interface.h",
    "content": "#ifndef PROJECT_OSQP_INTERFACE_H\n#define PROJECT_OSQP_INTERFACE_H\n#include <osqp/osqp.h>\n#include <Eigen/Sparse>\n\nnamespace osqp{\nclass OSQPInterface{\nprivate:\n    c_int   P_nnz;\n    c_int   A_nnz;\n    // Exitflag\n    c_int exitflag = 0;\n\n    // Workspace structures\n    OSQPWorkspace *work;\n    OSQPSettings  *settings;\n    OSQPData      *data;\n\npublic:\n    OSQPInterface();\n    ~OSQPInterface();\n    int updateMatrices(\n        Eigen::SparseMatrix<double> & Q_, \n        Eigen::SparseMatrix<double> & c_, \n        Eigen::SparseMatrix<double> & A_, \n        Eigen::SparseMatrix<double> & b_, \n        Eigen::SparseMatrix<double> & C_, \n        Eigen::SparseMatrix<double> & clow_, \n        Eigen::SparseMatrix<double> & cupp_,\n        Eigen::SparseMatrix<double> & xlow_,\n        Eigen::SparseMatrix<double> & xupp_,\n        int warmStart = WARM_START\n    );\n    int solveQP();\n    int solveStatus();\n    OSQPSolution* solPtr();\n};\n} // namespace osqp\n\n\n#endif\n\n\n"
  },
  {
    "path": "src/cmpcc/include/sparse_utils.h",
    "content": "#ifndef PROJECT_SPARSE_UTILS_H\n#define PROJECT_SPARSE_UTILS_H\n\n#include <Eigen/Sparse>\nnamespace sp{\n    namespace rowMajor{\n    inline void setBlock( // be careful to call this function!\n        Eigen::SparseMatrix<double,Eigen::RowMajor> &big, \n        Eigen::SparseMatrix<double,Eigen::RowMajor> &small, //matrix small had better be really small\n        int rowIndex, int colIndex)\n        {\n        for (int k=0; k<small.outerSize(); ++k){\n            for (Eigen::SparseMatrix<double,Eigen::RowMajor>::InnerIterator it(small,k); it; ++it){\n                big.coeffRef(rowIndex+it.row(),colIndex+it.col()) = it.value();\n            }\n        }\n    };\n    inline void setRows(\n        Eigen::SparseMatrix<double,Eigen::RowMajor> &big, //n*n\n        Eigen::SparseMatrix<double,Eigen::RowMajor> &small, //matrix small must be m*n, m<=n\n        int rowIndex)\n        {\n        assert(big.cols()==small.cols());\n        big.middleRows(rowIndex, small.rows()) = small;\n    }\n    inline void setCols(\n        Eigen::SparseMatrix<double,Eigen::RowMajor> &big, \n        Eigen::SparseMatrix<double,Eigen::RowMajor> &small,\n        int colIndex)\n        {\n        assert(big.rows() == small.rows());\n        Eigen::SparseMatrix<double,Eigen::RowMajor> bigT = big.transpose();\n        bigT.middleRows(colIndex, small.cols()) = small.transpose();\n        big = bigT.transpose();\n    } \n    inline void addCols(\n        Eigen::SparseMatrix<double,Eigen::RowMajor> &big, \n        Eigen::SparseMatrix<double,Eigen::RowMajor> &small)\n        {\n        if (big.rows() == 0 && big.cols() == 0){\n            big = small;\n            return;\n        };\n        assert(big.rows() == small.rows());\n        Eigen::SparseMatrix<double,Eigen::RowMajor> bigOld = big;\n        big.resize(bigOld.rows(),bigOld.cols()+small.cols());\n        setCols(big, bigOld, 0);\n        setCols(big, small, bigOld.cols());\n    }\n\n    inline void addRows(\n        Eigen::SparseMatrix<double,Eigen::RowMajor> &big, \n        Eigen::SparseMatrix<double,Eigen::RowMajor> &small)\n        {\n        if (big.rows() == 0 && big.cols() == 0){\n            big = small;\n            return;\n        };\n        assert(big.cols() == small.cols());\n        Eigen::SparseMatrix<double,Eigen::RowMajor> bigOld(big);\n        big.resize(bigOld.rows()+small.rows(), bigOld.cols());\n        setRows(big, bigOld, 0);\n        setRows(big, small, bigOld.rows());\n    }\n\n    inline void addBlock(\n        Eigen::SparseMatrix<double,Eigen::RowMajor> &big, \n        Eigen::SparseMatrix<double,Eigen::RowMajor> &small)\n        {\n        if (big.rows() == 0 && big.cols() == 0){\n            big = small;\n            return;\n        };\n        Eigen::SparseMatrix<double,Eigen::RowMajor> up(big.rows(), big.cols()+small.cols());\n        Eigen::SparseMatrix<double,Eigen::RowMajor> down(small.rows(), big.cols() + small.cols());\n        setCols(up, big, 0);\n        setCols(down, small, big.cols());\n        addRows(up, down);\n        big = up;\n    }\n    }// namespace rowMajor\n\n    namespace colMajor{\n    inline void setBlock( // be careful to call this function!\n        Eigen::SparseMatrix<double> &big, \n        Eigen::SparseMatrix<double> &small, //matrix small had better be really small\n        int rowIndex, int colIndex)\n        {\n        for (int k=0; k<small.outerSize(); ++k){\n            for (Eigen::SparseMatrix<double>::InnerIterator it(small,k); it; ++it){\n                big.coeffRef(rowIndex+it.row(),colIndex+it.col()) = it.value();\n            }\n        }\n    };\n    inline void setCols(\n        Eigen::SparseMatrix<double> &big, //n*n\n        Eigen::SparseMatrix<double> &small, //matrix small must be m*n, m<=n\n        int colIndex)\n        {\n        assert(big.rows()==small.rows());\n        big.middleCols(colIndex, small.cols()) = small;\n    }\n    inline void setRows(\n        Eigen::SparseMatrix<double> &big, \n        Eigen::SparseMatrix<double> &small,\n        int rowIndex)\n        {\n        assert(big.cols() == small.cols());\n        Eigen::SparseMatrix<double> bigT = big.transpose();\n        bigT.middleCols(rowIndex, small.rows()) = small.transpose();\n        big = bigT.transpose();\n    } \n    inline void addCols(\n        Eigen::SparseMatrix<double> &big, \n        Eigen::SparseMatrix<double> &small)\n        {\n        if (big.rows() == 0 && big.cols() == 0){\n            big = small;\n            return;\n        };\n        assert(big.rows() == small.rows());\n        Eigen::SparseMatrix<double> bigOld = big;\n        big.resize(bigOld.rows(),bigOld.cols()+small.cols());\n        setCols(big, bigOld, 0);\n        setCols(big, small, bigOld.cols());\n    }\n\n    inline void addRows(\n        Eigen::SparseMatrix<double> &big, \n        Eigen::SparseMatrix<double> &small)\n        {\n        if (big.rows() == 0 && big.cols() == 0){\n            big = small;\n            return;\n        };\n        assert(big.cols() == small.cols());\n        Eigen::SparseMatrix<double> bigOld(big);\n        big.resize(bigOld.rows()+small.rows(), bigOld.cols());\n        setRows(big, bigOld, 0);\n        setRows(big, small, bigOld.rows());\n    }\n\n    inline void addBlock(\n        Eigen::SparseMatrix<double> &big, \n        Eigen::SparseMatrix<double> &small)\n        {\n        if (big.rows() == 0 && big.cols() == 0){\n            big = small;\n            return;\n        };\n        Eigen::SparseMatrix<double> left(big.rows()+small.rows(), big.cols());\n        Eigen::SparseMatrix<double> right(big.rows()+small.rows(), small.cols());\n        setRows(left, big, 0);\n        setRows(right, small, big.rows());\n        addCols(left, right);\n        big = left;\n    }\n    }// namespace colMajor\n}// namespace sp\n\n#endif // PROJECT_SPARSE_UTILS_H\n"
  },
  {
    "path": "src/cmpcc/launch/flyfly.launch",
    "content": "<launch>\n\n<node pkg=\"rostopic\" type=\"rostopic\" name=\"global_traj\" args=\"pub --once /tr_node/space_time_traj quadrotor_msgs/SpatialTemporalTrajectory -f $(find trr_global_planner)/config/traj.yaml\" output=\"screen\">\n<remap from=\"global_traj\" to=\"/tr_node/space_time_traj\"/>\n</node>\n<node pkg=\"rostopic\" type=\"rostopic\" name=\"corridors\" args=\"pub --once /tr_node/polyhedron_corridor_mesh decomp_ros_msgs/PolyhedronArray -f $(find trr_global_planner)/config/corridor.yaml\" output=\"screen\">\n<remap from=\"corridors\" to=\"/tr_node/polyhedron_corridor_mesh\"/>\n</node>\n<node pkg=\"flight_tunnel\" type=\"flyfly_node\" name=\"flyfly_node\" output=\"screen\"/>\n\n</launch>"
  },
  {
    "path": "src/cmpcc/launch/plot_bag.launch",
    "content": "<launch>\n    <node pkg=\"flight_tunnel\" name=\"plot_bag_node\" type=\"plot_bag_node\" output=\"screen\"> \n        <param name=\"start_time\" value=\"1593240499.118571545\"/>\n    </node>\n</launch>\n"
  },
  {
    "path": "src/cmpcc/launch/vis_bag.launch",
    "content": "<launch>\n\n<node pkg=\"rostopic\" type=\"rostopic\" name=\"corridors\" args=\"pub --once corridors decomp_ros_msgs/PolyhedronArray -f $(find flight_tunnel)/config/corridor.yaml\" output=\"screen\">\n<remap from=\"corridors\" to=\"/polyhedron_corridor_mesh\"/>\n</node>\n\n<arg name=\"map_size_x\" default=\"20.0\"/>\n<arg name=\"map_size_y\" default=\"20.0\"/>\n<arg name=\"map_size_z\" default=\" 3.0\"/>\n\n<arg name=\"init_x\" default=\"-8.0\"/>\n<arg name=\"init_y\" default=\"-8.0\"/>\n<arg name=\"init_z\" default=\" 2.0\"/>\n\n<node pkg =\"environment\" name =\"generate_map_node\" type =\"generate_map_node\" output = \"screen\">\n    <remap from=\"~odometry\"       to=\"/drone_1/visual_slam/odom\"/>\n\n    <param name=\"init_state_x\"   value=\"$(arg init_x)\"/>\n    <param name=\"init_state_y\"   value=\"$(arg init_y)\"/>\n\n    <param name=\"map/x_size\"     value=\"$(arg map_size_x)\" />\n    <param name=\"map/y_size\"     value=\"$(arg map_size_y)\" />\n    <param name=\"map/z_size\"     value=\"$(arg map_size_z)\" />\n\n    <param name=\"map/circle_num\" value=\"50\"/>        \n    <param name=\"map/obs_num\"    value=\"150\"/>        \n    <param name=\"map/resolution\" value=\"0.1\"/>        \n\n    <param name=\"ObstacleShape/lower_rad\" value=\"0.1\"/>\n    <param name=\"ObstacleShape/upper_rad\" value=\"0.7\"/>\n    <param name=\"ObstacleShape/lower_hei\" value=\"1.0\"/>\n    <param name=\"ObstacleShape/upper_hei\" value=\"3.0\"/>\n\n    <param name=\"CircleShape/lower_circle_rad\"   value=\"0.6\"/>        \n    <param name=\"CircleShape/upper_circle_rad\"   value=\"2.0\"/>        \n    \n    <param name=\"sensing/rate\"   value=\"0.5\"/>   \n\n    <param name=\"map/file_name\"   value=\"random_map.pcd\"/>      \n</node>\n\n<node pkg=\"tf\" type=\"static_transform_publisher\" name=\"set_fix_frame\" \n    args=\"1 0 0 0 0 0 1 map base_link 50\" >\n</node>\n<node pkg=\"rviz\" name=\"rviz\" type=\"rviz\" output=\"log\"\nargs=\"-d $(find environment)/config/rviz_vis.rviz\"/>\n<remap from=\"drone_1/visual_slam/odom\" to=\"drone_1/state_ukf/odom\"/>\n\n<node pkg=\"odom_visualization\" name=\"odom_visualization\" type=\"odom_visualization\" output=\"screen\">\n    <remap from=\"~odom\" to=\"/vins_estimator/imu_propagate\"/>\n    <param name=\"base_frame\"       value=\"world\"/>\n    <param name=\"target_frame\"     value=\"world\"/>\n    <param name=\"tf45\"     value=\"true\"/>\n    <param name=\"color/a\" value=\"0.8\"/>    \n    <param name=\"color/r\" value=\"1.0\"/>        \n    <param name=\"color/g\" value=\"0.0\"/>        \n    <param name=\"color/b\" value=\"0.0\"/>       \n    <param name=\"covariance_scale\" value=\"100.0\"/>       \n</node>\n\n</launch>"
  },
  {
    "path": "src/cmpcc/package.xml",
    "content": "<?xml version=\"1.0\"?>\n<package format=\"2\">\n  <name>cmpcc</name>\n  <version>0.0.0</version>\n  <description>The cmpcc package</description>\n\n  <!-- One maintainer tag required, multiple allowed, one person per tag -->\n  <!-- Example:  -->\n  <!-- <maintainer email=\"jane.doe@example.com\">Jane Doe</maintainer> -->\n  <maintainer email=\"soviet@todo.todo\">soviet</maintainer>\n\n\n  <!-- One license tag required, multiple allowed, one license per tag -->\n  <!-- Commonly used license strings: -->\n  <!--   BSD, MIT, Boost Software License, GPLv2, GPLv3, LGPLv2.1, LGPLv3 -->\n  <license>TODO</license>\n\n\n  <!-- Url tags are optional, but multiple are allowed, one per tag -->\n  <!-- Optional attribute type can be: website, bugtracker, or repository -->\n  <!-- Example: -->\n  <!-- <url type=\"website\">http://wiki.ros.org/mpcc</url> -->\n\n\n  <!-- Author tags are optional, multiple are allowed, one per tag -->\n  <!-- Authors do not have to be maintainers, but could be -->\n  <!-- Example: -->\n  <!-- <author email=\"jane.doe@example.com\">Jane Doe</author> -->\n\n\n  <!-- The *depend tags are used to specify dependencies -->\n  <!-- Dependencies can be catkin packages or system dependencies -->\n  <!-- Examples: -->\n  <!-- Use depend as a shortcut for packages that are both build and exec dependencies -->\n  <!--   <depend>roscpp</depend> -->\n  <!--   Note that this is equivalent to the following: -->\n  <!--   <build_depend>roscpp</build_depend> -->\n  <!--   <exec_depend>roscpp</exec_depend> -->\n  <!-- Use build_depend for packages you need at compile time: -->\n  <!--   <build_depend>message_generation</build_depend> -->\n  <!-- Use build_export_depend for packages you need in order to build against this package: -->\n  <!--   <build_export_depend>message_generation</build_export_depend> -->\n  <!-- Use buildtool_depend for build tool packages: -->\n  <!--   <buildtool_depend>catkin</buildtool_depend> -->\n  <!-- Use exec_depend for packages you need at runtime: -->\n  <!--   <exec_depend>message_runtime</exec_depend> -->\n  <!-- Use test_depend for packages you need only for testing: -->\n  <!--   <test_depend>gtest</test_depend> -->\n  <!-- Use doc_depend for packages you need only for building documentation: -->\n  <!--   <doc_depend>doxygen</doc_depend> -->\n  <buildtool_depend>catkin</buildtool_depend>\n  <build_depend>message_generation</build_depend>\n  <build_depend>roscpp</build_depend>\n  <build_depend>rospy</build_depend>\n  <build_depend>quadrotor_msgs</build_depend>\n  <build_export_depend>roscpp</build_export_depend>\n  <build_export_depend>rospy</build_export_depend>\n  <build_export_depend>quadrotor_msgs</build_export_depend>\n  <exec_depend>message_runtime</exec_depend>\n  <exec_depend>roscpp</exec_depend>\n  <exec_depend>rospy</exec_depend>\n  <exec_depend>quadrotor_msgs</exec_depend>\n  <build_depend>visualization_msgs</build_depend>\n  <build_export_depend>visualization_msgs</build_export_depend>\n  <exec_depend>visualization_msgs</exec_depend>\n  <build_depend>tf2</build_depend>\n  <build_export_depend>tf2</build_export_depend>\n  <exec_depend>tf2</exec_depend>\n\n\n  <!-- The export tag contains other, unspecified, tags -->\n  <export>\n    <!-- Other tools can request additional information be placed here -->\n\n  </export>\n</package>\n"
  },
  {
    "path": "src/cmpcc/src/bezier_base.cpp",
    "content": "#include \"bezier_base.h\"\nusing namespace std;\nusing namespace Eigen;\n\nMatrixXd Bernstein::CholeskyDecomp(MatrixXd Q) // return square root F of Q; Q = F' * F\n{\n\tMatrixXd F, Ft;\n\tEigen::LDLT< MatrixXd > ldlt(Q);\n    F = ldlt.matrixL();\n    F = ldlt.transpositionsP().transpose() * F;\n    F *= ldlt.vectorD().array().sqrt().matrix().asDiagonal();\n\tFt = F.transpose();\n\n\treturn Ft;\n}\n\nBernstein::Bernstein(double min_order_ = 3.0)\n{\n\tmin_order = min_order_; \n\tconst static auto factorial = [](int n)\n\t{\n        int fact = 1;\n\n        for(int i = n; i > 0 ; i--)\n          fact *= i;\n\n        return fact; \n\t};\n\n\tconst static auto combinatorial = [](int n, int k) // for calculate n choose k combination problem\n\t{\n\t\treturn factorial(n) / (factorial(k) * factorial(n - k));\n\t};\n\n\tCList.clear();\n\tCvList.clear();\n\tCaList.clear();\n\tCjList.clear();\n\t\n\tfor(int order = 0; order <= order_max; order++)\n\t{\t\n\t\tMatrixXd M;   // Mapping matrix, used to map the coefficients of the bezier curve to a monomial polynomial .\n\n\t\tMatrixXd Q_l, Q_u, Q_arc;  // Cost Hessian matrix in each block of the objective. No scale, only meta elements .\n\t\tMatrixXd MQM_l, MQM_u, MQM_arc;     // M' * Q * M in each block of the objective. No scale, only meta elements .\n\t\t\n\t\tVectorXd C;   // Position coefficients vector, used to record all the pre-compute 'n choose k' combinatorial for the bernstein coefficients .\n\t\tVectorXd C_v; // Velocity coefficients vector.\n\t\tVectorXd C_a; // Acceleration coefficients vector.\n\t\tVectorXd C_j; // Acceleration coefficients vector.\n\n\t\tint poly_num1D = order + 1; \n\t\tM.resize(order + 1, order + 1);\n\t\tQ_l   = MatrixXd::Zero(order + 1, order + 1);\n\t\tQ_u   = MatrixXd::Zero(order + 1, order + 1);\n\t\tQ_arc = MatrixXd::Zero(order + 1, order + 1);\n\t\t//Q   = MatrixXd::Zero(order + 1, order + 1);\n\t\t\n\t\tC.resize  (order + 1);\n\t\tC_v.resize(order    );\n\t\t\n\t\tif(order > 1)\n\t\t\tC_a.resize(order - 1);\n\n\t\tif(order > 2)\n\t\t\tC_j.resize(order - 2);\n\n\t\tint min_order_l = floor(min_order);\n\t\tint min_order_u = ceil (min_order);\n\n\t\tif( poly_num1D > min_order_l )\n\t\t{\n\t\t\tfor( int i = min_order_l; i < poly_num1D; i ++ )\n\t\t\t{\n\t\t    \tfor(int j = min_order_l; j < poly_num1D; j ++ )\n\t\t    \t{\n\t\t      \t\tint coeff = 1.0;\n\t\t            int _d = min_order_l - 1;\n\t\t                \n\t\t            while(_d >= 0)\n\t\t            {\n\t\t                coeff = coeff * (i - _d) * (j - _d);\n\t\t                _d -= 1;\n\t\t            }\n\n\t\t            Q_l(i,j) = double(coeff) / double(i + j - 2 * min_order_l + 1);\n\t\t    \t}\n\t\t  \t}\n\t\t}\n\n\t\tif( poly_num1D > min_order_u )\n\t\t{\n\t\t\tfor( int i = min_order_u; i < poly_num1D; i ++ )\n\t\t\t{\n\t\t    \tfor(int j = min_order_u; j < poly_num1D; j ++ )\n\t\t    \t{\n\t\t      \t\tint coeff = 1.0;\n\t\t            int _d = min_order_u - 1;\n\t\t                \n\t\t            while(_d >= 0)\n\t\t            {\n\t\t                coeff = coeff * (i - _d) * (j - _d);\n\t\t                _d -= 1;\n\t\t            }\n\n\t\t            Q_u(i,j) = double(coeff) / double(i + j - 2 * min_order_u + 1);\n\t\t    \t}\n\t\t  \t}\n\t\t}\n\n\t\tfor( int i = 1; i < poly_num1D; i ++ )\n\t\t    for( int j = 1; j < poly_num1D; j ++ )\n\t\t        Q_arc( i, j ) = (double)i * j / (double)(i + j - 1);\n\n\t\tif(min_order_l == min_order_u)\n\t\t{\n\t\t\tQ_l = 0.0 * Q_l;\n\t\t\tQ_u = 1.0 * Q_u;\n\t\t}\n\t\telse\n\t\t{\n\t\t\tQ_l = (min_order_u - min_order)   * Q_l;\n\t\t\tQ_u = (min_order   - min_order_l) * Q_u;\n\t\t}\n\n\t \tswitch(order)\n\t\t{\t\n\t\t\tcase 0: \n\t\t\t{\n\t\t\t\tM << 1;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tcase 1: \n\t\t\t{\n\t\t\t\tM <<  1,  0,\n\t\t\t\t\t -1,  1;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tcase 2:\n\t\t\t{\n\t\t\t\tM <<  1,  0,  0,\n\t\t\t\t\t -2,  2,  0,\n\t\t\t\t\t  1, -2,  1;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tcase 3: \n\t\t\t{\n\t\t\t\tM <<  1,  0,  0,  0,\n\t\t\t\t\t -3,  3,  0,  0,\n\t\t\t\t\t  3, -6,  3,  0,\n\t\t\t\t\t -1,  3, -3,  1;\t\n\t\t\t\tbreak;\n\n\t\t\t}\n\t\t\tcase 4:\n\t\t\t{\n\t\t\t\tM <<  1,   0,   0,   0,  0,\n\t\t\t\t\t -4,   4,   0,   0,  0,\n\t\t\t\t\t  6, -12,   6,   0,  0,\n\t\t\t\t\t -4,  12, -12,   4,  0,\n\t\t\t\t\t  1,  -4,   6,  -4,  1;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tcase 5:\n\t\t\t{\n\t\t\t\tM << 1,   0,   0,   0,  0,  0,\n\t\t\t\t\t-5,   5,   0,   0,  0,  0,\n\t\t\t\t\t10, -20,  10,   0,  0,  0,\n\t\t\t\t   -10,  30, -30,  10,  0,  0,\n\t\t\t\t     5, -20,  30, -20,  5,  0,\n\t\t\t\t    -1,   5, -10,  10, -5,  1;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tcase 6:\n\t\t\t{\t\n\n\t\t\t\tM << 1,   0,   0,   0,   0,  0,  0,\n\t\t\t\t\t-6,   6,   0,   0,   0,  0,  0,\n\t\t\t\t\t15, -30,  15,   0,   0,  0,  0,\n\t\t\t\t   -20,  60, -60,  20,   0,  0,  0,\n\t\t\t\t    15, -60,  90, -60,  15,  0,  0,\n\t\t\t\t    -6,  30, -60,  60, -30,  6,  0,\n\t\t\t\t     1,  -6,  15, -20,  15, -6,  1;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tcase 7:\n\t\t\t{\n\t\t\t\tM << 1,    0,    0,    0,    0,   0,   0,   0,\n\t\t\t\t    -7,    7,    0,    0,    0,   0,   0,   0,\n\t\t\t\t    21,  -42,   21,    0,    0,   0,   0,   0,\n\t\t\t\t   -35,  105, -105,   35,    0,   0,   0,   0, \n\t\t\t\t    35, -140,  210, -140,   35,   0,   0,   0,\n\t\t\t\t   -21,  105, -210,  210, -105,  21,   0,   0,\n\t\t\t\t     7,  -42,  105, -140,  105, -42,   7,   0,\n\t\t\t\t    -1,    7,  -21,   35,  -35,  21,  -7,   1;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tcase 8:\n\t\t\t{\n\t\t\t\tM << 1,    0,    0,    0,    0,    0,   0,   0,   0,\n\t\t\t\t    -8,    8,    0,    0,    0,    0,   0,   0,   0,\n\t\t\t\t    28,  -56,   28,    0,    0,    0,   0,   0,   0,\n\t\t\t\t   -56,  168, -168,   56,    0,    0,   0,   0,   0, \n\t\t\t\t    70, -280,  420, -280,   70,    0,   0,   0,   0,\n\t\t\t\t   -56,  280, -560,  560, -280,   56,   0,   0,   0,\n\t\t\t\t    28, -168,  420, -560,  420, -168,  28,   0,   0,\n\t\t\t\t    -8,   56, -168,  280, -280,  168, -56,   8,   0,\n\t\t\t\t     1,   -8,   28,  -56,   70,  -56,  28,  -8,   1;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tcase 9:\n\t\t\t{\n\t\t\t\tM << 1,    0,     0,     0,     0,    0,    0,     0,     0,    0,\n\t\t\t\t    -9,    9,     0,     0,     0,    0,    0,     0,     0,    0, \n\t\t\t\t    36,  -72,    36,     0,     0,    0,    0,     0,     0,    0, \n\t\t\t\t   -84,  252,  -252,    84,     0,    0,    0,     0,     0,    0, \n\t\t\t\t   126, -504,   756,  -504,   126,    0,    0,     0,     0,    0,\n\t\t\t\t  -126,  630, -1260,  1260,  -630,  126,    0,     0,     0,    0,\n\t\t\t\t    84, -504,  1260, -1680,  1260, -504,   84,     0,     0,    0,\n\t\t\t\t   -36,  252,  -756,  1260, -1260,  756, -252,    36,     0,    0,\n\t\t\t\t     9,  -72,   252,  -504,   630, -504,  252,   -72,     9,    0,\n\t\t\t\t    -1,    9,   -36,    84,  -126,  126,  -84,    36,    -9,    1;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tcase 10:\n\t\t\t{\n\t\t\t\tM <<  1,     0,     0,     0,      0,     0,    0,     0,     0,    0,   0,\n\t\t\t\t    -10,    10,     0,     0,      0,     0,    0,     0,     0,    0,   0,\n\t\t\t\t     45,   -90,    45,     0,      0,     0,    0,     0,     0,    0,   0,\n\t\t\t\t   -120,   360,  -360,   120,      0,     0,    0,     0,     0,    0,   0,\n\t\t\t\t    210,  -840,  1260,  -840,    210,     0,    0,     0,     0,    0,   0,\n\t\t\t\t   -252,  1260, -2520,  2520,  -1260,   252,    0,     0,     0,    0,   0,\n\t\t\t\t    210, -1260,  3150, -4200,   3150, -1260,  210,     0,     0,    0,   0,\n\t\t\t\t   -120,  840,  -2520,  4200,  -4200,  2520, -840,   120,     0,    0,   0,\n\t\t\t\t     45, -360,   1260, -2520,   3150, -2520, 1260,  -360,    45,    0,   0,\n\t\t\t\t    -10,   90,   -360,   840,  -1260,  1260, -840,   360,   -90,   10,   0,\n\t\t\t\t      1,  -10,     45,  -120,    210,  -252,  210,  -120,    45,  -10,   1;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tcase 11:\n\t\t\t{\n\t\t\t\tM <<  1,     0,    0,      0,      0,      0,     0,     0,     0,    0,   0,  0,\n\t\t\t\t    -11,    11,    0,      0,      0,      0,     0,     0,     0,    0,   0,  0,\n\t\t\t\t     55,  -110,   55,      0,      0,      0,     0,     0,     0,    0,   0,  0,\n\t\t\t\t   -165,   495, -495,    165,      0,      0,     0,     0,     0,    0,   0,  0,\n\t\t\t\t    330, -1320, 1980,  -1320,    330,      0,     0,     0,     0,    0,   0,  0,\n\t\t\t\t   -462,  2310, -4620,  4620,  -2310,    462,     0,     0,     0,    0,   0,  0,\n\t\t\t\t    462, -2772,  6930, -9240,   6930,  -2772,   462,     0,     0,    0,   0,  0,\n\t\t\t\t   -330,  2310, -6930, 11550, -11550,   6930, -2310,   330,     0,    0,   0,  0,\n\t\t\t\t    165, -1320,  4620, -9240,  11550,  -9240,  4620, -1320,   165,    0,   0,  0,\n\t\t\t\t    -55,   495, -1980,  4620,  -6930,   6930, -4620,  1980,  -495,   55,   0,  0,\n\t\t\t\t     11,  -110,   495, -1320,   2310,  -2772,  2310, -1320,   495, -110,  11,  0,\n\t\t\t\t     -1,    11,   -55,   165,   -330,    462,  -462,   330,  -165,   55, -11,  1;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tcase 12:\n\t\t\t{\n\t\t\t\tM <<  1,     0,     0,      0,      0,      0,     0,     0,     0,    0,    0,   0,   0,\n\t\t\t\t    -12,    12,     0,      0,      0,      0,     0,     0,     0,    0,    0,   0,   0,\n\t\t\t\t     66,  -132,    66,      0,      0,      0,     0,     0,     0,    0,    0,   0,   0,\n\t\t\t\t   -220,   660,  -660,    220,      0,      0,     0,     0,     0,    0,    0,   0,   0,\n\t\t\t\t    495, -1980,  2970,  -1980,    495,      0,     0,     0,     0,    0,    0,   0,   0, \n\t\t\t\t   -792,  3960, -7920,   7920,  -3960,    792,     0,     0,     0,    0,    0,   0,   0,\n\t\t\t\t    924, -5544, 13860, -18480,  13860,  -5544,   924,     0,     0,    0,    0,   0,   0,\n\t\t\t\t   -792,  5544,-16632,  27720, -27720,  16632, -5544,   792,     0,    0,    0,   0,   0,\n\t\t\t\t    495, -3960, 13860, -27720,  34650, -27720, 13860, -3960,   495,    0,    0,   0,   0,\n\t\t\t\t   -220,  1980, -7920,  18480, -27720,  27720,-18480,  7920, -1980,  220,    0,   0,   0,\n\t\t\t\t     66,  -660,  2970,  -7920,  13860, -16632, 13860, -7920,  2970, -660,   66,   0,   0,\n\t\t\t\t    -12,   132,  -660,   1980,  -3960,   5544, -5544,  3960, -1980,  660, -132,  12,   0,\n\t\t\t\t      1,   -12,    66,   -220,    495,   -792,   924,  -792,   495, -220,   66, -12,   1;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t\t\n\t\tMList.push_back(M);\n\t\tMQM_l   = M.transpose() * Q_l   * M; // Get the cost block after mapping the coefficients\n\t\tMQM_u   = M.transpose() * Q_u   * M; // Get the cost block after mapping the coefficients\n\t\tMQM_arc = M.transpose() * Q_arc * M; // Get the cost block after mapping the coefficients\n\n\t\t/*MatrixXd F  = CholeskyDecomp(Q);\n\t\tMatrixXd FM = F * M;*/\n\n\t\tMQMList_l.push_back(MQM_l);\n\t\tMQMList_u.push_back(MQM_u);\n\t\tMQMList_arc.push_back(MQM_arc);\n\n\t\tint n = order;\n\t\tfor(int k = 0; k <= n; k ++ )\n\t\t{\n\t\t\tC(k)   = combinatorial(n, k);\n\t\t\t\n\t\t\tif( k <= (n - 1) )\n\t\t\t\tC_v(k) = combinatorial(n - 1, k);\n\t\t\tif( k <= (n - 2) )\n\t\t\t\tC_a(k) = combinatorial(n - 2, k);\n\t\t\tif( k <= (n - 3) )\n\t\t\t\tC_j(k) = combinatorial(n - 3, k);\n\t\t}\n\n\t\tCList.push_back(C);\n\t\tCvList.push_back(C_v);\n\t\tCaList.push_back(C_a);\n\t\tCjList.push_back(C_j);\n\t}\n}"
  },
  {
    "path": "src/cmpcc/src/corridor.cpp",
    "content": "#include <ctime>\n#include <iostream>\n#include <Eigen/Geometry>\n#include <ros/package.h>\n#include \"corridor.h\"\n\nusing namespace std;\n\nnamespace ft{\n\n    Corridor::Corridor(){\n        std::string path = ros::package::getPath(\"cmpcc\")+\"/config/corridor.yaml\";\n        YAML::Node polyData = YAML::LoadFile(path);\n        Eigen::Vector3d point = Eigen::MatrixXd::Zero(3,1);\n        Eigen::Vector3d normal = Eigen::MatrixXd::Zero(3,1);\n        int numPolys = polyData[\"polyhedrons\"].size();\n        int numPoints;\n        double x,y,z;\n        Eigen::MatrixXd C,d;\n        for(int i=0; i < numPolys; ++i){\n            numPoints = polyData[\"polyhedrons\"][i][\"points\"].size();\n            Poly poly;\n            C = Eigen::MatrixXd::Zero(numPoints,3);\n            d = Eigen::MatrixXd::Zero(numPoints,1);\n            for(int j=0; j < numPoints; ++j){\n                x = polyData[\"polyhedrons\"][i][\"points\"][j][\"x\"].as<double>();\n                y = polyData[\"polyhedrons\"][i][\"points\"][j][\"y\"].as<double>();\n                z = polyData[\"polyhedrons\"][i][\"points\"][j][\"z\"].as<double>();\n                point << x,y,z;\n                x = polyData[\"polyhedrons\"][i][\"normals\"][j][\"x\"].as<double>();\n                y = polyData[\"polyhedrons\"][i][\"normals\"][j][\"y\"].as<double>();\n                z = polyData[\"polyhedrons\"][i][\"normals\"][j][\"z\"].as<double>();\n                normal << x,y,z;\n                // point = point + 10 * normal.normalized();\n                poly.points.push_back(point);\n                poly.normals.push_back(normal);\n                C.block(j,0,1,3) = normal.transpose();\n                d(j) = normal.transpose() * point;\n            }\n            polys.push_back(poly);\n            Cn.push_back(C);\n            dn.push_back(d);\n            dn_neg.push_back(-d);\n        }\n    }\n\n    void Corridor::FindPolygon(const Eigen::Vector3d position, const Eigen::Vector3d tangent_line, const int corridor_id)\n    {\n        clock_t t1, t2;\n        t1 = clock();\n\n        assert((Cn[corridor_id] * position - dn[corridor_id]).maxCoeff() < 0);\n        for(int i=0; i<polys.size(); ++i){\n            assert(polys[i].starter.size() > 0);\n        }\n\n        /*  Note\n        The index system of the code is extremely complex, because there are too many elements and relationships.\n        @param corridor_id              The corridor consists of polyhedrons in a string, this index will find a specific one.\n        @param intersecting_line_id     Index of a line during intersecting points calculation.\n        @param final_line_id_table      The set of lines(std::vector<int>) that form the finnal polygon. \n        @param forward_point_id         Index of a point on a specific line.\n        @param candidate_next_line_id_table       The set of lines(std::vector<int>) that may be part of the polygon and will get further checks. \n        @param current_line_id          Index of a line.\n        @param current_forward_point_id Index of a point on a specific line.\n        @param current_backward_point_id    Index of a point on a specific line.\n        */\n\n        /* Formulation of the line parameterized by t\n            {x=x0+m*t\n            {y=y0+n*t\n            {z=z0+p*t \n            line := {x0, y0, z0, m, n, p}*/  \n        PolyLine poly_line;\n        std::vector<PolyLine> poly_lines;\n        //\"ref_plane := A*x+B*y+C*z+D=0\" is the plane that is perpendicular to the given line at the given position\n        Eigen::Matrix<double,1,3> ref_plane_ABC(tangent_line(0), tangent_line(1), tangent_line(2));\n        double ref_plane_D = - position.transpose() * tangent_line;\n\n        // step 1: find all the intersecting lines.\n        for ( int id = 0; id < Cn[corridor_id].rows(); id++ )\n        {\n            Eigen::Vector3d vec_a = ref_plane_ABC;\n            Eigen::Vector3d vec_b = Cn[corridor_id].row(id);\n\n            if ( (vec_a.normalized() - vec_b.normalized()).norm() < 1e-5 || (vec_a.normalized() - vec_b.normalized()).norm() > 2-1e-5) // parallel, skip\n                continue;\n\n            Eigen::Vector3d vec_line = vec_a.cross( vec_b );  // vec_a and vec_b mast be Eigen::Vector3d\n            vec_line.normalize();\n            Eigen::Vector3d p_on_ref = - vec_a * (ref_plane_D / (vec_a.norm() * vec_a.norm() ));\n            Eigen::Vector3d vec_on_ref = vec_line.cross(vec_a);\n            double t = - ( vec_b(0)*p_on_ref(0) + vec_b(1)*p_on_ref(1) + vec_b(2)*p_on_ref(2) + dn_neg[corridor_id](id) ) \n                       / (vec_b(0)*vec_on_ref(0) + vec_b(1)*vec_on_ref(1) + vec_b(2)*vec_on_ref(2));\n            Eigen::Vector3d point_line( p_on_ref(0) + vec_on_ref(0) * t, p_on_ref(1) + vec_on_ref(1) * t, p_on_ref(2) + vec_on_ref(2) * t);\n            poly_line.line.block(0,0,1,3) = point_line.transpose();\n            poly_line.line.block(0,3,1,3) = vec_line.transpose();\n            poly_lines.push_back(poly_line);\n        }\n\n        // step 2: find all the intersecting points of the lines.\n        for ( int i = 0; i < poly_lines.size(); i++ )\n        {\n            for ( int j = i+1; j < poly_lines.size(); j++ )\n            {\n                // A*t = B ==> t = ?\n                Eigen::Matrix<double,2,1> t;\n                Eigen::Matrix<double,2,2> A, A_;\n                Eigen::Matrix<double,2,1> B;\n                int flag = 0;\n\n                for ( int k = 0; k < 3; k++ )\n                {\n                    const int idx_ord[] = {0,1,0,1,2,2};\n                    A << poly_lines[i].line(idx_ord[k]+3), - poly_lines[j].line(idx_ord[k]+3), poly_lines[i].line(idx_ord[k+3]+3), - poly_lines[j].line(idx_ord[k+3]+3);\n                    if( fabs(A.determinant()) > 1e-5 )  // numerical stability\n                    {\n                        // calculate t\n                        B << (poly_lines[j].line(idx_ord[k]) - poly_lines[i].line(idx_ord[k])), (poly_lines[j].line(idx_ord[k+3]) - poly_lines[i].line(idx_ord[k+3]));\n                        A_ << A(1,1), -A(0,1), -A(1,0), A(0,0);\n                        t = 1 / ( A(0,0)*A(1,1) - A(0,1)*A(1,0) ) * A_ * B;\n\n                        // get point\n                        Eigen::Vector3d point;\n                        point << poly_lines[i].line(0) + poly_lines[i].line(3) * t(0,0),\n                                 poly_lines[i].line(1) + poly_lines[i].line(4) * t(0,0),\n                                 poly_lines[i].line(2) + poly_lines[i].line(5) * t(0,0);\n                        poly_lines[i].points.push_back(point);\n                        poly_lines[i].intersecting_line_id.push_back(j);\n\n                        point << poly_lines[j].line(0) + poly_lines[j].line(3) * t(1,0),\n                                 poly_lines[j].line(1) + poly_lines[j].line(4) * t(1,0),\n                                 poly_lines[j].line(2) + poly_lines[j].line(5) * t(1,0);\n                        poly_lines[j].points.push_back(point);\n                        poly_lines[j].intersecting_line_id.push_back(i);\n                        \n                        break;\n                    }\n                }\n            } \n        }\n\n        // step 3: find the polygon\n            //step 3.1: find the closest line to the point \n        std::vector<int> final_line_id_table = {-1};  // IDs of the lines that form the final polygen\n        double min_dist = abs(INFINITY);\n        Eigen::Vector3d pedal(INFINITY,INFINITY,INFINITY);\n        for ( int i = 0; i < poly_lines.size(); i++ )\n        {\n            // calculate distace from a point to a line\n            double x0,y0,z0, x1,y1,z1, xc,yc,zc, m,n,p, t, d;\n            x0 = position(0);\n            y0 = position(1);\n            z0 = position(2);\n            x1 = poly_lines[i].line(0,0);\n            y1 = poly_lines[i].line(0,1);\n            z1 = poly_lines[i].line(0,2);\n            m = poly_lines[i].line(0,3);\n            n = poly_lines[i].line(0,4);\n            p = poly_lines[i].line(0,5);\n            t = ( m*(x0-x1) + n*(y0-y1) + p*(z0-z1)) / (m*m + n*n + p*p);\n            xc = m*t + x1;\n            yc = n*t + y1;\n            zc = p*t + z1;\n            d = sqrt( (x0-xc)*(x0-xc) + (y0-yc)*(y0-yc) + (z0-zc)*(z0-zc) );\n            if ( d < min_dist )\n            {\n                min_dist = d;\n                final_line_id_table[0] = i;\n                pedal << xc, yc, zc;\n            }\n        }\n        if( isinf(min_dist) || final_line_id_table[0] == -1 )\n        {\n            cout << \"\\033[31m[corridor.cpp]ERROR CODE: 0x01\\033[0m\" << endl;\n            assert(false);\n        }\n        \n        /*  step 3.2: Search line by line\n                step 3.2.1: use cross product to distinguish if a point is on the clockwise side.\n                step 3.2.2: find the closest point on the right side.\n                step 3.2.3: segment the linked line on the clockwise side.\n                step 3.2.4: compare and find the line segment with smallest angle.\n                step 3.2.5: continue this loop until meeting the original line. */\n        Eigen::Vector3d vec_ret, vec_l, vec_r; // vec_ret = vec_l x vec_r.\n        double dist_min = abs(INFINITY);\n        int forward_point_id = -1;\n        std::vector<int> candidate_next_line_id_table;\n        vector<Eigen::Vector3d> vertexs;      // record vertexs for calculating area\n        // find the first intersection point.\n        for ( int i = 0; i < poly_lines[final_line_id_table[0]].points.size(); i++ )\n        {\n            vec_l = poly_lines[final_line_id_table[0]].points[i] - pedal;\n            vec_r = position - pedal;\n            vec_ret = vec_l.cross(vec_r);\n            if( vec_ret.dot(tangent_line) > 0) // same direction\n            {\n                double dist = ( poly_lines[final_line_id_table[0]].points[i] - pedal ).norm();\n                // sometimes more than two lines intersect at one same point.\n                if( dist < dist_min )\n                {\n                    dist_min = dist;\n                    candidate_next_line_id_table.clear();\n                    candidate_next_line_id_table.push_back(poly_lines[final_line_id_table[0]].intersecting_line_id[i]);\n                    forward_point_id = i;\n                }\n                else if( dist - dist_min < 1e-100 ) // Think of them as equals\n                {\n                    candidate_next_line_id_table.push_back(poly_lines[final_line_id_table[0]].intersecting_line_id[i]);\n                }\n                //cout << \"dist = \" << dist << \" dist_min = \" << dist_min << \" candidate_next_line_id_table = \" << candidate_next_line_id_table[0] << endl;\n            }\n        }\n        if( isinf(dist_min) || candidate_next_line_id_table.empty() )\n        {\n            cout << \"\\033[31m[corridor.cpp]ERROR CODE: 0x02\\033[0m\" << endl;\n            assert(false);\n        }\n        vertexs.push_back( poly_lines[final_line_id_table[0]].points[forward_point_id] );\n\n\n        // start loop\n        Eigen::Vector3d last_line_backward_point = pedal;\n        Eigen::Vector3d last_line_forward_point = poly_lines[final_line_id_table[0]].points[forward_point_id];\n        bool flag_find_a_polygon = false;\n        bool flag_dead_loop = false;\n        int iter_count = 0;\n        int current_line_id = -1;\n        int current_forward_point_id = -1;\n        int current_backward_point_id = -1;\n        while( ! flag_find_a_polygon && ! flag_dead_loop )\n        {\n            // find the line segment with smallest angle.\n            double max_cos = -1.0;\n            for ( int i = 0; i < candidate_next_line_id_table.size(); i++ )\n            {\n                vec_l = poly_lines[candidate_next_line_id_table[i]].line.block(0,3,1,3).transpose();\n                vec_r = last_line_backward_point - last_line_forward_point;\n                vec_ret = vec_l.cross(vec_r);\n                if (vec_ret.dot(tangent_line) < 0 ) // inverse direction\n                {\n                    vec_l = - vec_l;\n                }\n                double cos_l_r = vec_l.dot(vec_r) / ( vec_l.norm() * vec_r.norm() ); // cos_l_r from +1 -> -1\n                if( cos_l_r > max_cos ) // The bigger cos, the smaller angle\n                {\n                    max_cos = cos_l_r;\n                    current_line_id = candidate_next_line_id_table[i];\n                }\n            }\n            if( max_cos == -1.0 )\n            {\n                cout << \"\\033[31m[corridor.cpp]ERROR CODE: 0x03\\033[0m\" << endl;\n                assert(false);\n            }\n\n            // if ( candidate_next_line_id_table.size() >= 2 )\n            // {\n            //     cout << \"current_line_id=\" << current_line_id << endl;\n            //     for ( int i=0; i<candidate_next_line_id_table.size(); i++ )\n            //     {\n            //         cout << candidate_next_line_id_table[i] << endl;\n            //     }\n            // }\n\n            // delete the intersection information of all the redundant lines permanently\n            for ( int i = 0; i < candidate_next_line_id_table.size(); i++ )\n            {\n                if ( candidate_next_line_id_table[i] != current_line_id )\n                {\n                    // printf(\"candidate_next_line_id_table[i]=%d\\n\",candidate_next_line_id_table[i]);\n                    int redundant_line_id = candidate_next_line_id_table[i];\n\n                    poly_lines[redundant_line_id].intersecting_line_id.clear();\n                    poly_lines[redundant_line_id].points.clear();\n                    \n                    for ( int j=0; j<poly_lines.size(); j++ )\n                    {\n                        if ( j == redundant_line_id ) \n                            continue;\n\n                        for ( int k=0; k<poly_lines[j].intersecting_line_id.size(); k++ )\n                        {\n                            if ( poly_lines[j].intersecting_line_id[k] == redundant_line_id )\n                            {\n                                poly_lines[j].intersecting_line_id.erase( poly_lines[j].intersecting_line_id.begin() + k );\n                                poly_lines[j].points.erase( poly_lines[j].points.begin() + k );\n                                break;\n                            }\n                        }\n                    }\n                }\n            }\n            // cout << endl;\n\n            // successfully finish detect\n            if( current_line_id == final_line_id_table[0] )\n            {\n                flag_find_a_polygon = true;  // just a symbol for better reading, but not take any effect\n                break;\n            }\n\n            // dead loop detect\n            if( iter_count > poly_lines.size() )\n            {\n                flag_dead_loop = true;  // just a symbol for better reading, but not take any effect\n                cout << \"\\033[33m[corridor.cpp]ERROR: dead loop!\\033[0m\" << endl;\n\n                cout << \"position=\" << position.transpose() << endl << \" tangent_line=\" << tangent_line.transpose() << endl;\n\n                for ( int i = 0; i < poly_lines.size(); i++ )\n                {\n                    // cout << \"i = \" << i << \" ## \" << poly_lines[i].line << endl;\n                    cout.precision(15);\n                    cout << i << \" \" << poly_lines[i].line << endl;\n                }\n                cout << endl;\n\n                for ( int i=0; i<final_line_id_table.size(); i++ )\n                {\n                    cout << final_line_id_table[i] << \" \";\n                }\n                cout << endl;\n                cout << endl;\n\n                // for ( int i = 0; i < poly_lines.size(); i++ )\n                // {\n                //     for ( int j = 0; j < poly_lines[i].points.size(); j++)\n                //     {\n                //         cout << poly_lines[i].intersecting_line_id[j] << \" \" << poly_lines[i].points[j].transpose() << endl;\n                //     }\n                //     cout << endl;\n                // }\n                // cout << endl;\n\n                assert(false);\n            }\n\n            // find the intersection id of current line to the last line\n            for ( int i = 0; i < poly_lines[current_line_id].intersecting_line_id.size(); i++ )\n            {\n                if ( poly_lines[current_line_id].intersecting_line_id[i] == final_line_id_table.back() )\n                {\n                    current_backward_point_id = i;\n                }\n            }\n\n            // cout << \"cur_id=\" << current_line_id << endl;\n\n            // find next lines\n            dist_min = abs(INFINITY);\n            candidate_next_line_id_table.clear();\n            for ( int i = 0; i < poly_lines[current_line_id].points.size(); i++ )\n            {\n                if ( i == current_backward_point_id ) \n                    continue;\n\n                vec_l = poly_lines[current_line_id].points[i] - poly_lines[current_line_id].points[current_backward_point_id];\n                vec_r = last_line_backward_point - last_line_forward_point;\n                vec_ret = vec_l.cross(vec_r);\n                if( vec_ret.dot(tangent_line) > 0) // same direction\n                {\n                    double dist = ( poly_lines[current_line_id].points[i] - poly_lines[current_line_id].points[current_backward_point_id] ).norm();\n                    // cout << current_line_id << \" \" << poly_lines[current_line_id].intersecting_line_id[i] << \" \" << dist << \" \" << dist_min << endl;\n                    // sometimes more than two lines intersect at one same point.\n                    // cout << \"first_id=\" << final_line_id_table[0] << \" cur_id=\" << current_line_id << \" id=\" << poly_lines[current_line_id].intersecting_line_id[i] << \" dist=\" << dist << \" dist_min=\" << dist_min << endl;\n                    constexpr double CLOSE_MARGIN = 1e-3; // 1mm\n                    if( dist <= dist_min - CLOSE_MARGIN ) // small enough\n                    {\n                        dist_min = dist;\n                        current_forward_point_id = i;\n                        if ( candidate_next_line_id_table.size() <= 1 )\n                        {\n                            candidate_next_line_id_table.clear();\n                        }\n                        else\n                        {\n                            std::vector<int> candidate_next_line_id_table_temp;\n                            for( int j = 0; j < candidate_next_line_id_table.size(); j++ )\n                            {\n                                for ( int k = 0; k < poly_lines[current_line_id].intersecting_line_id.size(); k++ )\n                                {\n                                    if ( poly_lines[current_line_id].intersecting_line_id[k] == candidate_next_line_id_table[j] )\n                                    {\n                                        double dist2 = (poly_lines[current_line_id].points[k] - poly_lines[current_line_id].points[current_backward_point_id]).norm();\n                                        if ( abs(dist2 - dist_min) < CLOSE_MARGIN )\n                                        {\n                                            candidate_next_line_id_table_temp.push_back(candidate_next_line_id_table[j]);\n                                        }\n                                    }\n                                }\n                            }\n                            candidate_next_line_id_table = candidate_next_line_id_table_temp;\n                        }\n                        candidate_next_line_id_table.push_back(poly_lines[current_line_id].intersecting_line_id[i]);\n                        \n                    }\n                    else if( dist - dist_min < CLOSE_MARGIN ) // Some points closed to each other will be taken as candidates and the one of smallest angle will be select at the start of next iteration.\n                    {\n                        // cout << \"AAAAAAAAAAAAAAAAAAAAAAAAAAAA\" << endl;\n                        // cout << \"AAAAAAAAAAAAAAAAAAAAAAAAAAAA\" << endl;\n                        // cout << \"AAAAAAAAAAAAAAAAAAAAAAAAAAAA\" << endl;\n                        // cout << \"AAAAAAAAAAAAAAAAAAAAAAAAAAAA\" << endl;\n                        // cout << \"AAAAAAAAAAAAAAAAAAAAAAAAAAAA\" << endl;\n                        candidate_next_line_id_table.push_back(poly_lines[current_line_id].intersecting_line_id[i]);\n                    }\n                }\n            }\n            if( isinf(dist_min) || candidate_next_line_id_table.empty() )\n            {\n                cout << \"isinf(dist_min)=\" << isinf(dist_min) << \" candidate_next_line_id_table.empty()=\" << candidate_next_line_id_table.empty() << endl;\n                cout << \"\\033[31m[corridor.cpp]ERROR CODE: 0x05\\033[0m\" << endl;\n                assert(false);\n            }\n            vertexs.push_back( poly_lines[current_line_id].points[current_forward_point_id] );\n\n            last_line_backward_point = poly_lines[ current_line_id ].points[ current_backward_point_id ];\n            last_line_forward_point = poly_lines[ current_line_id ].points[ current_forward_point_id ];\n            final_line_id_table.push_back(current_line_id);\n            iter_count++;\n\n            // cout << \"current_line_id=\" << current_line_id << \" iter_count=\" << iter_count << endl;\n            // for ( int i=0; i<candidate_next_line_id_table.size(); i++ )\n            // {\n            //     cout << \"i=\" << i << \" candidate_id[i]=\" << candidate_next_line_id_table[i] << endl;\n            // }\n            // cout << \"--------------\" << endl;\n        }\n        // cout << endl << endl;\n\n        // calculate the area\n        double area = 0;\n        for ( int i = 0; i < vertexs.size(); i++)\n        {\n            vec_l = vertexs[i] - position;\n            if ( i == vertexs.size() - 1 )\n            {\n                vec_r = vertexs[0] - position;\n            }\n            else\n            {\n                vec_r = vertexs[i+1] - position;\n            }\n            vec_ret = vec_l.cross(vec_r);\n            area += vec_ret.norm() / 2.0;\n        }\n\n        // generate a polyhedron and return\n        ft::Polyhedron polyhedron(final_line_id_table.size(),4);\n        Eigen::Vector3d plane_normal, point_on_plane;\n        double D;\n        for ( int i = 0; i < final_line_id_table.size(); i++)\n        {\n            vec_l = tangent_line;\n            vec_r = poly_lines[final_line_id_table[i]].line.block(0,3,1,3).transpose();\n            plane_normal = vec_l.cross(vec_r);\n            point_on_plane = poly_lines[final_line_id_table[i]].line.block(0,0,1,3).transpose();\n            D = - point_on_plane.transpose() * plane_normal;\n            if( position.transpose() * plane_normal + D > 0 )\n            {\n                plane_normal = - plane_normal;\n                D = -D;\n            }\n            polyhedron.row(i) << plane_normal.transpose(), D;\n            polyhedron.row(i) = polyhedron.row(i).normalized();\n        }\n\n        if ( ! flag_find_a_polygon )\n        {\n            cout << \"\\033[31m[corridor.cpp]ERROR CODE: 0x06\\033[0m\" << endl;\n            assert(false);\n        }\n\n        t2 = clock();\n        double totaltime=(double)(t2-t1)/CLOCKS_PER_SEC;\n\t    // cout<<\"Total time: \"<<totaltime*1000<<endl;\t\t//ms\n\n        tunnel = polyhedron;\n        // cout << \"tunnel\"<< corridor_id << \": \" << endl << tunnel << endl;\n        tunnelArea = area;\n\n        // printf(\"AAAAAAAAAAAAAA\\n\");\n        // printf(\"AAAAAAAAAAAAAA\\n\");\n        // printf(\"AAAAAAAAAAAAAA\\n\");\n    }\n}//namespace ft\n"
  },
  {
    "path": "src/cmpcc/src/corridor_test.cpp",
    "content": "#include <corridor.h>\n#include <map.h>\n#include <Eigen/Core>\n#include <iostream>\n\nint main(void){\n    using namespace ft;\n    using namespace std;\n    Map map;\n    for (int i=0; i<map.corridor.polys.size(); ++i){\n        std::cout << i << \": \";\n        for (int j=0; j<map.corridor.polys[i].starter.size(); ++j){\n            std::cout << map.corridor.polys[i].starter[j] << \",\";\n            std::cout << map.corridor.polys[i].ender[j];\n            std::cout << \"|\";\n        }\n        std::cout << std::endl;\n    }\n    \n    // double theta = 2;\n    // Eigen::MatrixXd thetaPoint = map.trajPoint(theta);\n    // std::cout << map.corridor.Cn[1]*thetaPoint-map.corridor.dn[1] << std::endl;\n    // cout << \"thetaMax: \" << map.thetaMax << endl;\n    return 0;\n}\n"
  },
  {
    "path": "src/cmpcc/src/display_msg.cpp",
    "content": "#include \"display_msgs.h\"\n#include \"model.h\"\n#include \"Eigen/Dense\"\n#include \"ros/ros.h\"\n\nusing namespace std;\nnamespace ft{\n    DisplayMsgs::DisplayMsgs(Map &map_, int horizon_):map(map_), horizon(horizon_){\n        // initialize msgs:\n        refTraj_msg.header.frame_id = \"world\";\n        trajPred_msg.header.frame_id = \"world\";\n        corridor_array_msg.header.frame_id = \"world\";\n        tunnel_array_msg.header.frame_id = \"world\";\n        empty_poly_msg.header.frame_id = \"world\";\n        drone_msg.header.frame_id = \"world\";\n        drone_msg.type = visualization_msgs::Marker::ARROW;\n        drone_msg.action = visualization_msgs::Marker::ADD;\n        drone_msg.scale.x = 0.06;\n        drone_msg.scale.y = 0.1;\n        drone_msg.scale.z = 0;\n        drone_msg.color.a = 1;\n        drone_msg.color.r = 1;\n        drone_msg.color.g = 0;\n        drone_msg.color.b = 0;\n        drone_msg.pose.orientation.w = 1;\n        theta_msg = drone_msg;\n        theta_msg.color.r = 0;\n        theta_msg.color.b = 1;\n\n        // display unchanged msgs\n        displayRefTraj();\n        displayCorridors();\n\n        // pre malloc for trajPred_msg\n        trajPred_msg.poses.resize(horizon);\n        empty_poly_msg.polyhedrons.clear();\n        empty_poly_msg.ids.clear();\n    }   \n\n    void DisplayMsgs::displayRefTraj(){\n        geometry_msgs::PoseStamped tmpPose;\n        double theta = 0;\n        while (theta < map.thetaMax)\n        {\n            Eigen::Vector3d pos;\n            map.getGlobalCommand(theta, pos);\n            tmpPose.pose.position.x = pos(0);\n            tmpPose.pose.position.y = pos(1);\n            tmpPose.pose.position.z = pos(2);\n            refTraj_msg.poses.push_back(tmpPose);\n            theta += 0.01;\n        }\n    }\n    void DisplayMsgs::displayCorridors(){\n        for ( int i = 0; i < map.corridor.polys.size(); i++ )\n        {\n            auto poly = map.corridor.polys[i];\n            poly_msg.points.clear();\n            poly_msg.normals.clear();\n            for ( auto point : poly.points ){\n                pt.x = point(0);\n                pt.y = point(1);\n                pt.z = point(2);\n                poly_msg.points.push_back(pt);\n            }\n            for ( auto normal : poly.normals ){\n                nm.x = normal(0);\n                nm.y = normal(1);\n                nm.z = normal(2);\n                poly_msg.normals.push_back(nm);\n            }\n            corridor_array_msg.polyhedrons.push_back(poly_msg);\n            corridor_array_msg.ids.push_back(i);\n            // tunnel_array_msg.polyhedrons.push_back(poly_msg);\n            // tunnel_array_msg.ids.push_back(i);\n        }\n    }\n    void DisplayMsgs::clearTunnels(){\n        tunnel_array_msg.polyhedrons.clear();\n        tunnel_array_msg.ids.clear();\n    };\n    void DisplayMsgs::displayOneTunnel(int horizon_, double theta_){\n        Eigen::Vector3d position, tangent;\n        map.getGlobalCommand(theta_, position, tangent);\n        if (horizon_ == 0){\n            tunnel_array_msg.polyhedrons.clear();\n            tunnel_array_msg.ids.clear();\n        }\n        poly_msg.points.clear();\n        poly_msg.normals.clear();\n        for ( int j = 0; j < map.corridor.tunnel.rows(); j++ ){\n            Eigen::Vector3d normal = map.corridor.tunnel.block(j, 0, 1, 3).transpose().normalized();\n            double D = map.corridor.tunnel(j,3) / map.corridor.tunnel.block(j, 0, 1, 3).transpose().norm();\n            Eigen::Vector3d point_on_plane = - normal * D;\n            pt.x = point_on_plane(0);\n            pt.y = point_on_plane(1);\n            pt.z = point_on_plane(2);\n            poly_msg.points.push_back(pt);\n            nm.x = normal(0);\n            nm.y = normal(1);\n            nm.z = normal(2);\n            poly_msg.normals.push_back(nm);\n        }\n        Eigen::Vector3d extra_point, extra_normal = tangent.normalized();\n\n        extra_point = position + extra_normal * 1e-1;\n        pt.x = extra_point(0);\n        pt.y = extra_point(1);\n        pt.z = extra_point(2);\n        poly_msg.points.push_back(pt);\n        nm.x = extra_normal(0);\n        nm.y = extra_normal(1);\n        nm.z = extra_normal(2);\n        poly_msg.normals.push_back(nm);\n\n        extra_point = position - extra_normal * 1e-1;\n        pt.x = extra_point(0);\n        pt.y = extra_point(1);\n        pt.z = extra_point(2);\n        poly_msg.points.push_back(pt);\n        nm.x = -extra_normal(0);\n        nm.y = -extra_normal(1);\n        nm.z = -extra_normal(2);\n        poly_msg.normals.push_back(nm);\n        tunnel_array_msg.polyhedrons.push_back(poly_msg);\n        tunnel_array_msg.ids.push_back(horizon_);\n    }\n    void DisplayMsgs::displayDrone(Eigen::SparseMatrix<double> &state){\n        drone_msg.points.clear();\n        pt.x = state.coeffRef(0,0);\n        pt.y = state.coeffRef(Model::numOrder,0);\n        pt.z = state.coeffRef(2*Model::numOrder,0);\n        drone_msg.points.push_back(pt);\n        pt.x += state.coeffRef(1,0);\n        pt.y += state.coeffRef(1+Model::numOrder,0);\n        pt.z += state.coeffRef(1+2*Model::numOrder,0);\n        drone_msg.points.push_back(pt);\n        // drone_msg.header.stamp = ros::Time::now();\n    }\n    void DisplayMsgs::displayTheta(Eigen::SparseMatrix<double> &state){\n        double theta = state.coeffRef(Model::numState-Model::numOrder,0);\n        theta_msg.points.clear();\n        Eigen::Vector3d pos, vel;\n        map.getGlobalCommand(theta, pos, vel);\n        pt.x = pos(0);\n        pt.y = pos(1);\n        pt.z = pos(2);\n        theta_msg.points.push_back(pt);\n        pt.x += vel(0);\n        pt.y += vel(1);\n        pt.z += vel(2);\n        theta_msg.points.push_back(pt);      \n    }\n    void DisplayMsgs::displayPredict(Eigen::SparseMatrix<double> &statePredict){\n        geometry_msgs::PoseStamped tmpPose;\n        for (unsigned int i=0; i<horizon; ++i){\n            tmpPose.pose.position.x = statePredict.coeffRef(0,i);\n            tmpPose.pose.position.y = statePredict.coeffRef(Model::numOrder,i);\n            tmpPose.pose.position.z = statePredict.coeffRef(2*Model::numOrder,i);\n            trajPred_msg.poses[i] = tmpPose;\n        }\n    }\n\n    void DisplayMsgs::pubTunnels(ros::Publisher& pub){\n        pub.publish(empty_poly_msg);\n        pub.publish(tunnel_array_msg);\n    }\n}\n"
  },
  {
    "path": "src/cmpcc/src/fly_trr_node.cpp",
    "content": "#include <quadrotor_msgs/PositionCommand.h>\n#include <geometry_msgs/Point.h>\n#include <geometry_msgs/PoseStamped.h>\n#include <nav_msgs/Path.h>\n#include <nav_msgs/Odometry.h>\n#include <sensor_msgs/Imu.h>\n#include <ros/ros.h>\n#include \"mpc_solver.h\"\n\nusing namespace std;\nusing namespace Eigen;\nusing namespace ft;\nros::Time nowT;\nint fail_times = 0;\n\nros::Publisher cmd_pub, refer_pub, drone_pub, global_pub, vis_polytope_pub, flight_tunnel_pub, predict_pub, globalOdom_pub;\n\nft::MpcSolver simSolver;\nquadrotor_msgs::PositionCommand cmdMsg;\nnav_msgs::Odometry globalOdom;\n// Eigen::SparseMatrix<double,Eigen::RowMajor> stateOdom(Model::numState,1);\nEigen::SparseMatrix<double> stateCmd(Model::numState,1);\nEigen::SparseMatrix<double> stateMpc(Model::numState,1);\nEigen::SparseMatrix<double> stateOdom(Model::numState,1);\nEigen::SparseMatrix<double> stateOdomPrevious(Model::numState,1);\ndouble mpcT = 0.05;\ndouble odomT;\nEigen::Vector3d pDrone, vDrone, aDrone;\nbool mpcInit, odomRead, isGlobal, getDestination;\ngeometry_msgs::Point tmpPoint;\ngeometry_msgs::Vector3 tmpVector;\nros::Time tStart, tFinal, tOdom, tMpc;\n\n// vio\nMatrix3d w_R_odom;\nVector3d w_t_odom;\nbool update_transform = false;\nbool update_mpc_tr = false;\n\nint mycount = 0;\n\n// vio loop\nvoid rcvTransformCallBack(geometry_msgs::PoseConstPtr msg){\n    Quaterniond q(msg->orientation.w, msg->orientation.x, msg->orientation.y, msg->orientation.z);\n\n    w_R_odom = q.toRotationMatrix();\n    \n    w_t_odom << msg->position.x, msg->position.y, msg->position.z;\n    update_transform = true;\n}\n\nvoid odom_callback(const nav_msgs::Odometry& odom){\n    double t = (ros::Time::now() - nowT).toSec();\n    Vector3d pos, vel ,acc;\n    if(t < simSolver.map.thetaMax){\n    simSolver.map.getGlobalCommand(t, pos, vel, acc);\n    pos = w_R_odom.transpose() * (pos - w_t_odom);\n    vel = w_R_odom.transpose() * vel;\n    acc = w_R_odom.transpose() * acc;\n    cmdMsg.header.frame_id = \"world\";\n    cmdMsg.header.seq = mycount++;\n    cmdMsg.header.stamp = ros::Time::now();\n\n    tmpPoint.x = pos(0);\n    tmpPoint.y = pos(1);\n    tmpPoint.z = pos(2);\n    cmdMsg.position = tmpPoint;\n    tmpVector.x = vel(0);\n    tmpVector.y = vel(1);\n    tmpVector.z = vel(2);\n    cmdMsg.velocity = tmpVector;\n    tmpVector.x = acc(0);\n    tmpVector.y = acc(1);\n    tmpVector.z = acc(2);\n    cmdMsg.acceleration = tmpVector;\n    cmdMsg.trajectory_flag = 1u;\n    cmdMsg.yaw = atan2(cmdMsg.velocity.y, cmdMsg.velocity.x);\n    cmdMsg.yaw_dot = 1.0;\n    }\n    else{\n        pos << odom.pose.pose.position.x, odom.pose.pose.position.y, odom.pose.pose.position.z;\n        vel << 0,0,0;\n        acc << 0,0,0;\n        tmpPoint.x = pos(0);\n        tmpPoint.y = pos(1);\n        tmpPoint.z = pos(2);\n        cmdMsg.position = tmpPoint;\n        tmpVector.x = vel(0);\n        tmpVector.y = vel(1);\n        tmpVector.z = vel(2);\n        cmdMsg.velocity = tmpVector;\n        tmpVector.x = acc(0);\n        tmpVector.y = acc(1);\n        tmpVector.z = acc(2);\n        cmdMsg.acceleration = tmpVector;\n        cmdMsg.trajectory_flag = 1u;\n        cmdMsg.yaw = 0;\n        cmdMsg.yaw_dot = 1.0;\n    }\n    cmdMsg.acceleration.x += 2;\n    cmd_pub.publish(cmdMsg);\n}\n\nint main(int argc, char **argv)\n{\n    ros::init(argc, argv, \"fly_trr_node\");\n    ros::NodeHandle nodeHandle;\n    cmd_pub = nodeHandle.advertise<quadrotor_msgs::PositionCommand>(\"drone_1/position_cmd\",10);\n    refer_pub = nodeHandle.advertise<nav_msgs::Path>(\"refer_path\", 1000);\n    drone_pub = nodeHandle.advertise<visualization_msgs::Marker>(\"drone_pose\", 1000);\n    global_pub = nodeHandle.advertise<visualization_msgs::Marker>(\"global_pose\", 1000);\n    ros::Subscriber sub_odom = nodeHandle.subscribe(\"drone_1/state_ukf/odom\", 5 , odom_callback, ros::TransportHints().tcpNoDelay());\n    // ros::Subscriber sub_odom = nodeHandle.subscribe(\"/drone_1/visual_slam/odom\", 5 , odom_callback);\n    vis_polytope_pub  = nodeHandle.advertise<decomp_ros_msgs::PolyhedronArray>(\"polyhedron_corridor_mesh\", 1000, true);\n    flight_tunnel_pub = nodeHandle.advertise<decomp_ros_msgs::PolyhedronArray>(\"flight_tunnel\", 1000, true);\n    predict_pub = nodeHandle.advertise<nav_msgs::Path>(\"predict_path\", 1000);\n    globalOdom_pub = nodeHandle.advertise<nav_msgs::Odometry>(\"globalOdom\", 1000);\n\n    ros::MultiThreadedSpinner spinner(4);\n\n    cmdMsg.header.frame_id = \"world\";\n    cmdMsg.header.seq = mycount++;\n    cmdMsg.header.stamp = ros::Time::now();\n\n    // vio\n    w_R_odom = Matrix3d::Identity();\n    w_t_odom = Vector3d::Zero();\n    \n    // init position: \n    simSolver.map.getGlobalCommand(0, pDrone);\n    tmpPoint.x = pDrone(0);\n    tmpPoint.y = pDrone(1);\n    tmpPoint.z = pDrone(2);\n    cmdMsg.position = tmpPoint;\n    tmpVector.x = 0;\n    tmpVector.y = 0;\n    tmpVector.z = 0;\n    cmdMsg.velocity = tmpVector;\n    tmpVector.x = 0;\n    tmpVector.y = 0;\n    tmpVector.z = 0;\n    cmdMsg.acceleration = tmpVector;\n    cmdMsg.trajectory_flag = 1u;\n    cmdMsg.yaw = 0;\n    cmdMsg.yaw_dot = 0.0;\n    ros::Rate loopRate(10);\n    ros::Time startT = ros::Time::now();\n    while(ros::ok()){\n        nowT = ros::Time::now();\n        cmd_pub.publish(cmdMsg);\n        if ((nowT-startT).toSec() > 5){\n            cout << \"start_time_stamp: \" << nowT << endl;\n            break;\n        }\n        loopRate.sleep();\n    }\n\n    spinner.spin();\n    return 0;\n}\n"
  },
  {
    "path": "src/cmpcc/src/flyfly_node.cpp",
    "content": "#include <quadrotor_msgs/PositionCommand.h>\n#include <geometry_msgs/Point.h>\n#include <geometry_msgs/PoseStamped.h>\n#include <nav_msgs/Path.h>\n#include <nav_msgs/Odometry.h>\n#include <sensor_msgs/Imu.h>\n#include <ros/ros.h>\n#include \"mpc_solver.h\"\n\nusing namespace std;\nusing namespace Eigen;\nusing namespace ft;\n\nros::Publisher cmd_pub, refer_pub, drone_pub, global_pub, vis_polytope_pub, flight_tunnel_pub, predict_pub, globalOdom_pub;\n\nft::MpcSolver simSolver;\nquadrotor_msgs::PositionCommand cmdMsg;\nnav_msgs::Odometry globalOdom;\n// Eigen::SparseMatrix<double,Eigen::RowMajor> stateOdom(Model::numState,1);\nEigen::SparseMatrix<double> stateCmd(Model::numState,1);\nEigen::SparseMatrix<double> stateMpc(Model::numState,1);\nEigen::SparseMatrix<double> stateOdom(Model::numState,1);\nEigen::SparseMatrix<double> stateOdomPrevious(Model::numState,1);\ndouble mpcT = 0.05;\ndouble odomT;\nEigen::Vector3d pDrone, vDrone, aDrone;\nbool mpcInit, odomRead, isGlobal, getDestination;\ngeometry_msgs::Point tmpPoint;\ngeometry_msgs::Vector3 tmpVector;\nros::Time tStart, tFinal, tOdom, tMpc;\n\n// vio\nMatrix3d w_R_odom;\nVector3d w_t_odom;\nbool update_transform = false;\nbool update_mpc_tr = false;\n\n\n// vio loop\nvoid rcvTransformCallBack(geometry_msgs::PoseConstPtr msg){\n    Quaterniond q(msg->orientation.w, msg->orientation.x, msg->orientation.y, msg->orientation.z);\n\n    w_R_odom = q.toRotationMatrix();\n    \n    w_t_odom << msg->position.x, msg->position.y, msg->position.z;\n    update_transform = true;\n}\n\nvoid odom_callback(const nav_msgs::Odometry& odom){\n    odomRead = true;\n    odomT = (odom.header.stamp - tOdom).toSec();\n    tOdom = odom.header.stamp;\n    pDrone << odom.pose.pose.position.x, odom.pose.pose.position.y, odom.pose.pose.position.z;\n    vDrone << odom.twist.twist.linear.x, odom.twist.twist.linear.y, odom.twist.twist.linear.z;\n    aDrone << odom.twist.twist.angular.x, odom.twist.twist.angular.y, odom.twist.twist.angular.z;\n    stateOdom.coeffRef(9,0) = simSolver.map.findNearestTheta(pDrone);\n    odomRead = false;\n}\n\nvoid mpc_callback(const ros::TimerEvent& event){\n    while(ros::ok() && odomRead);\n    mpcInit = true;\n    tMpc = ros::Time::now();\n    // calculate stateMPC\n    // stateMpc = (stateOdom - stateOdomPrevious)/odomT*(tMpc-tOdom).toSec() + stateOdom;\n    update_mpc_tr = false;\n    if (update_transform){\n        update_mpc_tr = true;\n        Eigen::Vector3d p = w_R_odom * pDrone + w_t_odom;\n        Eigen::Vector3d v = w_R_odom * vDrone;\n        stateMpc.coeffRef(0,0) = p(0);\n        stateMpc.coeffRef(3,0) = p(1);\n        stateMpc.coeffRef(6,0) = p(2);\n        stateMpc.coeffRef(1,0) = v(0);\n        stateMpc.coeffRef(4,0) = v(1);\n        stateMpc.coeffRef(7,0) = v(2);\n    }\n    else{\n        stateMpc.coeffRef(0,0) = pDrone(0);\n        stateMpc.coeffRef(3,0) = pDrone(1);\n        stateMpc.coeffRef(6,0) = pDrone(2);\n        stateMpc.coeffRef(1,0) = vDrone(0);\n        stateMpc.coeffRef(4,0) = vDrone(1);\n        stateMpc.coeffRef(7,0) = vDrone(2);\n    }\n    update_transform = false;\n    // integral\n    stateMpc.coeffRef(2, 0) = simSolver.statePredict.coeffRef(2, 1); // ax\n    stateMpc.coeffRef(5, 0) = simSolver.statePredict.coeffRef(5, 1); // ay\n    stateMpc.coeffRef(8, 0) = simSolver.statePredict.coeffRef(8, 1); // az\n    stateMpc.coeffRef(10,0) = simSolver.statePredict.coeffRef(10,1); // vtheta\n    stateMpc.coeffRef(11,0) = simSolver.statePredict.coeffRef(11,1); // atheta\n    mpcInit = false;\n    simSolver.solveMpcQp(stateMpc);\n    \n    refer_pub.publish(simSolver.displayPtr->refTraj_msg);\n    global_pub.publish(simSolver.displayPtr->theta_msg);\n    drone_pub.publish(simSolver.displayPtr->drone_msg);\n    vis_polytope_pub.publish(simSolver.displayPtr->corridor_array_msg);\n    simSolver.displayPtr->pubTunnels(flight_tunnel_pub);\n    predict_pub.publish(simSolver.displayPtr->trajPred_msg);\n}\n\nvoid cmd_callback(const ros::TimerEvent& event){\n    while(ros::ok() && mpcInit);\n    double deltaT = (ros::Time::now() - tMpc).toSec();\n    Eigen::SparseMatrix<double> stateL = simSolver.statePredict.col(0);\n    Eigen::SparseMatrix<double> stateR = simSolver.statePredict.col(1);\n    stateCmd = stateL + (stateR-stateL)*deltaT/mpcT;\n    Eigen::Vector3d pos(stateCmd.coeffRef(0,0), stateCmd.coeffRef(3,0), stateCmd.coeffRef(6,0));\n    Eigen::Vector3d vel(stateCmd.coeffRef(1,0), stateCmd.coeffRef(4,0), stateCmd.coeffRef(7,0));\n    Eigen::Vector3d acc(stateCmd.coeffRef(2,0), stateCmd.coeffRef(5,0), stateCmd.coeffRef(8,0));\n    // vio {\n    if (update_mpc_tr){\n        pos = w_R_odom.transpose() * (pos - w_t_odom);\n        vel = w_R_odom.transpose() * vel;\n        acc = w_R_odom.transpose() * acc;\n    }\n    // } vio\n    tmpPoint.x = pos(0);\n    tmpPoint.y = pos(1);\n    tmpPoint.z = pos(2);\n    cmdMsg.position = tmpPoint;\n    tmpVector.x = vel(0);\n    tmpVector.y = vel(1);\n    tmpVector.z = vel(2);\n    cmdMsg.velocity = tmpVector;\n    tmpVector.x = acc(0);\n    tmpVector.y = acc(1);\n    tmpVector.z = acc(2);\n    cmdMsg.acceleration = tmpVector;\n    cmd_pub.publish(cmdMsg);\n}\n\nvoid safe_callback(const ros::TimerEvent& event){\n\n}\n\n\nint main(int argc, char **argv)\n{\n    ros::init(argc, argv, \"flyfly_node\");\n    ros::NodeHandle nodeHandle;\n    cmd_pub = nodeHandle.advertise<quadrotor_msgs::PositionCommand>(\"drone_1/position_cmd\",10);\n    refer_pub = nodeHandle.advertise<nav_msgs::Path>(\"refer_path\", 1000);\n    drone_pub = nodeHandle.advertise<visualization_msgs::Marker>(\"drone_pose\", 1000);\n    global_pub = nodeHandle.advertise<visualization_msgs::Marker>(\"global_pose\", 1000);\n    ros::Subscriber sub_odom = nodeHandle.subscribe(\"drone_1/state_ukf/odom\", 5 , odom_callback, ros::TransportHints().tcpNoDelay());\n    // ros::Subscriber sub_odom = nodeHandle.subscribe(\"/drone_1/visual_slam/odom\", 5 , odom_callback);\n    vis_polytope_pub  = nodeHandle.advertise<decomp_ros_msgs::PolyhedronArray>(\"polyhedron_corridor_mesh\", 1000, true);\n    flight_tunnel_pub = nodeHandle.advertise<decomp_ros_msgs::PolyhedronArray>(\"flight_tunnel\", 1000, true);\n    predict_pub = nodeHandle.advertise<nav_msgs::Path>(\"predict_path\", 1000);\n    globalOdom_pub = nodeHandle.advertise<nav_msgs::Odometry>(\"globalOdom\", 1000);\n    // vio\n    ros::Subscriber transformation_sub = nodeHandle.subscribe(\"w_T_odom\", 1, rcvTransformCallBack, ros::TransportHints().tcpNoDelay());\n\n    ros::Timer timer_mpc = nodeHandle.createTimer(ros::Duration(mpcT), mpc_callback);\n    ros::Timer timer_cmd = nodeHandle.createTimer(ros::Duration(0.01), cmd_callback);\n    // ros::Timer timer_safe = nodeHandle.createTimer(ros::Duration(0.005), safe_callback);\n\n    ros::MultiThreadedSpinner spinner(4);\n\n    cmdMsg.header.frame_id = \"world\";\n\n    // vio\n    w_R_odom = Matrix3d::Identity();\n    w_t_odom = Vector3d::Zero();\n    \n    // init position: \n    simSolver.map.getGlobalCommand(0, pDrone);\n    tmpPoint.x = pDrone(0);\n    tmpPoint.y = pDrone(1);\n    tmpPoint.z = pDrone(2);\n    cmdMsg.position = tmpPoint;\n    tmpVector.x = 0;\n    tmpVector.y = 0;\n    tmpVector.z = 0;\n    cmdMsg.velocity = tmpVector;\n    tmpVector.x = 0;\n    tmpVector.y = 0;\n    tmpVector.z = 0;\n    cmdMsg.acceleration = tmpVector;\n    ros::Rate loopRate(10);\n    ros::Time startT = ros::Time::now();\n    while(ros::ok()){\n        ros::Time nowT = ros::Time::now();\n        cmd_pub.publish(cmdMsg);\n        if ((nowT-startT).toSec() > 5){\n            break;\n        }\n        loopRate.sleep();\n    }\n\n    spinner.spin();\n    return 0;\n}\n"
  },
  {
    "path": "src/cmpcc/src/map.cpp",
    "content": "#include \"map.h\"\n#include <ros/package.h>\n#include <ros/ros.h>\n\nusing namespace std;\nusing namespace Eigen;\nnamespace ft{\n    Map::Map(){\n        std::string path = ros::package::getPath(\"cmpcc\")+\"/config/traj.yaml\";\n        YAML::Node node = YAML::LoadFile(path);\n        num_segment  = node[\"num_segment\" ].as<int> ();\n        traj_order = 5;\n        K_data = node[\"K\"].as <std::vector<double>>();\n        K_max = node[\"K_max\"].as <int>();\n        range  = node[\"range\" ].as < std::vector < double >> ();\n        long secs = node[\"final_time\"][\"secs\"].as<long>() - node[\"start_time\"][\"secs\"].as<long>();\n        long nsecs = node[\"final_time\"][\"nsecs\"].as<long>() - node[\"start_time\"][\"nsecs\"].as<long>();\n        global_traj_time = secs + nsecs*1e-9;\n        thetaMax = global_traj_time;\n        vector<double> coef_x = node[\"coef_x\"].  as < vector < double >> ();\n        vector<double> coef_y = node[\"coef_y\"].  as < vector < double >> ();\n        vector<double> coef_z = node[\"coef_z\"].  as < vector < double >> ();\n        vector<double> tt     = node[\"time\"].    as < vector < double >> ();\n        vector<double> ta     = node[\"time_acc\"].as < vector < double >> ();\n        vector<double> a      = node[\"a\"].       as < vector < double >> ();\n        vector<double> b      = node[\"b\"].       as < vector < double >> ();\n        vector<double> s      = node[\"s\"].       as < vector < double >> ();\n        int seg_num = K_data.size();\n        a_data   = MatrixXd::Zero(seg_num, K_max);\n        b_data   = MatrixXd::Zero(seg_num, K_max + 1);\n        s_data   = MatrixXd::Zero(seg_num, K_max + 1);\n        time     = MatrixXd::Zero(seg_num, K_max);\n        time_acc = MatrixXd::Zero(seg_num, K_max);\n        coef = Eigen::MatrixXd::Zero(num_segment, 3*(traj_order + 1) );\n        bezier_basis.setFixedOrder(traj_order);\n        int poly_num = traj_order + 1;\n        for (int idx = 0; idx < num_segment; ++idx){   \n            for (int j = 0; j < poly_num; ++j)\n            {\n                coef(idx, 0 * poly_num + j) = coef_x[idx * poly_num + j];\n                coef(idx, 1 * poly_num + j) = coef_y[idx * poly_num + j];\n                coef(idx, 2 * poly_num + j) = coef_z[idx * poly_num + j];\n            }\n        }\n        int K_shift = 0;\n        int K_plus_shift = 0;\n        for (int i = 0; i < seg_num; i++ )\n        {\n            for(int j = 0; j < K_data[i] + 1; j++ )\n            {   \n                if( j < K_data[i] )\n                {\n                    a_data(i, j) = a[j + K_shift];\n                    time    (i, j) = tt[j + K_shift];\n                    time_acc(i, j) = ta[j + K_shift];\n                }\n\n                b_data(i, j) = b[j + K_plus_shift];\n                s_data(i, j) = s[j + K_plus_shift];\n            }\n\n            K_shift      += K_data[i];\n            K_plus_shift += K_data[i] + 1;\n        }\n        // corridor\n        double theta, maxItem;\n        int starter = 0;\n        bool inPoly = false;\n        Eigen::Vector3d thetaPoint;\n        for (int i=0; i<corridor.polys.size(); ++i){\n            for (theta=0; theta<thetaMax; theta+=0.001){\n                getGlobalCommand(theta, thetaPoint);\n                theta_sample.push_back(theta);\n                pos_sample.push_back(thetaPoint);\n                maxItem = (corridor.Cn[i]*thetaPoint-corridor.dn[i]).maxCoeff();\n                if (maxItem < 0 && !inPoly){\n                    corridor.polys[i].starter.push_back(theta);\n                    inPoly = true;\n                }\n                if (maxItem > 0 && inPoly){\n                    corridor.polys[i].ender.push_back(theta-0.001);\n                    inPoly = false;\n                }\n            }\n            if (inPoly){\n                corridor.polys[i].ender.push_back(thetaMax);\n                inPoly = false;\n            }\n        }\n    }\n    double Map::findNearestTheta(double theta, Eigen::Vector3d & position){\n        Eigen::Vector3d thetaPoint;\n        int index = 0;\n        int left = 0;\n        int right = theta_sample.size()-1;\n        double distance = 0;\n        double nearestTheta = 0;\n        double distanceMin = 10;\n        double error = 10;\n        while(fabs(error) > 0.005){\n            error = theta_sample[index] - theta;\n            if(error > 0){\n                right = index;\n                index = (index+left)/2;\n            }\n            else{\n                left = index;\n                index = (index+right)/2;\n            }\n        }\n        for (int i=((index-1000)>0? index-1000:0); i<(index+1000<theta_sample.size()? index+1000:theta_sample.size()-1); i+=100){\n            distance = (position - pos_sample[i]).squaredNorm();\n            if(distance < distanceMin){\n                distanceMin = distance;\n                nearestTheta = theta_sample[i];\n                index = i;\n            }\n        }\n        for (int i=((index-100)>0? index-100:0); i<(index+100<theta_sample.size()? index+100:theta_sample.size()-1); i+=10){\n            distance = (position - pos_sample[i]).squaredNorm();\n            if(distance < distanceMin){\n                distanceMin = distance;\n                nearestTheta = theta_sample[i];\n                index = i;\n            }\n        }\n        for (int i=((index-10)>0? index-10:0); i<(index+10<theta_sample.size()? index+10:theta_sample.size()-1); i+=1){\n            distance = (position - pos_sample[i]).squaredNorm();\n            if(distance < distanceMin){\n                distanceMin = distance;\n                nearestTheta = theta_sample[i];\n            }\n        }\n        return nearestTheta;\n    };\n    double Map::findNearestTheta(Eigen::Vector3d & position){\n        Eigen::Vector3d thetaPoint;\n        int index;\n        double distance = 0;\n        double nearestTheta = 0;\n        double distanceMin = 10;\n        // sampling method for finding the nearest point on the trajectory\n        for (int i=0; i<theta_sample.size(); i+=1000){\n            distance = (position - pos_sample[i]).squaredNorm();\n            if(distance < distanceMin){\n                distanceMin = distance;\n                nearestTheta = theta_sample[i];\n                index = i;\n            }\n        }\n        for (int i=((index-1000)>0? index-1000:0); i<(index+1000<theta_sample.size()? index+1000:theta_sample.size()-1); i+=100){\n            distance = (position - pos_sample[i]).squaredNorm();\n            if(distance < distanceMin){\n                distanceMin = distance;\n                nearestTheta = theta_sample[i];\n                index = i;\n            }\n        }\n        for (int i=((index-100)>0? index-100:0); i<(index+100<theta_sample.size()? index+100:theta_sample.size()-1); i+=10){\n            distance = (position - pos_sample[i]).squaredNorm();\n            if(distance < distanceMin){\n                distanceMin = distance;\n                nearestTheta = theta_sample[i];\n                index = i;\n            }\n        }\n        for (int i=((index-10)>0? index-10:0); i<(index+10<theta_sample.size()? index+10:theta_sample.size()-1); i+=1){\n            distance = (position - pos_sample[i]).squaredNorm();\n            if(distance < distanceMin){\n                distanceMin = distance;\n                nearestTheta = theta_sample[i];\n            }\n        }\n        return nearestTheta;\n    }\n\n    void Map::findRangeForEachPoly(){\n        // find range of theta for each poly\n        double theta, maxItem;\n        int starter = 0;\n        bool inPoly = false;\n        Eigen::Vector3d thetaPoint;\n        for (int i=0; i<corridor.polys.size(); ++i){\n            for (theta=0; theta<thetaMax; theta+=0.001){\n                getGlobalCommand(theta, thetaPoint);\n                maxItem = (corridor.Cn[i]*thetaPoint-corridor.dn[i]).maxCoeff();\n                if (maxItem < 0 && !inPoly){\n                    corridor.polys[i].starter.push_back(theta);\n                    inPoly = true;\n                }\n                if (maxItem > 0 && inPoly){\n                    corridor.polys[i].ender.push_back(theta-0.001);\n                    inPoly = false;\n                }\n            }\n            if (inPoly){\n                corridor.polys[i].ender.push_back(thetaMax);\n                inPoly = false;\n            }\n        }\n    }\n\n    void Map::getGlobalCommand(double t, Vector3d & position){\n        t = std::fmod(t, global_traj_time);\n\n        int idx;\n        for(idx = 0; idx < num_segment; idx++)\n        {   \n            int K = K_data[idx];\n            if( t  > time(idx, K - 1)){\n                t -= time(idx, K - 1);\n            }\n            else break;\n        }\n\n        double t_tmp = t;     \n        int grid_num = K_data[idx];\n\n        //ROS_WARN(\"[Time Optimal Server] publish command, segm index is %d, segm time is %f\", idx, t);\n        int grid_idx;\n        for(grid_idx = 0; grid_idx < K_data[idx]; grid_idx++)\n        {\n            if (t > time(idx, grid_idx)) continue;\n            else\n            { \n                if(grid_idx > 0) t -= time(idx, grid_idx - 1);\n                else t -= 0.0;\n\n                break;\n            }\n        }\n\n        //ROS_WARN(\"[Time Optimal Server] publish command, grid index is %d, grid time is %f\", grid_idx, t);\n        double delta_t;\n        if(grid_idx > 0)\n          delta_t = (time(idx, grid_idx) - time(idx, grid_idx - 1));\n        else\n          delta_t = time(idx, grid_idx) - 0.0;\n        \n        double delta_s = t * s_step / delta_t;\n\n        double s = s_data(idx, grid_idx) + delta_s;\n\n        Vector3d position_s = bezier_basis.getPos(coef, idx, s/range[idx]) * range[idx]; \n        position   = position_s;\n    }\n\n    void Map::getGlobalCommand(double t, Vector3d & position, Vector3d & velocity){\n        t = std::fmod(t, global_traj_time);\n\n        int idx;\n        for(idx = 0; idx < num_segment; idx++)\n        {   \n            int K = K_data[idx];\n            if( t  > time(idx, K - 1)){\n                t -= time(idx, K - 1);\n            }\n            else break;\n        }\n\n        double t_tmp = t;     \n        int grid_num = K_data[idx];\n\n        //ROS_WARN(\"[Time Optimal Server] publish command, segm index is %d, segm time is %f\", idx, t);\n        int grid_idx;\n        for(grid_idx = 0; grid_idx < K_data[idx]; grid_idx++)\n        {\n            if (t > time(idx, grid_idx)) continue;\n            else\n            { \n                if(grid_idx > 0) t -= time(idx, grid_idx - 1);\n                else t -= 0.0;\n\n                break;\n            }\n        }\n\n        //ROS_WARN(\"[Time Optimal Server] publish command, grid index is %d, grid time is %f\", grid_idx, t);\n        double delta_t;\n        if(grid_idx > 0)\n          delta_t = (time(idx, grid_idx) - time(idx, grid_idx - 1));\n        else\n          delta_t = time(idx, grid_idx) - 0.0;\n        \n        double delta_s = t * s_step / delta_t;\n\n        double s = s_data(idx, grid_idx) + delta_s;\n\n        Vector3d position_s = bezier_basis.getPos(coef, idx, s/range[idx]) * range[idx]; \n        position   = position_s;\n        // cout<<\"s: \"<<s<<\", position: \"<<position <<\", range: \"<<range[idx] << \", idx:\"<<idx <<endl;\n\n        double s_k   = s_data(idx, grid_idx);\n        double s_k_1 = s_data(idx, grid_idx + 1);\n        double b_k   = b_data(idx, grid_idx);\n        double b_k_1 = b_data(idx, grid_idx + 1);\n\n        Vector3d velocity_s1 = bezier_basis.getVel(coef, idx, s_k   /range[idx]); \n        Vector3d velocity_s2 = bezier_basis.getVel(coef, idx, s_k_1 /range[idx]);\n\n        Vector3d velocity1 = velocity_s1 * sqrt(b_k);\n        Vector3d velocity2 = velocity_s2 * sqrt(b_k_1);\n        velocity  = velocity1 + (velocity2 - velocity1) * t / delta_t;\n    }\n    \n    void Map::getGlobalCommand(double t, Vector3d & position, Vector3d & velocity, Vector3d & acceleration)\n    {   \n        t = std::fmod(t, global_traj_time);\n\n        int idx;\n        for(idx = 0; idx < num_segment; idx++)\n        {   \n            int K = K_data[idx];\n            if( t  > time(idx, K - 1)){\n                t -= time(idx, K - 1);\n            }\n            else break;\n        }\n\n        double t_tmp = t;     \n        int grid_num = K_data[idx];\n\n        //ROS_WARN(\"[Time Optimal Server] publish command, segm index is %d, segm time is %f\", idx, t);\n        int grid_idx;\n        for(grid_idx = 0; grid_idx < K_data[idx]; grid_idx++)\n        {\n            if (t > time(idx, grid_idx)) continue;\n            else\n            { \n                if(grid_idx > 0) t -= time(idx, grid_idx - 1);\n                else t -= 0.0;\n\n                break;\n            }\n        }\n\n        //ROS_WARN(\"[Time Optimal Server] publish command, grid index is %d, grid time is %f\", grid_idx, t);\n        double delta_t;\n        if(grid_idx > 0)\n          delta_t = (time(idx, grid_idx) - time(idx, grid_idx - 1));\n        else\n          delta_t = time(idx, grid_idx) - 0.0;\n        \n        double delta_s = t * s_step / delta_t;\n\n        double s = s_data(idx, grid_idx) + delta_s;\n\n        Vector3d position_s = bezier_basis.getPos(coef, idx, s/range[idx]) * range[idx]; \n        position   = position_s;\n        // cout<<\"s: \"<<s<<\", position: \"<<position <<\", range: \"<<range[idx] << \", idx:\"<<idx <<endl;\n\n        double s_k   = s_data(idx, grid_idx);\n        double s_k_1 = s_data(idx, grid_idx + 1);\n        double b_k   = b_data(idx, grid_idx);\n        double b_k_1 = b_data(idx, grid_idx + 1);\n\n        Vector3d velocity_s1 = bezier_basis.getVel(coef, idx, s_k   /range[idx]); \n        Vector3d velocity_s2 = bezier_basis.getVel(coef, idx, s_k_1 /range[idx]);\n\n        Vector3d velocity1 = velocity_s1 * sqrt(b_k);\n        Vector3d velocity2 = velocity_s2 * sqrt(b_k_1);\n        velocity  = velocity1 + (velocity2 - velocity1) * t / delta_t;\n\n        t = t_tmp;\n        for(grid_idx = 0; grid_idx < K_data[idx]; grid_idx++)\n        {\n            if (t > time_acc(idx, grid_idx)) continue;\n            else\n            { \n                if(grid_idx > 0) t -= time_acc(idx, grid_idx - 1);\n                else t -= 0.0;\n\n                break;\n            }\n        }\n        \n        if(grid_idx == grid_num) t -= time_acc(idx, grid_num - 1);\n\n        //ROS_WARN(\"[Time Optimal Server] publish command, grid index is %d, grid time is %f\", grid_idx, t);\n        Vector3d velocity_s, acceleration_s, acceleration1, acceleration2;\n\n        double a_k, s_1;\n        if( grid_idx == 0 && idx == 0 )\n        {   \n            s_k   = s_data(idx, 0);\n            s_k_1 = s_data(idx, 0 + 1);\n            \n            a_k   = a_data(idx, 0);\n            b_k   = b_data(idx, 0);\n            b_k_1 = b_data(idx, 0 + 1);\n\n            s_1 = (s_k + s_k_1 ) / 2.0 / range[idx];\n            velocity_s     = bezier_basis.getVel(coef, idx, s_1);\n            acceleration_s = bezier_basis.getAcc(coef, idx, s_1) / range[idx];\n            acceleration2 = velocity_s * a_k + acceleration_s * (b_k + b_k_1) / 2.0;\n            acceleration1 << 0.0, 0.0, 0.0;\n            \n            acceleration   = acceleration1 + (acceleration2 - acceleration1) * t / time_acc(0, 0); \n        }\n        else if( grid_idx == grid_num && idx == (num_segment - 1) )\n        {   \n            s_k   = s_data(idx, grid_num - 1);\n            s_k_1 = s_data(idx, grid_num);\n            \n            a_k   = a_data(idx, grid_num - 1);\n            b_k   = b_data(idx, grid_num - 1);\n            b_k_1 = b_data(idx, grid_num    );\n\n            s_1 = (s_k + s_k_1 ) / 2.0 /range[idx];\n            velocity_s     = bezier_basis.getVel(coef, idx, s_1);\n            acceleration_s = bezier_basis.getAcc(coef, idx, s_1) / range[idx];\n            acceleration = velocity_s * a_k + acceleration_s * (b_k + b_k_1) / 2.0;\n        }\n        else\n        {   \n            if(grid_idx < grid_num && grid_idx > 0) // take average accleration in a same segment\n            {   \n                delta_t = (time_acc(idx, grid_idx) - time_acc(idx, grid_idx - 1));\n                \n                s_k   = s_data(idx, grid_idx - 1);\n                s_k_1 = s_data(idx, grid_idx + 0);\n                \n                a_k   = a_data(idx, grid_idx - 1);\n                b_k   = b_data(idx, grid_idx - 1);\n                b_k_1 = b_data(idx, grid_idx + 0);\n\n                s_1 = (s_k + s_k_1 ) / 2.0 /range[idx];\n                velocity_s     = bezier_basis.getVel(coef, idx, s_1);\n                acceleration_s = bezier_basis.getAcc(coef, idx, s_1) / range[idx];\n                acceleration1 = velocity_s * a_k + acceleration_s * (b_k + b_k_1) / 2.0;\n\n                s_k   = s_data(idx, grid_idx + 0);\n                s_k_1 = s_data(idx, grid_idx + 1);\n\n                a_k   = a_data(idx, grid_idx + 0);\n                b_k   = b_data(idx, grid_idx + 0);\n                b_k_1 = b_data(idx, grid_idx + 1);              \n\n                s_1 = (s_k + s_k_1 ) / 2.0 /range[idx];\n                velocity_s     = bezier_basis.getVel(coef, idx, s_1);\n                acceleration_s = bezier_basis.getAcc(coef, idx, s_1) / range[idx];\n                acceleration2 = velocity_s * a_k + acceleration_s * (b_k + b_k_1) / 2.0;\n                acceleration   = acceleration1 + (acceleration2 - acceleration1) * t / delta_t;   \n            }\n            else if(grid_idx == grid_num)// take average accleration between two segments\n            {   \n                delta_t = (time(idx, grid_num - 1) - time_acc(idx, grid_num - 1) + time_acc(idx + 1, 0) );\n                \n                s_k   = s_data(idx, grid_idx - 1);\n                s_k_1 = s_data(idx, grid_idx);\n                \n                a_k   = a_data(idx, grid_idx - 1);\n                b_k   = b_data(idx, grid_idx - 1);\n                b_k_1 = b_data(idx, grid_idx);\n\n                s_1 = (s_k + s_k_1 ) / 2.0 /range[idx];\n                velocity_s     = bezier_basis.getVel(coef, idx, s_1);\n                acceleration_s = bezier_basis.getAcc(coef, idx, s_1) / range[idx];\n                acceleration1 = velocity_s * a_k + acceleration_s * (b_k + b_k_1) / 2.0;\n                s_k   = s_data(idx + 1, 0);\n                s_k_1 = s_data(idx + 1, 1);\n\n                a_k   = a_data(idx + 1, 0);\n                b_k   = b_data(idx + 1, 0);\n                b_k_1 = b_data(idx + 1, 1);              \n\n                s_1 = (s_k + s_k_1 ) / 2.0 /range[idx + 1];\n                velocity_s     = bezier_basis.getVel(coef, idx + 1, s_1);\n                acceleration_s = bezier_basis.getAcc(coef, idx + 1, s_1) / range[idx + 1];\n                acceleration2 = velocity_s * a_k + acceleration_s * (b_k + b_k_1) / 2.0;\n                acceleration  = acceleration1 + (acceleration2 - acceleration1) * t / delta_t;        \n            }\n            else if(grid_idx == 0)// take average accleration between two segments\n            {   \n                int grid_num_k = K_data[idx - 1]; // last segment's grid num\n                delta_t = (time(idx - 1, grid_num_k - 1) - time_acc(idx - 1, grid_num_k - 1) + time_acc(idx, 0) );\n                \n                s_k   = s_data(idx - 1, grid_num_k - 1);\n                s_k_1 = s_data(idx - 1, grid_num_k    );\n                \n                a_k   = a_data(idx - 1, grid_num_k - 1);\n                b_k   = b_data(idx - 1, grid_num_k - 1);\n                b_k_1 = b_data(idx - 1, grid_num_k    );\n\n                s_1 = (s_k + s_k_1 ) / 2.0 / range[idx - 1];\n                velocity_s     = bezier_basis.getVel(coef, idx - 1, s_1);\n                acceleration_s = bezier_basis.getAcc(coef, idx - 1, s_1) / range[idx - 1];\n                acceleration1  = velocity_s * a_k + acceleration_s * (b_k + b_k_1) / 2.0;\n\n                s_k   = s_data(idx, 0);\n                s_k_1 = s_data(idx, 0 + 1);\n                \n                a_k   = a_data(idx, 0);\n                b_k   = b_data(idx, 0);\n                b_k_1 = b_data(idx, 0 + 1);\n\n                s_1 = (s_k + s_k_1 ) / 2.0 / range[idx];\n                velocity_s     = bezier_basis.getVel(coef, idx, s_1);\n                acceleration_s = bezier_basis.getAcc(coef, idx, s_1) / range[idx];\n                acceleration2  = velocity_s * a_k + acceleration_s * (b_k + b_k_1) / 2.0;\n                acceleration   = acceleration1 + (acceleration2 - acceleration1) * (t + time(idx - 1, grid_num_k - 1) - time_acc(idx - 1, grid_num_k - 1)) / delta_t;   \n            } \n        }\n    }\n\n} //namespace ft\n"
  },
  {
    "path": "src/cmpcc/src/model.cpp",
    "content": "#include \"model.h\"\n#include <iostream>\n\nusing namespace Eigen;\nusing namespace std;\nnamespace ft{\n    Model::Model (double T){\n        Ad.resize(numState,numState);\n        Bd.resize(numState,numInput);\n        std::vector<Eigen::Triplet<double>> triplets;\n        for (int i=0; i<numState; ++i){\n            triplets.push_back(Triplet<double>(i,i,1));\n            if ((i+1) % numOrder){\n                triplets.push_back(Triplet<double>(i,i+1,T));\n            }\n        }\n        Ad.setFromTriplets(triplets.begin(), triplets.end());\n        triplets.clear();\n        for (int i=0; i<numInput; ++i){\n            triplets.push_back(Triplet<double>((i+1)*numOrder-1,i,T));\n        }\n        Bd.setFromTriplets(triplets.begin(), triplets.end());\n    }\n\n}// namespace ft\n"
  },
  {
    "path": "src/cmpcc/src/mpc_solver.cpp",
    "content": "#include \"mpc_solver.h\"\n#include \"sparse_utils.h\"\n#include <iostream>\n#include <ros/package.h>\n#include <ctime>\n\nusing namespace Eigen;\nusing namespace std;\nnamespace ft{\n    MpcSolver::MpcSolver()\n    {\n        std::string path = ros::package::getPath(\"cmpcc\")+\"/config/mpcParameters.yaml\";\n        YAML::Node node = YAML::LoadFile(path);\n        horizon = node[\"horizon\"].as<int>();\n        corridorConstrains = node[\"corridorConstrains\"].as<int>();\n        qVTheta = node[\"qVTheta\"].as<double>();\n        stateUpper = node[\"stateUpper\"].as<vector<double>>();\n        stateLower = node[\"stateLower\"].as<vector<double>>();\n        inputUpper = node[\"inputUpper\"].as<vector<double>>();\n        inputLower = node[\"inputLower\"].as<vector<double>>();\n        displayPtr = new DisplayMsgs(map, horizon);\n        initStatus = true;\n\n        Qk.resize(horizon*numState, horizon*numState);\n        qk.resize(horizon*numState, 1);\n        AA.resize(horizon*numState, numState);\n        BB.resize(horizon*numState, horizon*numInput);\n        In.resize(horizon*numInput, horizon*numInput);\n        A.resize(0,0);\n        b.resize(0,0);\n        xl.resize(horizon*numState, 1);\n        xu.resize(horizon*numState, 1);\n        ul.resize(horizon*numInput, 1);\n        uu.resize(horizon*numInput, 1);\n        inputPredict.resize(numInput, horizon);\n        statePredict.resize(numState, horizon);\n        // initialize xl,xu,ul,uu: \n        Eigen::SparseMatrix<double> stateUs(numState,1);\n        Eigen::SparseMatrix<double> stateLs(numState,1);\n        Eigen::SparseMatrix<double> inputUs(numInput,1);\n        Eigen::SparseMatrix<double> inputLs(numInput,1);\n        for (int i=0; i<numState; ++i){\n            stateUs.coeffRef(i,0) = stateUpper[i];\n            stateLs.coeffRef(i,0) = stateLower[i];\n        }\n        for (int i=0; i<numInput; ++i){\n            inputUs.coeffRef(i,0) = inputUpper[i];\n            inputLs.coeffRef(i,0) = inputLower[i];\n        }\n        for (int i=0; i<horizon; ++i){\n            sp::colMajor::setRows(xl, stateLs, numState*i);\n            sp::colMajor::setRows(xu, stateUs, numState*i);\n            sp::colMajor::setRows(ul, inputLs, numInput*i);\n            sp::colMajor::setRows(uu, inputUs, numInput*i);\n            xu.coeffRef(i*numState+9,0) = map.thetaMax;\n        }\n        // initialize AA & BB: \n        Eigen::SparseMatrix<double> tmpA = model.Ad;\n        Eigen::SparseMatrix<double> tmpB(numState, horizon*numInput);\n        sp::colMajor::setRows(AA, model.Ad, 0);\n        sp::colMajor::setBlock(BB, model.Bd, 0, 0);\n        for (int k=1; k<horizon; ++k){\n            tmpA = model.Ad * tmpA;\n            sp::colMajor::setRows(AA, tmpA, k*numState);\n            tmpB = model.Ad * BB.block((k-1)*numState,0,numState,horizon*numInput);\n            sp::colMajor::setRows(BB, tmpB, k*numState);\n            sp::colMajor::setBlock(BB, model.Bd, k*numState, k*numInput);\n        }\n        AAT = AA.transpose();\n        BBT = BB.transpose();\n        // initialize In: -> penalty minium snap\n        for (int i=0; i<horizon*numInput; ++i){\n            In.coeffRef(i,i) = 1e-4;\n            if (i < (horizon-1)*numInput){\n                In.coeffRef(i,i+numInput) = -1e-6;\n                In.coeffRef(i+numInput,i) = -1e-6;\n            }\n        }\n        // initialize Inu:\n        Inu.resize(horizon*numInput, horizon*numInput);\n        Inu.setIdentity();\n    }\n\n    MpcSolver::~MpcSolver(){\n        delete displayPtr;\n    }\n\n    void MpcSolver::calculateCost(double theta, int horizon_){\n        double theta_ = std::fmod(theta, map.thetaMax);\n        Eigen::Vector3d pos, vel;\n        map.getGlobalCommand(theta_, pos, vel);\n        double x_v = pos(0);\n        double y_v = pos(1);\n        double z_v = pos(2);\n        double dx_v__dtheta = vel(0);\n        double dy_v__dtheta = vel(1);\n        double dz_v__dtheta = vel(2);\n        double r_x = x_v - dx_v__dtheta * theta;\n        double r_y = y_v - dy_v__dtheta * theta;\n        double r_z = z_v - dz_v__dtheta * theta;\n        Eigen::SparseMatrix<double> grad_x(numState, 1);\n        Eigen::SparseMatrix<double> grad_y(numState, 1);\n        Eigen::SparseMatrix<double> grad_z(numState, 1);\n        grad_x.coeffRef(0,0) = 1;\n        grad_y.coeffRef(Model::numOrder,0) = 1;\n        grad_z.coeffRef(2*Model::numOrder,0) = 1;\n        grad_x.coeffRef(numState-Model::numOrder,0) = -dx_v__dtheta;\n        grad_y.coeffRef(numState-Model::numOrder,0) = -dy_v__dtheta;\n        grad_z.coeffRef(numState-Model::numOrder,0) = -dz_v__dtheta;\n        Qn = grad_x * Eigen::SparseMatrix<double>(grad_x.transpose()) + \n             grad_y * Eigen::SparseMatrix<double>(grad_y.transpose()) + \n             grad_z * Eigen::SparseMatrix<double>(grad_z.transpose());\n        qn = -r_x*grad_x - r_y*grad_y - r_z*grad_z;\n        qn.coeffRef(numState-Model::numOrder+1,0) = -qVTheta;\n        sp::colMajor::setBlock(Qk, Qn, numState*horizon_, numState*horizon_);\n        // sp::colMajor::setRows(qk, qn, numState*horizon_);\n        // todo: I do not understand why setRows not work here but well in other places...\n        sp::colMajor::setBlock(qk,qn,numState*horizon_,0);\n    }\n    void MpcSolver::calculateConstrains(Eigen::SparseMatrix<double> &stateTmp, int horizon_){\n        assert(stateTmp.rows() == numState);\n        double theta_ = stateTmp.coeffRef(numState-Model::numOrder,0);\n        double theta = std::fmod(theta_+map.thetaMax, map.thetaMax);\n        // double theta = theta_>map.thetaMax ? map.thetaMax:theta_;\n        int numConstrains = 0;\n        Polyhedron chosenPoly;\n        if (theta < 1e-6){\n            theta = 1e-6;\n        }\n        Eigen::Vector3d position, tangentLine;\n        map.getGlobalCommand(theta, position, tangentLine);\n\n        // find the max Polyhedron intersecting theta face\n        double maxArea = 0;\n        // cout << \"theta: \" << stateTmp.coeffRef(7,0) << endl;\n        // find tunnel for this horizon: \n        for (int i=0; i < map.corridor.polys.size(); ++i){\n            bool inThisPoly = false;\n            for (int k=0; k<map.corridor.polys[i].starter.size(); ++k){\n                bool in = theta >= map.corridor.polys[i].starter[k] && theta <= map.corridor.polys[i].ender[k];\n                inThisPoly = inThisPoly || in ;\n            }\n            if ( inThisPoly ){\n                // std::cout << \"polyindex: \" << i << std::endl;\n                // cout << \"pos: \" << position.transpose() << endl;\n                // cout << \"tan: \" << tangentLine.transpose() << endl;\n                // cout << \"i: \" << i << endl;\n                map.corridor.FindPolygon(position, tangentLine, i);\n                if(maxArea < map.corridor.tunnelArea){\n                    maxArea = map.corridor.tunnelArea;\n                    chosenPoly = map.corridor.tunnel;\n                }\n                // std::cout << \"area: \" << maxArea << endl;\n            }\n        }\n        displayPtr->displayOneTunnel(horizon_, theta);\n        numConstrains = chosenPoly.rows();\n\n        Cn.resize(numConstrains, numState);\n        Cnb.resize(numConstrains, 1);\n        for (int i=0; i<numConstrains; ++i){\n            for (int j=0; j<Model::numDimention; ++j){\n                Cn.coeffRef(i,Model::numOrder*j) = chosenPoly(i,j);\n            }\n            Cnb.coeffRef(i,0) = chosenPoly(i,3);\n        }\n        sp::colMajor::addBlock(Ck, Cn);\n        sp::colMajor::addRows(Ckb, Cnb);\n    }\n    int MpcSolver::solveMpcQp(Eigen::SparseMatrix<double> &stateTmp){\n        clock_t t1, t2;\n        // t1 = clock();\n        // t2 = clock();\n        // std:: cout << \"first time : \" << (double)(t2 - t1)/CLOCKS_PER_SEC << endl;\n        Eigen::SparseMatrix<double> state = stateTmp;\n        // Vector3d posNow(stateTmp.coeffRef(0,0), \n        //              stateTmp.coeffRef(Model::numOrder,0), \n        //              stateTmp.coeffRef(2*Model::numOrder,0));\n        // double nearestTheta = map.findNearestTheta(state.coeffRef(3*Model::numOrder,0), posNow);\n        // double nearestTheta = map.findNearestTheta(posNow);\n        // state.coeffRef(3*Model::numOrder,0) = nearestTheta;\n        Eigen::SparseMatrix<double> inputPredict1toN = inputPredict.block(0,1,numInput,horizon-1);\n        sp::colMajor::setCols(inputPredict, inputPredict1toN, 1);\n        int solveStatus = 1;\n\n        Ck.resize(0, 0);\n        Ckb.resize(0,0);\n        \n        // t1 = clock();\n        displayPtr->displayDrone(state);\n        displayPtr->displayTheta(state);\n        double theta = 0;\n        // displayPtr->clearTunnels();\n        for (int horizonI=0; horizonI<horizon; ++horizonI){\n            if(initStatus){\n                theta = model.ts*horizon;\n            }\n            else{\n                theta = state.coeffRef(numState-Model::numOrder, 0);\n            }\n            calculateCost(theta, horizonI);\n            if (corridorConstrains){\n                calculateConstrains(state, horizonI);\n            }\n            state = model.Ad * state + model.Bd * inputPredict.col(horizonI);\n        }\n        // t2 = clock();\n        // std:: cout << \"calculate cost : \" << (double)(t2 - t1)/CLOCKS_PER_SEC << endl;\n        // t1 = clock();\n        Q = BBT * Qk * BB + In;\n        c = BBT * Qk*AA*stateTmp + BBT * qk;\n        // t2 = clock();\n        // std:: cout << \"boundary : \" << (double)(t2 - t1)/CLOCKS_PER_SEC << endl;\n        // state lower and upper constrains : \n        // t1 = clock();\n\n        C = BB;\n        \n        // ! set terminal velocity constrain\n        Vector3d pos ,vel;\n        map.getGlobalCommand(theta, pos ,vel);\n        xu.coeffRef(numState*(horizon-1)+1, 0) =  1*fabs(vel(0));\n        xu.coeffRef(numState*(horizon-1)+4, 0) =  1*fabs(vel(1));\n        xu.coeffRef(numState*(horizon-1)+7, 0) =  1*fabs(vel(2));\n        xl.coeffRef(numState*(horizon-1)+1, 0) = -1*fabs(vel(0));\n        xl.coeffRef(numState*(horizon-1)+4, 0) = -1*fabs(vel(1));\n        xl.coeffRef(numState*(horizon-1)+7, 0) = -1*fabs(vel(2));\n        // xu.coeffRef(numState*(horizon-1)+10, 0) = 1.1;\n        cupp = xu - SparseMatrix<double>(AA*stateTmp);\n        clow = xl - SparseMatrix<double>(AA*stateTmp);\n        // C.resize(0,0);\n        // t2 = clock();\n        // std:: cout << \"boundary b : \" << (double)(t2 - t1)/CLOCKS_PER_SEC << endl;\n        // add real constrains\n        // t1 = clock();\n        if (Ck.rows() != 0){\n            // lower bound should be quite small\n            Eigen::SparseMatrix<double> zeroRows(Ck.rows(),1);\n            for (int i=0; i<Ck.rows(); ++i){\n                zeroRows.coeffRef(i, 0) = -1e10;\n            }\n            sp::colMajor::addRows(clow, zeroRows);\n            zeroRows = - Ck * AA * stateTmp - Ckb;\n            sp::colMajor::addRows(cupp, zeroRows);\n            SparseMatrix<double> Ck2 = Ck * BB;\n            sp::colMajor::addRows(C, Ck2);\n            sp::colMajor::addRows(C, Inu);\n            sp::colMajor::addRows(cupp, uu);\n            sp::colMajor::addRows(clow, ul);\n        }\n        // t2 = clock();\n        // std:: cout << \"constrains : \" << (double)(t2 - t1)/CLOCKS_PER_SEC << endl;\n        // printMatrices();\n        clock_t t_osqp_start = clock();\n        osqpInterface.updateMatrices(Q, c, A, b, C, clow, cupp, ul, uu);\n        osqpInterface.solveQP();\n        solveStatus = osqpInterface.solveStatus();\n        // if (solveStatus == OSQP_PRIMAL_INFEASIBLE){\n        //     osqpInterface.updateMatrices(Q, c, A, b, C, clow, cupp, ul, uu, 0);\n        //     osqpInterface.solveQP();\n        //     solveStatus = osqpInterface.solveStatus();\n        // }\n        // cout << \"solveStatus: \" << solveStatus << endl;\n        auto solPtr = osqpInterface.solPtr();\n        clock_t t_osqp_over = clock();\n        double time = (double)(t_osqp_over - t_osqp_start) / CLOCKS_PER_SEC;\n        // cout << \"osqp time: \" << time << \", \";\n        // printMatrices();\n        if (solveStatus == OSQP_SOLVED){\n            // cout << \"Solved! \" << endl;\n            state = stateTmp;\n            for (int i=0; i<horizon; ++i){\n                for (int j=0; j<numInput; ++j){\n                    inputPredict.coeffRef(j,i) = solPtr->x[i*numInput+j];\n                }\n                state = model.Ad * state + model.Bd * inputPredict.col(i);\n                sp::colMajor::setCols(statePredict, state, i);\n            }\n        }\n        else {\n            // cout << \"failed: \" << solveStatus << endl;\n            // printMatrices();\n\n            sp::colMajor::setCols(inputPredict, inputPredict1toN, 0);\n            SparseMatrix<double> statePredict1toN = statePredict.block(0,1,numState,horizon-1);\n            sp::colMajor::setCols(statePredict, statePredict1toN, 0);\n        }\n        \n        if(initStatus){\n            initStatus = false;\n        }\n        displayPtr->displayPredict(statePredict);\n        if(solveStatus == OSQP_SOLVED) \n            return 0; // 0 for solved\n        else \n            return 1;\n    };\n    void MpcSolver::printMatrices(){\n        cout << \"Q:\\n\" << Q << endl;\n        cout << \"c:\\n\" << c << endl;\n        cout << \"A:\\n\" << A << endl;\n        cout << \"b:\\n\" << b << endl;\n        cout << \"C:\\n\" << C << endl;\n        cout << \"clow:\\n\" << clow << endl;\n        cout << \"cupp:\\n\" << cupp << endl;\n        cout << \"ul:\\n\" << ul << endl;\n        cout << \"uu:\\n\" << uu << endl;\n    }\n} // namespace ft\n"
  },
  {
    "path": "src/cmpcc/src/my_spline.cpp",
    "content": "#include \"my_spline.h\"\n\nnamespace ft{\n    namespace tk{\n\n        // band_matrix implementation\n        // -------------------------\n        band_matrix::band_matrix(int dim, int n_u, int n_l)\n        {\n            resize(dim, n_u, n_l);\n        }\n        void band_matrix::resize(int dim, int n_u, int n_l)\n        {\n            assert(dim>0);\n            assert(n_u>=0);\n            assert(n_l>=0);\n            m_upper.resize(n_u+1);\n            m_lower.resize(n_l+1);\n            for(size_t i=0; i<m_upper.size(); i++) {\n                m_upper[i].resize(dim);\n            }\n            for(size_t i=0; i<m_lower.size(); i++) {\n                m_lower[i].resize(dim);\n            }\n        }\n        int band_matrix::dim() const\n        {\n            if(m_upper.size()>0) {\n                return m_upper[0].size();\n            } else {\n                return 0;\n            }\n        }\n        // defines the new operator (), so that we can access the elements\n        // by A(i,j), index going from i=0,...,dim()-1\n        double & band_matrix::operator () (int i, int j)\n        {\n            int k=j-i;       // what band is the entry\n            assert( (i>=0) && (i<dim()) && (j>=0) && (j<dim()) );\n            assert( (-num_lower()<=k) && (k<=num_upper()) );\n            // k=0 -> diogonal, k<0 lower left part, k>0 upper right part\n            if(k>=0)   return m_upper[k][i];\n            else\t    return m_lower[-k][i];\n        }\n        double band_matrix::operator () (int i, int j) const\n        {\n            int k=j-i;       // what band is the entry\n            assert( (i>=0) && (i<dim()) && (j>=0) && (j<dim()) );\n            assert( (-num_lower()<=k) && (k<=num_upper()) );\n            // k=0 -> diogonal, k<0 lower left part, k>0 upper right part\n            if(k>=0)   return m_upper[k][i];\n            else\t    return m_lower[-k][i];\n        }\n        // second diag (used in LU decomposition), saved in m_lower\n        double band_matrix::saved_diag(int i) const\n        {\n            assert( (i>=0) && (i<dim()) );\n            return m_lower[0][i];\n        }\n        double & band_matrix::saved_diag(int i)\n        {\n            assert( (i>=0) && (i<dim()) );\n            return m_lower[0][i];\n        }\n\n        // LR-Decomposition of a band matrix\n        void band_matrix::lu_decompose()\n        {\n            int  i_max,j_max;\n            int  j_min;\n            double x;\n\n            // preconditioning\n            // normalize column i so that a_ii=1\n            for(int i=0; i<this->dim(); i++) {\n                assert(this->operator()(i,i)!=0.0);\n                this->saved_diag(i)=1.0/this->operator()(i,i);\n                j_min=std::max(0,i-this->num_lower());\n                j_max=std::min(this->dim()-1,i+this->num_upper());\n                for(int j=j_min; j<=j_max; j++) {\n                    this->operator()(i,j) *= this->saved_diag(i);\n                }\n                this->operator()(i,i)=1.0;          // prevents rounding errors\n            }\n\n            // Gauss LR-Decomposition\n            for(int k=0; k<this->dim(); k++) {\n                i_max=std::min(this->dim()-1,k+this->num_lower());  // num_lower not a mistake!\n                for(int i=k+1; i<=i_max; i++) {\n                    assert(this->operator()(k,k)!=0.0);\n                    x=-this->operator()(i,k)/this->operator()(k,k);\n                    this->operator()(i,k)=-x;                         // assembly part of L\n                    j_max=std::min(this->dim()-1,k+this->num_upper());\n                    for(int j=k+1; j<=j_max; j++) {\n                        // assembly part of R\n                        this->operator()(i,j)=this->operator()(i,j)+x*this->operator()(k,j);\n                    }\n                }\n            }\n        }\n        // solves Ly=b\n        std::vector<double> band_matrix::l_solve(const std::vector<double>& b) const\n        {\n            assert( this->dim()==(int)b.size() );\n            std::vector<double> x(this->dim());\n            int j_start;\n            double sum;\n            for(int i=0; i<this->dim(); i++) {\n                sum=0;\n                j_start=std::max(0,i-this->num_lower());\n                for(int j=j_start; j<i; j++) sum += this->operator()(i,j)*x[j];\n                x[i]=(b[i]*this->saved_diag(i)) - sum;\n            }\n            return x;\n        }\n        // solves Rx=y\n        std::vector<double> band_matrix::r_solve(const std::vector<double>& b) const\n        {\n            assert( this->dim()==(int)b.size() );\n            std::vector<double> x(this->dim());\n            int j_stop;\n            double sum;\n            for(int i=this->dim()-1; i>=0; i--) {\n                sum=0;\n                j_stop=std::min(this->dim()-1,i+this->num_upper());\n                for(int j=i+1; j<=j_stop; j++) sum += this->operator()(i,j)*x[j];\n                x[i]=( b[i] - sum ) / this->operator()(i,i);\n            }\n            return x;\n        }\n\n        std::vector<double> band_matrix::lu_solve(const std::vector<double>& b,\n                                                  bool is_lu_decomposed)\n        {\n            assert( this->dim()==(int)b.size() );\n            std::vector<double>  x,y;\n            if(is_lu_decomposed==false) {\n                this->lu_decompose();\n            }\n            y=this->l_solve(b);\n            x=this->r_solve(y);\n            return x;\n        }\n\n\n        // spline implementation\n        // -----------------------\n        spline::spline(): m_left(second_deriv), m_right(second_deriv),\n                      m_left_value(0.0), m_right_value(0.0),\n                      m_force_linear_extrapolation(false)\n            {\n                ;\n            }\n        void spline::set_boundary(spline::bd_type left, double left_value,\n                                  spline::bd_type right, double right_value,\n                                  bool force_linear_extrapolation)\n        {\n            assert(m_x.size()==0);          // set_points() must not have happened yet\n            m_left=left;\n            m_right=right;\n            m_left_value=left_value;\n            m_right_value=right_value;\n            m_force_linear_extrapolation=force_linear_extrapolation;\n        }\n\n\n        void spline::set_points(const std::vector<double>& x,\n                                const std::vector<double>& y, bool cubic_spline)\n        {\n            assert(x.size()==y.size());\n            assert(x.size()>2);\n            m_x=x;\n            m_y=y;\n            int   n=x.size();\n            for(int i=0; i<n-1; i++) {\n                assert(m_x[i]<m_x[i+1]);\n            }\n\n            if(cubic_spline==true) { // cubic spline interpolation\n                // setting up the matrix and right hand side of the equation system\n                // for the parameters b[]\n                band_matrix A(n,1,1);\n                std::vector<double>  rhs(n);\n                for(int i=1; i<n-1; i++) {\n                    A(i,i-1)=1.0/3.0*(x[i]-x[i-1]);\n                    A(i,i)=2.0/3.0*(x[i+1]-x[i-1]);\n                    A(i,i+1)=1.0/3.0*(x[i+1]-x[i]);\n                    rhs[i]=(y[i+1]-y[i])/(x[i+1]-x[i]) - (y[i]-y[i-1])/(x[i]-x[i-1]);\n                }\n                // boundary conditions\n                if(m_left == spline::second_deriv) {\n                    // 2*b[0] = f''\n                    A(0,0)=2.0;\n                    A(0,1)=0.0;\n                    rhs[0]=m_left_value;\n                } else if(m_left == spline::first_deriv) {\n                    // c[0] = f', needs to be re-expressed in terms of b:\n                    // (2b[0]+b[1])(x[1]-x[0]) = 3 ((y[1]-y[0])/(x[1]-x[0]) - f')\n                    A(0,0)=2.0*(x[1]-x[0]);\n                    A(0,1)=1.0*(x[1]-x[0]);\n                    rhs[0]=3.0*((y[1]-y[0])/(x[1]-x[0])-m_left_value);\n                } else {\n                    assert(false);\n                }\n                if(m_right == spline::second_deriv) {\n                    // 2*b[n-1] = f''\n                    A(n-1,n-1)=2.0;\n                    A(n-1,n-2)=0.0;\n                    rhs[n-1]=m_right_value;\n                } else if(m_right == spline::first_deriv) {\n                    // c[n-1] = f', needs to be re-expressed in terms of b:\n                    // (b[n-2]+2b[n-1])(x[n-1]-x[n-2])\n                    // = 3 (f' - (y[n-1]-y[n-2])/(x[n-1]-x[n-2]))\n                    A(n-1,n-1)=2.0*(x[n-1]-x[n-2]);\n                    A(n-1,n-2)=1.0*(x[n-1]-x[n-2]);\n                    rhs[n-1]=3.0*(m_right_value-(y[n-1]-y[n-2])/(x[n-1]-x[n-2]));\n                } else {\n                    assert(false);\n                }\n\n                // solve the equation system to obtain the parameters b[]\n                m_b=A.lu_solve(rhs);\n\n                // calculate parameters a[] and c[] based on b[]\n                m_a.resize(n);\n                m_c.resize(n);\n                for(int i=0; i<n-1; i++) {\n                    m_a[i]=1.0/3.0*(m_b[i+1]-m_b[i])/(x[i+1]-x[i]);\n                    m_c[i]=(y[i+1]-y[i])/(x[i+1]-x[i])\n                           - 1.0/3.0*(2.0*m_b[i]+m_b[i+1])*(x[i+1]-x[i]);\n                }\n            } else { // linear interpolation\n                m_a.resize(n);\n                m_b.resize(n);\n                m_c.resize(n);\n                for(int i=0; i<n-1; i++) {\n                    m_a[i]=0.0;\n                    m_b[i]=0.0;\n                    m_c[i]=(m_y[i+1]-m_y[i])/(m_x[i+1]-m_x[i]);\n                }\n            }\n\n            // for left extrapolation coefficients\n            m_b0 = (m_force_linear_extrapolation==false) ? m_b[0] : 0.0;\n            m_c0 = m_c[0];\n\n            // for the right extrapolation coefficients\n            // f_{n-1}(x) = b*(x-x_{n-1})^2 + c*(x-x_{n-1}) + y_{n-1}\n            double h=x[n-1]-x[n-2];\n            // m_b[n-1] is determined by the boundary condition\n            m_a[n-1]=0.0;\n            m_c[n-1]=3.0*m_a[n-2]*h*h+2.0*m_b[n-2]*h+m_c[n-2];   // = f'_{n-2}(x_{n-1})\n            if(m_force_linear_extrapolation==true)\n                m_b[n-1]=0.0;\n        }\n\n        double spline::operator()(double x, int dd) const {\n            assert(dd == 0 || dd == 1 || dd == 2);\n            size_t n=m_x.size();\n            x = std::fmod(x, m_x[n-1]);\n            // find the closest point m_x[idx] < x, idx=0 even if x<m_x[0]\n            std::vector<double>::const_iterator it;\n            it=std::lower_bound(m_x.begin(),m_x.end(),x);\n            int idx=std::max( int(it-m_x.begin())-1, 0);\n\n            double h=x-m_x[idx];\n            double interpol;\n            // interpolation\n            if (dd == 0){\n                interpol=((m_a[idx]*h + m_b[idx])*h + m_c[idx])*h + m_y[idx];\n            }\n            else if(dd == 1) {\n                interpol = (3 * m_a[idx] * h + 2 * m_b[idx]) * h + m_c[idx];\n            }\n            else if (dd == 2){\n                interpol = 6 * m_a[idx] * h + 2 * m_b[idx];\n            }\n\n            return interpol;\n        }\n    } //namespace tk\n} // namespace ft\n"
  },
  {
    "path": "src/cmpcc/src/osqp_interface.cpp",
    "content": "#include \"osqp_interface.h\"\n#include <iostream>\n\nusing namespace std;\nusing namespace Eigen;\n\nnamespace osqp{\nOSQPInterface::OSQPInterface(){\n    settings = new OSQPSettings;\n    data     = new OSQPData;\n}\nOSQPInterface::~OSQPInterface(){\n    delete settings;\n    delete data;\n    osqp_cleanup(work);\n}\nint OSQPInterface::updateMatrices(\n    Eigen::SparseMatrix<double> & Q_, \n    Eigen::SparseMatrix<double> & c_, \n    Eigen::SparseMatrix<double> & A_, \n    Eigen::SparseMatrix<double> & b_, \n    Eigen::SparseMatrix<double> & C_, \n    Eigen::SparseMatrix<double> & clow_, \n    Eigen::SparseMatrix<double> & cupp_,\n    Eigen::SparseMatrix<double> & xlow_,\n    Eigen::SparseMatrix<double> & xupp_,\n    int warmStart\n){\n    // change Q_ upper triangular\n    Q_ = Q_.triangularView<Upper>();\n    // compress the matrices for osqp data\n    Q_.makeCompressed();\n    c_.makeCompressed();\n    A_.makeCompressed();\n    b_.makeCompressed();\n    C_.makeCompressed();\n    clow_.makeCompressed();\n    cupp_.makeCompressed();\n    xlow_.makeCompressed();\n    xupp_.makeCompressed();\n\n    data->n = Q_.rows();\n    data->m = C_.rows();\n    // use the eigen sparse ptrs make it faster\n    data->P = csc_matrix(data->n, data->n, Q_.nonZeros(), \n        Q_.valuePtr(), Q_.innerIndexPtr(), Q_.outerIndexPtr());\n    data->q = new c_float[c_.rows()];\n    for (int i=0; i<c_.rows(); ++i){\n        data->q[i] = c_.coeffRef(i,0);\n    }\n    data->A = csc_matrix(data->m, data->n, C_.nonZeros(), \n        C_.valuePtr(), C_.innerIndexPtr(), C_.outerIndexPtr());\n    data->l = new c_float[clow_.rows()];\n    for (int i=0; i<clow_.rows(); ++i){\n        data->l[i] = clow_.coeffRef(i,0);\n    }\n    data->u = new c_float[cupp_.rows()];\n    for (int i=0; i<cupp_.rows(); ++i){\n        data->u[i] = cupp_.coeffRef(i,0);\n    }\n    if (settings) osqp_set_default_settings(settings);\n    settings->warm_start = warmStart;\n    exitflag = osqp_setup(&work, data, settings);\n    delete data->q;\n    delete data->l;\n    delete data->u;\n    return 0;\n}\nint OSQPInterface::solveQP(){\n    // Solve Problem\n    return osqp_solve(work);\n}\nint OSQPInterface::solveStatus(){\n    return work->info->status_val;\n}\nOSQPSolution* OSQPInterface::solPtr(){\n    return work->solution;\n}\n}// namespace osqp"
  },
  {
    "path": "src/cmpcc/src/osqp_test.cpp",
    "content": "#include <Eigen/Sparse>\n#include <iostream>\n#include \"osqp_interface.h\"\n\nusing namespace Eigen;\nusing namespace std;\nusing namespace osqp;\n\nvoid printQ(Eigen::SparseMatrix<double> Q_){\n  cout << Q_ << endl;\n  for(int i=0; i<Q_.nonZeros(); ++i){\n    cout << Q_.innerIndexPtr()[i] << \" \";\n  }\n  cout << endl;\n  for(int i=0; i<Q_.cols()+1; ++i){\n    cout << Q_.outerIndexPtr()[i] << \" \";\n  }\n  cout << endl;\n}\n\nint main(int argc, char const *argv[])\n{\n  Eigen::SparseMatrix<double> Q_; \n  Eigen::SparseMatrix<double> c_; \n  Eigen::SparseMatrix<double> A_; \n  Eigen::SparseMatrix<double> b_; \n  Eigen::SparseMatrix<double> C_; \n  Eigen::SparseMatrix<double> clow_;\n  Eigen::SparseMatrix<double> cupp_;\n  Eigen::SparseMatrix<double> xlow_;\n  Eigen::SparseMatrix<double> xupp_;\n  Q_.resize(2,2);\n  Q_.coeffRef(0,0) = 4;\n  Q_.coeffRef(0,1) = 1;\n  Q_.coeffRef(1,1) = 2;\n  c_.resize(2,1);\n  c_.coeffRef(0,0) = 1;\n  c_.coeffRef(1,0) = 1;\n  C_.resize(3,2);\n  C_.coeffRef(0,0) = 1;\n  C_.coeffRef(1,0) = 1;\n  C_.coeffRef(0,1) = 1;\n  C_.coeffRef(2,1) = 1;\n  clow_.resize(3,1);\n  clow_.coeffRef(0,0) = 1;\n  clow_.coeffRef(1,0) = 0;\n  clow_.coeffRef(2,0) = 0;\n  cupp_.resize(3,1);\n  cupp_.coeffRef(0,0) = 1;\n  cupp_.coeffRef(1,0) = 0.7;\n  cupp_.coeffRef(2,0) = 0.7;\n  // printQ(Q_);\n  osqp::OSQPInterface osqpInterface;\n  osqpInterface.updateMatrices(Q_, c_, A_, b_, C_, clow_, cupp_, xlow_, xupp_);\n  osqpInterface.solveQP();\n  auto solPtr = osqpInterface.solPtr();\n  cout << solPtr->x[0] << endl;\n  return 0;\n}\n"
  },
  {
    "path": "src/cmpcc/src/plot_bag_node.cpp",
    "content": "#include <quadrotor_msgs/PositionCommand.h>\n#include <geometry_msgs/Point.h>\n#include <geometry_msgs/PoseStamped.h>\n#include <nav_msgs/Path.h>\n#include <nav_msgs/Odometry.h>\n#include <sensor_msgs/Imu.h>\n#include <ros/ros.h>\n#include \"mpc_solver.h\"\n\nusing namespace std;\nusing namespace ft;\nusing namespace Eigen;\n\nft::MpcSolver simSolver;\nMatrix3d w_R_odom;\nVector3d w_t_odom;\n\ndouble start_time;\n\nvoid rcvTransformCallBack(geometry_msgs::PoseConstPtr msg){\n    Quaterniond q(msg->orientation.w, msg->orientation.x, msg->orientation.y, msg->orientation.z);\n    w_R_odom = q.toRotationMatrix();\n    w_t_odom << msg->position.x, msg->position.y, msg->position.z;\n}\n\nvoid odom_callback(const nav_msgs::Odometry& odom){\n    double t = odom.header.stamp.toSec() - start_time;\n    if (t < 0){\n        return;\n    }\n    Vector3d pDrone( odom.pose.pose.position.x, odom.pose.pose.position.y, odom.pose.pose.position.z);\n    Vector3d vDrone( odom.twist.twist.linear.x, odom.twist.twist.linear.y, odom.twist.twist.linear.z);\n    Vector3d aDrone( odom.twist.twist.angular.x, odom.twist.twist.angular.y, odom.twist.twist.angular.z);\n    pDrone= w_R_odom * pDrone + w_t_odom;\n    vDrone = w_R_odom * vDrone;\n    aDrone = w_R_odom * aDrone;\n    double theta = simSolver.map.findNearestTheta(pDrone);\n    Vector3d p, v, a;\n    simSolver.map.getGlobalCommand(t, p, v, a);\n    cout << t << \" \"\n         << pDrone.transpose() << \" \"\n         << vDrone.transpose() << \" \"\n         << aDrone.transpose() << \" \"\n         << p.transpose() << \" \"         \n         << v.transpose() << \" \"\n         << a.transpose() << \" \"\n         << endl;\n}\n\nvoid cmd_callback(const quadrotor_msgs::PositionCommand& cmd){\n    double t = cmd.header.stamp.toSec() - start_time;\n    if (t < 0){\n        return;\n    }\n    Vector3d pos, vel, acc;\n    pos << cmd.position.x, cmd.position.y, cmd.position.z;\n    double theta = simSolver.map.findNearestTheta(pos);\n    simSolver.map.getGlobalCommand(theta, pos, vel, acc);\n    pos = w_R_odom.transpose() * (pos - w_t_odom);\n    vel = w_R_odom.transpose() * vel;\n    acc = w_R_odom.transpose() * acc;\n    cout << t << \" \" \n         << cmd.position.x << \" \"\n         << cmd.position.y << \" \"\n         << cmd.position.z << \" \"\n         << cmd.velocity.x << \" \"\n         << cmd.velocity.y << \" \"\n         << cmd.velocity.z << \" \"\n         << cmd.acceleration.x << \" \"\n         << cmd.acceleration.y << \" \"\n         << cmd.acceleration.z << \" \"\n         << pos.transpose() << \" \"\n         << vel.transpose() << \" \"\n         << acc.transpose() << \" \"\n         << endl;\n}\n\nint main(int argc, char **argv){\n    ros::init(argc, argv, \"plot_bag_node\");\n    ros::NodeHandle n(\"~\");\n    n.getParam(\"start_time\", start_time);\n    ros::Subscriber sub_odom = n.subscribe(\"/vins_estimator/imu_propagate\", 5 , odom_callback, ros::TransportHints().tcpNoDelay());\n    // ros::Subscriber sub_odom = n.subscribe(\"drone_1/state_ukf/odom\", 5 , odom_callback, ros::TransportHints().tcpNoDelay());\n    // ros::Subscriber sub_cmd = n.subscribe(\"/position_cmd\", 5 , cmd_callback, ros::TransportHints().tcpNoDelay());\n    // ros::Subscriber sub_cmd = n.subscribe(\"drone_1/position_cmd\", 5 , cmd_callback, ros::TransportHints().tcpNoDelay());\n    ros::Subscriber transformation_sub = n.subscribe(\"/loop_fusion/pg_T_vio\", 1, rcvTransformCallBack, ros::TransportHints().tcpNoDelay());\n    w_R_odom = Matrix3d::Identity();\n    w_t_odom = Vector3d::Zero();\n    ros::spin();\n    return 0;\n}\n\n\n\n"
  },
  {
    "path": "src/cmpcc/src/simulation_node.cpp",
    "content": "#include <quadrotor_msgs/PositionCommand.h>\n#include <geometry_msgs/Point.h>\n#include <geometry_msgs/PoseStamped.h>\n#include <nav_msgs/Path.h>\n#include <nav_msgs/Odometry.h>\n#include <sensor_msgs/Imu.h>\n#include <ros/ros.h>\n#include <sensor_msgs/Joy.h>\n#include \"mpc_solver.h\"\n\nusing namespace std;\nusing namespace Eigen;\nusing namespace ft;\n\nros::Publisher cmd_pub, refer_pub, drone_pub, global_pub, vis_polytope_pub, flight_tunnel_pub, predict_pub, globalOdom_pub;\n\nft::MpcSolver simSolver;\nquadrotor_msgs::PositionCommand cmdMsg;\nnav_msgs::Odometry globalOdom;\n// Eigen::SparseMatrix<double,Eigen::RowMajor> stateOdom(Model::numState,1);\nEigen::SparseMatrix<double> stateCmd(Model::numState,1);\nEigen::SparseMatrix<double> stateMpc(Model::numState,1);\nEigen::SparseMatrix<double> stateOdom(Model::numState,1);\nEigen::SparseMatrix<double> stateOdomPrevious(Model::numState,1);\ndouble mpcT = 0.05;\ndouble odomT;\nEigen::Vector3d pDrone, vDrone, aDrone;\nbool mpcInit, odomRead, isGlobal, getDestination;\ngeometry_msgs::Point tmpPoint;\ngeometry_msgs::Vector3 tmpVector;\nros::Time tStart, tFinal, tOdom, tMpc;\n\nbool reset_now = false;\nint fail_times = 0;\n\nEigen::SparseMatrix<double> stateL;\nEigen::SparseMatrix<double> stateR;\nbool state_predict_done = false;\n\ndouble wind_x = 0;\ndouble wind_y = 0;\n\nvoid odom_callback(const nav_msgs::Odometry& odom){\n    odomRead = true;\n    odomT = (odom.header.stamp - tOdom).toSec();\n    tOdom = odom.header.stamp;\n    pDrone << odom.pose.pose.position.x, odom.pose.pose.position.y, odom.pose.pose.position.z;\n    vDrone << odom.twist.twist.linear.x, odom.twist.twist.linear.y, odom.twist.twist.linear.z;\n    aDrone << odom.twist.twist.angular.x, odom.twist.twist.angular.y, odom.twist.twist.angular.z;\n    stateOdomPrevious = stateOdom;\n    stateOdom.coeffRef(0,0) = pDrone(0);\n    stateOdom.coeffRef(3,0) = pDrone(1);\n    stateOdom.coeffRef(6,0) = pDrone(2);\n    stateOdom.coeffRef(1,0) = vDrone(0);\n    stateOdom.coeffRef(4,0) = vDrone(1);\n    stateOdom.coeffRef(7,0) = vDrone(2);\n    stateOdom.coeffRef(9,0) = simSolver.map.findNearestTheta(pDrone);\n    odomRead = false;\n}\n\nvoid mpc_callback(const ros::TimerEvent& event){\n    while(ros::ok() && odomRead);\n    mpcInit = true;\n    tMpc = ros::Time::now();\n    stateL = simSolver.statePredict.col(0);\n    stateR = simSolver.statePredict.col(1);\n    // calculate stateMPC\n    stateMpc = (stateOdom - stateOdomPrevious)/odomT*(tMpc-tOdom).toSec() + stateOdom;\n    // update some state from last horizon\n    if (fail_times > 3){\n        stateMpc.coeffRef( 2, 0) = 0;\n        stateMpc.coeffRef( 5, 0) = 0;\n        stateMpc.coeffRef( 8, 0) = 0;\n        stateMpc.coeffRef(10, 0) = 0;\n        stateMpc.coeffRef(11, 0) = 0;\n        simSolver.initStatus = true;\n        mpcInit = false;\n        if( simSolver.solveMpcQp(stateMpc) ){\n            ROS_ERROR(\"come on! Your disturbance too fierce!\");\n            ros::shutdown();\n        }\n        else{\n            fail_times = 0;\n        }\n    }\n    else{\n        stateMpc.coeffRef(2, 0) = simSolver.statePredict.coeffRef(2, 1); // ax\n        stateMpc.coeffRef(5, 0) = simSolver.statePredict.coeffRef(5, 1); // ay\n        stateMpc.coeffRef(8, 0) = simSolver.statePredict.coeffRef(8, 1); // az\n        stateMpc.coeffRef(10,0) = simSolver.statePredict.coeffRef(10,1); // vtheta\n        stateMpc.coeffRef(11,0) = simSolver.statePredict.coeffRef(11,1); // atheta\n        mpcInit = false;\n        if( simSolver.solveMpcQp(stateMpc) ){\n            fail_times ++;\n        }\n        else{\n            fail_times = 0;\n        }\n    }\n\n    state_predict_done = true;\n    \n    refer_pub.publish(simSolver.displayPtr->refTraj_msg);\n    global_pub.publish(simSolver.displayPtr->theta_msg);\n    drone_pub.publish(simSolver.displayPtr->drone_msg);\n    vis_polytope_pub.publish(simSolver.displayPtr->corridor_array_msg);\n    simSolver.displayPtr->pubTunnels(flight_tunnel_pub);\n    predict_pub.publish(simSolver.displayPtr->trajPred_msg);\n}\n\nvoid cmd_callback(const ros::TimerEvent& event){\n    if (state_predict_done){\n        while(ros::ok() && mpcInit);\n        double deltaT = (ros::Time::now() - tMpc).toSec();\n        stateCmd = stateL + (stateR-stateL)*deltaT/mpcT;\n        tmpPoint.x = stateCmd.coeffRef(0,0);\n        tmpPoint.y = stateCmd.coeffRef(3,0);\n        tmpPoint.z = stateCmd.coeffRef(6,0);\n        cmdMsg.position = tmpPoint;\n        tmpVector.x = stateCmd.coeffRef(1,0);\n        tmpVector.y = stateCmd.coeffRef(4,0);\n        tmpVector.z = stateCmd.coeffRef(7,0);\n        cmdMsg.velocity = tmpVector;\n        tmpVector.x = stateCmd.coeffRef(2,0) + wind_x;\n        tmpVector.y = stateCmd.coeffRef(5,0) + wind_y;\n        tmpVector.z = stateCmd.coeffRef(8,0);\n        cmdMsg.acceleration = tmpVector;\n        cmdMsg.header.stamp = ros::Time::now();\n        cmd_pub.publish(cmdMsg);\n    }\n}\n\nvoid joy_callback(const sensor_msgs::Joy::ConstPtr& msg){\n    wind_x = 3 * msg->axes[0];\n    wind_y = 3 * msg->axes[1];\n}\n\n\nint main(int argc, char **argv)\n{\n    ros::init(argc, argv, \"simulation_node\");\n    ros::NodeHandle nodeHandle;\n    cmd_pub = nodeHandle.advertise<quadrotor_msgs::PositionCommand>(\"position_cmd\",1);\n    refer_pub = nodeHandle.advertise<nav_msgs::Path>(\"refer_path\", 1);\n    drone_pub = nodeHandle.advertise<visualization_msgs::Marker>(\"drone_pose\", 1);\n    global_pub = nodeHandle.advertise<visualization_msgs::Marker>(\"global_pose\", 1);\n    ros::Subscriber sub_odom = nodeHandle.subscribe(\"odom\", 5 , odom_callback, ros::TransportHints().tcpNoDelay());\n    ros::Subscriber sub_joy = nodeHandle.subscribe(\"/joy\", 5 , joy_callback, ros::TransportHints().tcpNoDelay());\n    vis_polytope_pub  = nodeHandle.advertise<decomp_ros_msgs::PolyhedronArray>(\"polyhedron_corridor_mesh\", 1, true);\n    flight_tunnel_pub = nodeHandle.advertise<decomp_ros_msgs::PolyhedronArray>(\"flight_tunnel\", 1, true);\n    predict_pub = nodeHandle.advertise<nav_msgs::Path>(\"predict_path\", 1);\n    globalOdom_pub = nodeHandle.advertise<nav_msgs::Odometry>(\"globalOdom\", 1);\n\n    ros::Timer timer_mpc = nodeHandle.createTimer(ros::Duration(mpcT), mpc_callback);\n    ros::Timer timer_cmd = nodeHandle.createTimer(ros::Duration(0.01), cmd_callback);\n\n    ros::MultiThreadedSpinner spinner(4);\n\n    cmdMsg.header.frame_id = \"world\";\n    \n    // init position: \n    simSolver.map.getGlobalCommand(0, pDrone);\n    tmpPoint.x = pDrone(0);\n    tmpPoint.y = pDrone(1);\n    tmpPoint.z = pDrone(2);\n    cmdMsg.position = tmpPoint;\n    tmpVector.x = 0;\n    tmpVector.y = 0;\n    tmpVector.z = 0;\n    cmdMsg.velocity = tmpVector;\n    tmpVector.x = 0;\n    tmpVector.y = 0;\n    tmpVector.z = 0;\n    cmdMsg.acceleration = tmpVector;\n    ros::Rate loopRate(10);\n    ros::Time startT = ros::Time::now();\n    while(ros::ok()){\n        ros::Time nowT = ros::Time::now();\n        cmd_pub.publish(cmdMsg);\n        if ((nowT-startT).toSec() > 3){\n            break;\n        }\n        loopRate.sleep();\n    }\n\n    spinner.spin();\n    return 0;\n}\n"
  },
  {
    "path": "src/cmpcc/src/solver_test.cpp",
    "content": "#include \"mpc_solver.h\"\n\nusing namespace Eigen;\nusing namespace std;\n\nint main(int argc, char const *argv[])\n{\n    using namespace ft;\n    ft::Map map;\n    MpcSolver solver;\n    \n    SparseMatrix<double> state(Model::numState,1);\n    double theta = 0.01;\n    Eigen::Vector3d pos, vel;\n    map.getGlobalCommand(theta, pos, vel);\n    cout << \"theta_max: \" << map.thetaMax << endl;\n    cout << \"pos_test: \" << pos.transpose() << endl;\n    cout << \"vel_tess: \" << vel.transpose() << endl;\n    state.coeffRef(0,0) = pos(0);\n    state.coeffRef(Model::numOrder,0) = pos(1);\n    state.coeffRef(2*Model::numOrder,0) = pos(2);\n    state.coeffRef(Model::numState-Model::numOrder,0) = theta;\n    int solveStatus = solver.solveMpcQp(state);\n    cout << solveStatus <<endl;\n    cout << solver.inputPredict << endl;\n}\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/LICENSE",
    "content": "BSD 3-Clause License\n\nCopyright (c) 2018, sikang\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n  list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n  this list of conditions and the following disclaimer in the documentation\n  and/or other materials provided with the distribution.\n\n* Neither the name of the copyright holder nor the names of its\n  contributors may be used to endorse or promote products derived from\n  this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/catkin_simple/CMakeLists.txt",
    "content": "cmake_minimum_required(VERSION 2.8.3)\nproject(catkin_simple)\n\nset(CMAKE_CXX_FLAGS_RELEASE \"-O3 -w -Wno-deprecated -g -fPIC\")\n\nfind_package(catkin REQUIRED)\n\ncatkin_package(\n  CATKIN_DEPENDS catkin\n  CFG_EXTRAS catkin_simple-extras.cmake\n)\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/catkin_simple/README.md",
    "content": "# catkin simple\n\nThis `catkin` package is designed to make the `CMakeLists.txt` of other `catkin` packages simpler.\n\n## CMakeLists.txt Example\n\nHere is an example of a package `foo` which depends on other catkin packages:\n\n```cmake\ncmake_minimum_required(VERSION 2.8.3)\nproject(foo)\n\nfind_package(catkin_simple REQUIRED)\n\ncatkin_simple()\n\ncs_add_library(my_lib src/my_lib.cpp)\n\ncs_add_executable(my_exec src/main.cpp)\ntarget_link_libraries(my_exec my_lib)\n\ncs_install()\n\ncs_install_scripts(scripts/my_script.py)\n\ncs_export()\n```\n\nLets break this down, line by line.\n\nFirst is the standard CMake header:\n\n```cmake\ncmake_minimum_required(VERSION 2.8.3)\nproject(foo)\n```\n\nWhich defines the minimum CMake version and the name of this project.\n\nNext comes the `find_package` of `catkin_simple`:\n\n```cmake\nfind_package(catkin_simple REQUIRED)\n```\n\nThis is just like `find_package` for any other `catkin` package. This command is required.\n\n### catkin_simple()\n\nThen you invoke `catkin_simple`:\n\n```cmake\ncatkin_simple()\n```\n\nThis macro call gathers your `build_depend`'s from the `package.xml` of your package, then does a `find_package(...)` on each of them. \n\nBy default, `find_package(...)` is called with the `QUIET` option and without the `REQUIRED` option. That way, if any of the build_depend's are not `catkin` packages then they are simply ignored. This means that if you depend on a `caktin` package which is not on your system, `catkin_simple` will not warn you about this, because there is no way to know if it is a missing `catkin` package or a system dependency. If this is not the desired behaviour, calling `catkin_simple(ALL_DEPS_REQUIRED)` will call `find_package(...)` on each dependency *with* the `REQUIRED` option.\n\nPackages which are successfully found and identified to be `catkin` packages are added to a list of \"catkin build dependencies\" for your package. This list of build dependencies is passed to `find_package(catkin REQUIRED COMPONENTS ...)`. This command is required.\n\nNext, this macro adds the local `include` folder and any `catkin` include directories to the include path with CMake's `include_directories(...)` macro, but the local `include` folder is only added if it exists.\n\nFinally, this macro will discover and build any ROS messages, services, and actions which reside in the `msg`, `srv`, action `action` folders, respectively. The automatic discovery and building of messages/services is only done if your package `build_depend`'s on `message_generation`, and message generation will complain if your package does not run_depend on `message_runtime`.\n\nThis macro discovers and builds [dynamic_reconfigure](http://wiki.ros.org/dynamic_reconfigure) config files from the `cfg` folder. The automatic discovery only works if your package `build_depend`'s on `dynamic_reconfigure`, and dynamic reconfigure will complain if your package does not run_depend on `dynamic_reconfigure`.\n\n### cs_add_library()\n\nNext we create a library:\n\n```cmake\ncs_add_library(my_lib src/my_lib.cpp)\n```\n\nThis call does a few things, first it calls directly through to the normal CMake macro `add_library`, then it calls `target_link_libraries(my_lib ${catkin_LIBRARIES})` to link your new library against any catkin libraries you have build depended on in your package.xml. Finally it does some bookkeeping so that your library target can be implicitly used later.\n\n### cs_add_executable()\n\nNext we add a new executable:\n\n```cmake\ncs_add_executable(my_exec src/main.cpp)\ntarget_link_libraries(my_exec my_lib)\n```\n\nThis works just like `cs_add_library`, but it calls CMake's `add_executable(...)` instead.\n\nNotice that here we have to explicitly call `target_link_libraries` for linking against our library, this is because there is no way to enforce order of target creation. The executable is still automatically linked against the catkin libraries.\n\n### cs_install()\n\nNext we install everything:\n\n```cmake\ncs_install()\n\ncs_install_scripts(scripts/my_script.py)\n```\n\nThe first macro call creates an installation rule for any libraries and executables you created with `cs_` prefixed commands. That call can also take zero to many additional targets you wish to install which were created without the `cs_` prefixed commands. This command is optional.\n\nThe second macro call creates an installation rule for the given scripts, installing them to `${prefix}/lib/${pkg_name}/`. This command is optional.\n\n### cs_export()\n\nFinally, we export everything:\n\n```cmake\ncs_export()\n```\n\nThis command calls `catkin_package(...)` under the hood, extending that call with any libraries created and catkin_depends found automatically with `catkin_simple`. You can also pass in your own arguments to `catkin_package(...)` through this command. This command is required.\n\n## Known Limitations\n\nThere are several known assumptions and incorrect behaviors in `catkin_simple` which are a result of the trade-off of correctness for convenience.\n\n* There is no warning when a catkin package is not found during `find_package`.\n* There is over linking, as all libraries of all dependencies are linked against all targets indiscriminately.\n* Assumes that the `include` folder is meant to be in the include path.\n* If new .msg or .srv files are added, they will not be detected until you force CMake to run again\n* All targets have a target dependency on any downstream message generation, which results in sub-optimal parallelization of targets, as there are unnecessary dependencies created.\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/catkin_simple/cmake/catkin_simple-extras.cmake.em",
    "content": "# Generated from: catkin_simple/cmake/catkin_simple-extras.cmake.em\n\nif(_CATKIN_SIMPLE_EXTRAS_INCLUDED_)\n  return()\nendif()\nset(_CATKIN_SIMPLE_EXTRAS_INCLUDED_ TRUE)\n\ninclude(CMakeParseArguments)\n\n@[if DEVELSPACE]@\n# cmake dir in develspace\nset(catkin_simple_CMAKE_DIR \"@(CMAKE_CURRENT_SOURCE_DIR)/cmake\")\n@[else]@\n# cmake dir in installspace\nset(catkin_simple_CMAKE_DIR \"@(PKG_CMAKE_DIR)\")\n@[end if]@\n\nmacro(catkin_simple)\n  # Arguments\n  # ALL_DEPS_REQUIRED -- Add the \"REQUIRED\" flag when calling\n  #                      FIND_PACKAGE() for each dependency\n  cmake_parse_arguments(cs_args \"ALL_DEPS_REQUIRED\" \"\" \"\" ${ARGN})\n\n  if(TARGET ${PROJECT_NAME}_package)\n    message(WARNING \"Could not create target '${${PROJECT_NAME}_package}' for project ${PROJECT_NAME}, as it already exists.\")\n  endif()\n  add_custom_target(${PROJECT_NAME}_package)\n  set(${PROJECT_NAME}_TARGETS)\n  set(${PROJECT_NAME}_LIBRARIES)\n\n  find_package(catkin REQUIRED)\n  # call catkin_package_xml() if it has not been called before\n  if(NOT _CATKIN_CURRENT_PACKAGE)\n    catkin_package_xml()\n  endif()\n\n  set(${PROJECT_NAME}_CATKIN_BUILD_DEPENDS)\n  set(${PROJECT_NAME}_CATKIN_BUILD_DEPENDS_EXPORTED_TARGETS)\n  foreach(dep ${${PROJECT_NAME}_BUILD_DEPENDS})\n    # If this flag is defined, add the \"REQUIRED\" flag\n    # to all FIND_PACKAGE calls\n    if(cs_args_ALL_DEPS_REQUIRED)\n      find_package(${dep} REQUIRED)\n    else()\n      find_package(${dep} QUIET)\n    endif()\n\n    if(${dep}_FOUND_CATKIN_PROJECT)\n      list(APPEND ${PROJECT_NAME}_CATKIN_BUILD_DEPENDS ${dep})\n      list(APPEND ${PROJECT_NAME}_CATKIN_BUILD_DEPENDS_EXPORTED_TARGETS ${${dep}_EXPORTED_TARGETS})\n    endif()\n  endforeach()\n\n  # Let find_package(catkin ...) do the heavy lifting\n  find_package(catkin REQUIRED COMPONENTS ${${PROJECT_NAME}_CATKIN_BUILD_DEPENDS})\n\n  # add include directory if available\n  set(${PROJECT_NAME}_LOCAL_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/include)\n  if(NOT IS_DIRECTORY ${${PROJECT_NAME}_LOCAL_INCLUDE_DIR})\n    set(${PROJECT_NAME}_LOCAL_INCLUDE_DIR)\n  endif()\n  include_directories(${${PROJECT_NAME}_LOCAL_INCLUDE_DIR} ${catkin_INCLUDE_DIRS})\n\n  # perform action/msg/srv generation if necessary\n  if(message_generation_FOUND_CATKIN_PROJECT)\n    set(${PROJECT_NAME}_DO_MESSAGE_GENERATION FALSE)\n    # add action files if available\n    set(${PROJECT_NAME}_LOCAL_ACTION_DIR ${CMAKE_CURRENT_SOURCE_DIR}/action)\n    if(NOT IS_DIRECTORY ${${PROJECT_NAME}_LOCAL_ACTION_DIR})\n      set(${PROJECT_NAME}_LOCAL_ACTION_DIR)\n    endif()\n    if(${PROJECT_NAME}_LOCAL_ACTION_DIR)\n      add_action_files(DIRECTORY action)\n      set(${PROJECT_NAME}_DO_MESSAGE_GENERATION TRUE)\n    endif()\n\n    # add message files if available\n    set(${PROJECT_NAME}_LOCAL_MSG_DIR ${CMAKE_CURRENT_SOURCE_DIR}/msg)\n    if(NOT IS_DIRECTORY ${${PROJECT_NAME}_LOCAL_MSG_DIR})\n      set(${PROJECT_NAME}_LOCAL_MSG_DIR)\n    endif()\n    if(${PROJECT_NAME}_LOCAL_MSG_DIR)\n      add_message_files(DIRECTORY msg)\n      set(${PROJECT_NAME}_DO_MESSAGE_GENERATION TRUE)\n    endif()\n\n    # add service files if available\n    set(${PROJECT_NAME}_LOCAL_SRV_DIR ${CMAKE_CURRENT_SOURCE_DIR}/srv)\n    if(NOT IS_DIRECTORY ${${PROJECT_NAME}_LOCAL_SRV_DIR})\n      set(${PROJECT_NAME}_LOCAL_SRV_DIR)\n    endif()\n    if(${PROJECT_NAME}_LOCAL_SRV_DIR)\n      add_service_files(DIRECTORY srv)\n      set(${PROJECT_NAME}_DO_MESSAGE_GENERATION TRUE)\n    endif()\n\n    # generate messages if necessary\n    if(${PROJECT_NAME}_DO_MESSAGE_GENERATION)\n      # identify all build dependencies which contain messages\n      set(${PROJECT_NAME}_MSG_PACKAGES)\n      foreach(dep ${${PROJECT_NAME}_CATKIN_BUILD_DEPENDS})\n        set(${PROJECT_NAME}_MSG_PACKAGE_FILE ${${dep}_DIR}/${dep}-msg-paths.cmake)\n        if(EXISTS ${${PROJECT_NAME}_MSG_PACKAGE_FILE})\n          list(APPEND ${PROJECT_NAME}_MSG_PACKAGES ${dep})\n        endif()\n      endforeach()\n      generate_messages(DEPENDENCIES ${${PROJECT_NAME}_MSG_PACKAGES})\n      # add additional exported targets coming from generate_messages()\n      list(INSERT ${PROJECT_NAME}_CATKIN_BUILD_DEPENDS_EXPORTED_TARGETS 0 ${${PROJECT_NAME}_EXPORTED_TARGETS})\n    endif()\n  endif()\n  \n  # generate dynamic reconfigure files\n  if(dynamic_reconfigure_FOUND_CATKIN_PROJECT)\n    set(${PROJECT_NAME}_LOCAL_CFG_DIR ${CMAKE_CURRENT_SOURCE_DIR}/cfg)\n    if(IS_DIRECTORY ${${PROJECT_NAME}_LOCAL_CFG_DIR})\n      # create a list containing all the cfg files\n      file(GLOB ${PROJECT_NAME}_LOCAL_CFG_FILES RELATIVE \"${CMAKE_CURRENT_SOURCE_DIR}\" \"${${PROJECT_NAME}_LOCAL_CFG_DIR}/*.cfg\")\n      if(${PROJECT_NAME}_LOCAL_CFG_FILES)\n        generate_dynamic_reconfigure_options(${${PROJECT_NAME}_LOCAL_CFG_FILES})\n        # add build dep on gencfg\n        list(APPEND ${PROJECT_NAME}_CATKIN_BUILD_DEPENDS_EXPORTED_TARGETS ${PROJECT_NAME}_gencfg)\n      endif()\n    endif()\n  endif()\nendmacro()\n\nmacro(cs_add_targets_to_package)\n  add_dependencies(${PROJECT_NAME}_package ${ARGN})\n  list(APPEND ${PROJECT_NAME}_TARGETS ${ARGN})\nendmacro()\n\nmacro(cs_add_executable _target)\n  if(${_target} STREQUAL ${PROJECT_NAME}_package)\n    message(WARNING \"Could not create executable with name '${_target}' as '${PROJECT_NAME}_package' is reserved for the top level target name for this project.\")\n  endif()\n  cmake_parse_arguments(cs_add_executable_args \"NO_AUTO_LINK;NO_AUTO_DEP\" \"\" \"\" ${ARGN})\n  add_executable(${_target} ${cs_add_executable_args_UNPARSED_ARGUMENTS})\n  if(NOT cs_add_executable_args_NO_AUTO_LINK)\n    target_link_libraries(${_target} ${catkin_LIBRARIES})\n  endif()\n  if(NOT cs_add_executable_args_NO_AUTO_DEP)\n    if(NOT \"${${PROJECT_NAME}_CATKIN_BUILD_DEPENDS_EXPORTED_TARGETS}\" STREQUAL \"\")\n      add_dependencies(${_target} ${${PROJECT_NAME}_CATKIN_BUILD_DEPENDS_EXPORTED_TARGETS})\n    endif()\n  endif()\n  cs_add_targets_to_package(${_target})\nendmacro()\n\nmacro(cs_add_library _target)\n  if(${_target} STREQUAL ${PROJECT_NAME}_package)\n    message(WARNING \"Could not create library with name '${_target}' as '${PROJECT_NAME}_package' is reserved for the top level target name for this project.\")\n  endif()\n  cmake_parse_arguments(cs_add_library \"NO_AUTO_LINK;NO_AUTO_DEP;NO_AUTO_EXPORT\" \"\" \"\" ${ARGN})\n  add_library(${_target} ${cs_add_library_UNPARSED_ARGUMENTS})\n  if(NOT cs_add_library_NO_AUTO_LINK)\n    target_link_libraries(${_target} ${catkin_LIBRARIES})\n  endif()\n  if(NOT cs_add_library_NO_AUTO_DEP)\n    if(NOT \"${${PROJECT_NAME}_CATKIN_BUILD_DEPENDS_EXPORTED_TARGETS}\" STREQUAL \"\")\n      add_dependencies(${_target} ${${PROJECT_NAME}_CATKIN_BUILD_DEPENDS_EXPORTED_TARGETS})\n    endif()\n  endif()\n  if(NOT cs_add_library_NO_AUTO_EXPORT)\n    list(APPEND ${PROJECT_NAME}_LIBRARIES ${_target})\n  endif()\n  cs_add_targets_to_package(${_target})\nendmacro()\n\nmacro(cs_install)\n  # Install targets (exec's and lib's)\n  foreach(_target ${${PROJECT_NAME}_TARGETS})\n    get_target_property(${_target}_type ${_target} TYPE)\n    message(STATUS \"Marking ${${_target}_type} \\\"${_target}\\\" of package \\\"${PROJECT_NAME}\\\" for installation\")\n  endforeach()\n  install(TARGETS ${${PROJECT_NAME}_TARGETS} ${ARGN}\n    ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}\n    LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}\n    RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}\n  )\n  if(EXISTS ${${PROJECT_NAME}_LOCAL_INCLUDE_DIR})\n    # Install include directory\n    message(STATUS \"Marking HEADER FILES in \\\"include\\\" folder of package \\\"${PROJECT_NAME}\\\" for installation\")\n    install(DIRECTORY ${${PROJECT_NAME}_LOCAL_INCLUDE_DIR}/\n      DESTINATION ${CATKIN_GLOBAL_INCLUDE_DESTINATION}\n      FILES_MATCHING PATTERN \"*.h\" PATTERN \"*.hpp\"\n      PATTERN \".svn\" EXCLUDE\n    )\n  endif()\n  # Install shared content located in commonly used folders\n  set(_shared_content_folders launch rviz urdf meshes maps worlds Media param)\n  foreach(_folder ${_shared_content_folders})\n    if(IS_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/${_folder})\n      message(STATUS \"Marking SHARED CONTENT FOLDER \\\"${_folder}\\\" of package \\\"${PROJECT_NAME}\\\" for installation\")\n      install(DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/${_folder}/\n        DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}/${_folder}\n      )\n    endif()\n  endforeach()\nendmacro()\n\nmacro(cs_install_scripts)\n  install(PROGRAMS ${ARGN} DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION})\nendmacro()\n\nmacro(cs_export)\n  cmake_parse_arguments(CS_PROJECT\n    \"\" \"\" \"INCLUDE_DIRS;LIBRARIES;CATKIN_DEPENDS;DEPENDS;CFG_EXTRAS\"\n    ${ARGN})\n\n  set(${PROJECT_NAME}_CATKIN_RUN_DEPENDS)\n  foreach(dep ${${PROJECT_NAME}_RUN_DEPENDS})\n    find_package(${dep} QUIET)\n    if(${dep}_FOUND_CATKIN_PROJECT)\n      list(APPEND ${PROJECT_NAME}_CATKIN_RUN_DEPENDS ${dep})\n    endif()\n  endforeach()\n\n  catkin_package(\n    INCLUDE_DIRS ${${PROJECT_NAME}_LOCAL_INCLUDE_DIR} ${CS_PROJECT_INCLUDE_DIRS}\n    LIBRARIES ${${PROJECT_NAME}_LIBRARIES} ${CS_PROJECT_LIBRARIES}\n    CATKIN_DEPENDS ${${PROJECT_NAME}_CATKIN_RUN_DEPENDS} ${CS_PROJECT_CATKIN_DEPENDS}\n    DEPENDS ${CS_PROJECT_DEPENDS}\n    CFG_EXTRAS ${CS_PROJECT_CFG_EXTRAS}\n  )\nendmacro()\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/catkin_simple/package.xml",
    "content": "<?xml version=\"1.0\"?>\n<package>\n  <name>catkin_simple</name>\n  <version>0.1.1</version>\n  <description>catkin, simpler</description>\n\n  <maintainer email=\"william@osrfoundation.org\">William Woodall</maintainer>\n  <maintainer email=\"dthomas@osrfoundation.org\">Dirk Thomas</maintainer>\n  <license>BSD</license>\n\n  <author email=\"william@osrfoundation.org\">William Woodall</author>\n  <author email=\"dthomas@osrfoundation.org\">Dirk Thomas</author>\n\n  <buildtool_depend>catkin</buildtool_depend>\n  <run_depend>catkin</run_depend>\n\n</package>"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_msgs/CMakeLists.txt",
    "content": "cmake_minimum_required(VERSION 2.8.3)\nproject(decomp_ros_msgs)\n\nfind_package(catkin REQUIRED COMPONENTS\n  #armadillo\n  roscpp\n  geometry_msgs\n  message_generation\n)\n\nset(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake)\n\ninclude_directories(\n    ${catkin_INCLUDE_DIRS}\n    include\n    )\n\nadd_message_files(\n  FILES\n  Ellipsoid.msg\n  EllipsoidArray.msg\n  Polyhedron.msg\n  PolyhedronArray.msg\n)\n\ngenerate_messages(\n    DEPENDENCIES\n    geometry_msgs\n)\n\ncatkin_package(\n  LIBRARIES encode_msgs decode_msgs\n  CATKIN_DEPENDS message_runtime\n)\n\n#find_package(Eigen3 REQUIRED)\n#include_directories(${EIGEN3_INCLUDE_DIR})\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_msgs/msg/Ellipsoid.msg",
    "content": "float64[3] d\nfloat64[9] E\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_msgs/msg/EllipsoidArray.msg",
    "content": "Header header\nEllipsoid[] ellipsoids\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_msgs/msg/Polyhedron.msg",
    "content": "geometry_msgs/Point[] points\ngeometry_msgs/Point[] normals #norm is an outer vector\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_msgs/msg/PolyhedronArray.msg",
    "content": "Header header\nPolyhedron[] polyhedrons\nint32[] ids"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_msgs/package.xml",
    "content": "<?xml version=\"1.0\"?>\n<package format=\"2\">\n  <name>decomp_ros_msgs</name>\n  <version>0.0.0</version>\n  <description>The decomp_ros_msgs package</description>\n\n  <!-- One maintainer tag required, multiple allowed, one person per tag -->\n  <!-- Example:  -->\n  <!-- <maintainer email=\"jane.doe@example.com\">Jane Doe</maintainer> -->\n  <maintainer email=\"sikang@seas.upenn.edu\">sikang</maintainer>\n\n  <license>TODO</license>\n\n  <buildtool_depend>catkin</buildtool_depend>\n  <build_depend>catkin_simple</build_depend>\n  <depend>geometry_msgs</depend>\n  <depend>message_generation</depend>\n  <depend>message_runtime</depend>\n\n\n  <!-- The export tag contains other, unspecified, tags -->\n  <export>\n    <!-- Other tools can request additional information be placed here -->\n\n  </export>\n</package>\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/CMakeLists.txt",
    "content": "cmake_minimum_required(VERSION 2.8.3)\nproject(decomp_ros_utils)\n\nset(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -std=c++14 -O3 -Wall\")\nfind_package(catkin REQUIRED COMPONENTS rviz roscpp)\nfind_package(catkin_simple REQUIRED)\nfind_package(cmake_modules)\nfind_package(Eigen3 REQUIRED)\n#find_package(decomp_util REQUIRED)\ninclude_directories(${EIGEN3_INCLUDE_DIRS} ) #${DECOMP_UTIL_INCLUDE_DIRS})\n\nadd_definitions(-DQT_NO_KEYWORDS)\n\n## This setting causes Qt's \"MOC\" generation to happen automatically.\n## this does not moc things in include!!!!!!! only in src\nset(CMAKE_AUTOMOC ON)\n\n## We'll use the version that rviz used so they are compatible.\nif(rviz_QT_VERSION VERSION_LESS \"5\")\n  message(STATUS \"Using Qt4 based on the rviz_QT_VERSION: ${rviz_QT_VERSION}\")\n  find_package(Qt4 ${rviz_QT_VERSION} EXACT REQUIRED QtCore QtGui)\n  include(${QT_USE_FILE})\nelse()\n  message(STATUS \"Using Qt5 based on the rviz_QT_VERSION: ${rviz_QT_VERSION}\")\n  find_package(Qt5 ${rviz_QT_VERSION} EXACT REQUIRED Core Widgets)\n  set(QT_LIBRARIES Qt5::Widgets)\nendif()\n\ncatkin_simple()\n\nset(SOURCE_FILES\n  src/bound_visual.cpp\n  src/mesh_visual.cpp\n  src/vector_visual.cpp\n  src/ellipsoid_array_visual.cpp\n  src/ellipsoid_array_display.cpp\n  src/polyhedron_array_display.cpp\n  ${MOC_FILES})\n\ncs_add_library(decomp_rviz_plugins ${SOURCE_FILES})\ntarget_link_libraries(decomp_rviz_plugins ${QT_LIBRARIES} ${catkin_LIBRARIES} ${rviz_DEFAULT_PLUGIN_LIBRARIES})\n\ncs_install()\n\n#cs_export()\n#cs_export(DEPENDS decomp_util)\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/include/decomp_basis/data_type.h",
    "content": "/**\n * @file data_type.h\n * @brief Defines all data types used in this lib\n\n * Mostly alias from Eigen Library.\n */\n\n#include <stdio.h>\n#include <math.h>\n#include <limits>\n#include <vector>\n#include <Eigen/Geometry>\n#include <Eigen/StdVector>\n\n///Set red font in printf funtion\n#ifndef ANSI_COLOR_RED\n#define ANSI_COLOR_RED \"\\x1b[1;31m\"\n#endif\n///Set green font in printf funtion\n#ifndef ANSI_COLOR_GREEN\n#define ANSI_COLOR_GREEN \"\\x1b[1;32m\"\n#endif\n///Set yellow font in printf funtion\n#ifndef ANSI_COLOR_YELLOW\n#define ANSI_COLOR_YELLOW \"\\x1b[1;33m\"\n#endif\n///Set blue font in printf funtion\n#ifndef ANSI_COLOR_BLUE\n#define ANSI_COLOR_BLUE \"\\x1b[1;34m\"\n#endif\n///Set magenta font in printf funtion\n#ifndef ANSI_COLOR_MAGENTA\n#define ANSI_COLOR_MAGENTA \"\\x1b[1;35m\"\n#endif\n///Set cyan font in printf funtion\n#ifndef ANSI_COLOR_CYAN\n#define ANSI_COLOR_CYAN \"\\x1b[1;36m\"\n#endif\n///Reset font color in printf funtion\n#ifndef ANSI_COLOR_RESET\n#define ANSI_COLOR_RESET \"\\x1b[0m\"\n#endif\n\n#ifndef DATA_TYPE_H\n#define DATA_TYPE_H\n/*! \\brief Rename the float type used in lib\n\n    Default is set to be double, but user can change it to float.\n*/\ntypedef double decimal_t;\n\n///Pre-allocated std::vector for Eigen using vec_E\ntemplate <typename T>\nusing vec_E = std::vector<T, Eigen::aligned_allocator<T>>;\n///Eigen 1D float vector\ntemplate <int N>\nusing Vecf = Eigen::Matrix<decimal_t, N, 1>;\n///Eigen 1D int vector\ntemplate <int N>\nusing Veci = Eigen::Matrix<int, N, 1>;\n///MxN Eigen matrix\ntemplate <int M, int N>\nusing Matf = Eigen::Matrix<decimal_t, M, N>;\n///MxN Eigen matrix with M unknown\ntemplate <int N>\nusing MatDNf = Eigen::Matrix<decimal_t, Eigen::Dynamic, N>;\n///Vector of Eigen 1D float vector\ntemplate <int N>\nusing vec_Vecf = vec_E<Vecf<N>>;\n///Vector of Eigen 1D int vector\ntemplate <int N>\nusing vec_Veci = vec_E<Veci<N>>;\n\n///Eigen 1D float vector of size 2\ntypedef Vecf<2> Vec2f;\n///Eigen 1D int vector of size 2\ntypedef Veci<2> Vec2i;\n///Eigen 1D float vector of size 3\ntypedef Vecf<3> Vec3f;\n///Eigen 1D int vector of size 3\ntypedef Veci<3> Vec3i;\n///Eigen 1D float vector of size 4\ntypedef Vecf<4> Vec4f;\n///Column vector in float of size 6\ntypedef Vecf<6> Vec6f;\n\n///Vector of type Vec2f.\ntypedef vec_E<Vec2f> vec_Vec2f;\n///Vector of type Vec2i.\ntypedef vec_E<Vec2i> vec_Vec2i;\n///Vector of type Vec3f.\ntypedef vec_E<Vec3f> vec_Vec3f;\n///Vector of type Vec3i.\ntypedef vec_E<Vec3i> vec_Vec3i;\n\n///2x2 Matrix in float\ntypedef Matf<2, 2> Mat2f;\n///3x3 Matrix in float\ntypedef Matf<3, 3> Mat3f;\n///4x4 Matrix in float\ntypedef Matf<4, 4> Mat4f;\n///6x6 Matrix in float\ntypedef Matf<6, 6> Mat6f;\n\n///Dynamic Nx1 Eigen float vector\ntypedef Vecf<Eigen::Dynamic> VecDf;\n///Nx2 Eigen float matrix\ntypedef MatDNf<2> MatD2f;\n///Nx3 Eigen float matrix\ntypedef MatDNf<3> MatD3f;\n///Dynamic MxN Eigen float matrix\ntypedef Matf<Eigen::Dynamic, Eigen::Dynamic> MatDf;\n\n///Allias of Eigen::Affine2d\ntypedef Eigen::Transform<decimal_t, 2, Eigen::Affine> Aff2f;\n///Allias of Eigen::Affine3d\ntypedef Eigen::Transform<decimal_t, 3, Eigen::Affine> Aff3f;\n#endif\n\n#ifndef EIGEN_QUAT\n#define EIGEN_QUAT\n///Allias of Eigen::Quaterniond\ntypedef Eigen::Quaternion<decimal_t> Quatf;\n#endif\n\n#ifndef EIGEN_EPSILON\n#define EIGEN_EPSILON\n///Compensate for numerical error\nconstexpr decimal_t epsilon_ = 1e-10; // numerical calculation error\n#endif\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/include/decomp_basis/data_utils.h",
    "content": "/**\n * @file data_utils.h\n * @brief Provide a few widely used function for basic type\n */\n#ifndef DATA_UTILS_H\n#define DATA_UTILS_H\n\n#include <decomp_basis/data_type.h>\n\n///Template for transforming a vector\ntemplate <class T, class TF>\nvec_E<T> transform_vec(const vec_E<T> &t, const TF &tf) {\n  vec_E<T> new_t;\n  for (const auto &it : t)\n    new_t.push_back(tf * it);\n  return new_t;\n}\n\n///Template for calculating distance\ntemplate <class T>\ndecimal_t total_distance(const vec_E<T>& vs){\n  decimal_t dist = 0;\n  for(unsigned int i = 1; i < vs.size(); i++)\n    dist += (vs[i] - vs[i-1]).norm();\n\n  return dist;\n}\n\n\n///Transform all entries in a vector using given TF\n#define transform_vec3 transform_vec<Vec3f, Aff3f>\n///Sum up total distance for Vec3f\n#define total_distance3f total_distance<Vec3f>\n///Sum up total distance for Vec3i\n#define total_distance3i total_distance<Vec3i>\n#endif\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/include/decomp_geometry/ellipsoid.h",
    "content": "/**\n * @file ellipsoid.h\n * @brief Ellipsoid class\n */\n\n#ifndef DECOMP_ELLIPSOID_H\n#define DECOMP_ELLIPSOID_H\n\n#include <iostream>\n#include <decomp_basis/data_type.h>\n#include <decomp_geometry/polyhedron.h>\n\ntemplate <int Dim>\nstruct Ellipsoid {\n  Ellipsoid() {}\n  Ellipsoid(const Matf<Dim, Dim>& C, const Vecf<Dim>& d) : C_(C), d_(d) {}\n\n  /// Calculate distance to the center\n  decimal_t dist(const Vecf<Dim>& pt) const {\n    return (C_.inverse() * (pt - d_)).norm();\n  }\n\n  /// Check if the point is inside, non-exclusive\n  bool inside(const Vecf<Dim>& pt) const {\n      return dist(pt) <= 1;\n  }\n\n  /// Calculate points inside ellipsoid, non-exclusive\n  vec_Vecf<Dim> points_inside(const vec_Vecf<Dim> &O) const {\n    vec_Vecf<Dim> new_O;\n    for (const auto &it : O) {\n      if (inside(it))\n        new_O.push_back(it);\n    }\n    return new_O;\n  }\n\n  ///Find the closest point\n  Vecf<Dim> closest_point(const vec_Vecf<Dim> &O) const {\n    Vecf<Dim> pt = Vecf<Dim>::Zero();\n    decimal_t min_dist = std::numeric_limits<decimal_t>::max();\n    for (const auto &it : O) {\n      decimal_t d = dist(it);\n      if (d < min_dist) {\n        min_dist = d;\n        pt = it;\n      }\n    }\n    return pt;\n  }\n\n  ///Find the closest hyperplane from the closest point\n  Hyperplane<Dim> closest_hyperplane(const vec_Vecf<Dim> &O) const {\n    const auto closest_pt = closest_point(O);\n    const auto n = C_.inverse() * C_.inverse().transpose() *\n      (closest_pt - d_);\n    return Hyperplane<Dim>(closest_pt, n.normalized());\n  }\n\n  /// Sample n points along the contour\n  template<int U = Dim>\n    typename std::enable_if<U == 2, vec_Vecf<U>>::type\n    sample(int num) const {\n    vec_Vecf<Dim> pts;\n      decimal_t dyaw = M_PI*2/num;\n      for(decimal_t yaw = 0; yaw < M_PI*2; yaw+=dyaw) {\n        Vecf<Dim> pt;\n        pt << cos(yaw), sin(yaw);\n        pts.push_back(C_ * pt + d_);\n    }\n    return pts;\n  }\n\n  void print() const {\n    std::cout << \"C: \" << C_ << std::endl;\n    std::cout << \"d: \" << d_ << std::endl;\n  }\n\n  /// Get ellipsoid volume\n  decimal_t volume() const {\n    return C_.determinant();\n  }\n\n  /// Get C matrix\n  Matf<Dim, Dim> C() const {\n    return C_;\n  }\n\n  /// Get center\n  Vecf<Dim> d() const {\n    return d_;\n  }\n\n  Matf<Dim, Dim> C_;\n  Vecf<Dim> d_;\n};\n\ntypedef Ellipsoid<2> Ellipsoid2D;\n\ntypedef Ellipsoid<3> Ellipsoid3D;\n\n#endif\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/include/decomp_geometry/geometric_utils.h",
    "content": "/**\n * @file geometric_utils.h\n * @brief basic geometry utils\n */\n#ifndef DECOMP_GEOMETRIC_UTILS_H\n#define DECOMP_GEOMETRIC_UTILS_H\n\n#include <Eigen/Eigenvalues>\n#include <decomp_basis/data_utils.h>\n#include <decomp_geometry/polyhedron.h>\n#include <iostream>\n\n/// Calculate eigen values\ntemplate <int Dim> Vecf<Dim> eigen_value(const Matf<Dim, Dim> &A) {\n  Eigen::SelfAdjointEigenSolver<Matf<Dim, Dim>> es(A);\n  return es.eigenvalues();\n}\n\n/// Calculate rotation matrix from a vector (aligned with x-axis)\ninline Mat2f vec2_to_rotation(const Vec2f &v) {\n  decimal_t yaw = std::atan2(v(1), v(0));\n  Mat2f R;\n  R << cos(yaw), -sin(yaw), sin(yaw), cos(yaw);\n  return R;\n}\n\ninline Mat3f vec3_to_rotation(const Vec3f &v) {\n  // zero roll\n  Vec3f rpy(0, std::atan2(-v(2), v.topRows<2>().norm()),\n            std::atan2(v(1), v(0)));\n  Quatf qx(cos(rpy(0) / 2), sin(rpy(0) / 2), 0, 0);\n  Quatf qy(cos(rpy(1) / 2), 0, sin(rpy(1) / 2), 0);\n  Quatf qz(cos(rpy(2) / 2), 0, 0, sin(rpy(2) / 2));\n  return Mat3f(qz * qy * qx);\n}\n\n/// Sort plannar points in the counter-clockwise order\ninline vec_Vec2f sort_pts(const vec_Vec2f &pts) {\n  /// if empty, dont sort\n  if (pts.empty())\n    return pts;\n  /// calculate center point\n  Vec2f avg = Vec2f::Zero();\n  for (const auto &pt : pts)\n    avg += pt;\n  avg /= pts.size();\n\n  /// sort in body frame\n  vec_E<std::pair<decimal_t, Vec2f>> pts_valued;\n  pts_valued.resize(pts.size());\n  for (unsigned int i = 0; i < pts.size(); i++) {\n    decimal_t theta = atan2(pts[i](1) - avg(1), pts[i](0) - avg(0));\n    pts_valued[i] = std::make_pair(theta, pts[i]);\n  }\n\n  std::sort(\n      pts_valued.begin(), pts_valued.end(),\n      [](const std::pair<decimal_t, Vec2f> &i,\n         const std::pair<decimal_t, Vec2f> &j) { return i.first < j.first; });\n  vec_Vec2f pts_sorted(pts_valued.size());\n  for (size_t i = 0; i < pts_valued.size(); i++)\n    pts_sorted[i] = pts_valued[i].second;\n  return pts_sorted;\n}\n\n/// Find intersection between two lines on the same plane, return false if they\n/// are not intersected\ninline bool line_intersect(const std::pair<Vec2f, Vec2f> &v1,\n                           const std::pair<Vec2f, Vec2f> &v2, Vec2f &pi) {\n  decimal_t a1 = -v1.first(1);\n  decimal_t b1 = v1.first(0);\n  decimal_t c1 = a1 * v1.second(0) + b1 * v1.second(1);\n\n  decimal_t a2 = -v2.first(1);\n  decimal_t b2 = v2.first(0);\n  decimal_t c2 = a2 * v2.second(0) + b2 * v2.second(1);\n\n  decimal_t x = (c1 * b2 - c2 * b1) / (a1 * b2 - a2 * b1);\n  decimal_t y = (c1 * a2 - c2 * a1) / (a2 * b1 - a1 * b2);\n\n  if (std::isnan(x) || std::isnan(y) || std::isinf(x) || std::isinf(y))\n    return false;\n  else {\n    pi << x, y;\n    return true;\n  }\n}\n\n/// Find intersection between multiple lines\ninline vec_Vec2f line_intersects(const vec_E<std::pair<Vec2f, Vec2f>> &lines) {\n  vec_Vec2f pts;\n  for (unsigned int i = 0; i < lines.size(); i++) {\n    for (unsigned int j = i + 1; j < lines.size(); j++) {\n      Vec2f pi;\n      if (line_intersect(lines[i], lines[j], pi)) {\n        pts.push_back(pi);\n      }\n    }\n  }\n  return pts;\n}\n\n/// Find extreme points of Polyhedron2D\ninline vec_Vec2f cal_vertices(const Polyhedron2D &poly) {\n  vec_E<std::pair<Vec2f, Vec2f>> lines;\n  const auto vs = poly.hyperplanes();\n  for (unsigned int i = 0; i < vs.size(); i++) {\n    Vec2f n = vs[i].n_;\n    Vec2f v(-n(1), n(0));\n    v = v.normalized();\n\n    lines.push_back(std::make_pair(v, vs[i].p_));\n    /*\n    std::cout << \"add p: \" << lines.back().second.transpose() <<\n      \" v: \" << lines.back().first.transpose() << std::endl;\n      */\n  }\n\n  auto vts = line_intersects(lines);\n  // for(const auto& it: vts)\n  // std::cout << \"vertice: \" << it.transpose() << std::endl;\n\n  vec_Vec2f vts_inside = poly.points_inside(vts);\n  vts_inside = sort_pts(vts_inside);\n\n  return vts_inside;\n}\n\n/// Find extreme points of Polyhedron3D\ninline vec_E<vec_Vec3f> cal_vertices(const Polyhedron3D &poly) {\n  vec_E<vec_Vec3f> bds;\n  const auto vts = poly.hyperplanes();\n  //**** for each plane, find lines on it\n  for (unsigned int i = 0; i < vts.size(); i++) {\n    const Vec3f t = vts[i].p_;\n    const Vec3f n = vts[i].n_;\n    const Quatf q = Quatf::FromTwoVectors(Vec3f(0, 0, 1), n);\n    const Mat3f R(q); // body to world\n    vec_E<std::pair<Vec2f, Vec2f>> lines;\n    for (unsigned int j = 0; j < vts.size(); j++) {\n      if (j == i)\n        continue;\n      Vec3f nw = vts[j].n_;\n      Vec3f nb = R.transpose() * nw;\n      decimal_t bb = vts[j].p_.dot(nw) - nw.dot(t);\n      Vec2f v = Vec3f(0, 0, 1).cross(nb).topRows<2>(); // line direction\n      Vec2f p;                                         // point on the line\n      if (nb(1) != 0)\n        p << 0, bb / nb(1);\n      else if (nb(0) != 0)\n        p << bb / nb(0), 0;\n      else\n        continue;\n      lines.push_back(std::make_pair(v, p));\n    }\n\n    //**** find all intersect points\n    vec_Vec2f pts = line_intersects(lines);\n    //**** filter out points inside polytope\n    vec_Vec2f pts_inside;\n    for (const auto &it : pts) {\n      Vec3f p = R * Vec3f(it(0), it(1), 0) + t; // convert to world frame\n      if (poly.inside(p))\n        pts_inside.push_back(it);\n    }\n\n    if (pts_inside.size() > 2) {\n      //**** sort in plane frame\n      pts_inside = sort_pts(pts_inside);\n\n      //**** transform to world frame\n      vec_Vec3f points_valid;\n      for (auto &it : pts_inside)\n        points_valid.push_back(R * Vec3f(it(0), it(1), 0) + t);\n\n      //**** insert resulting polygon\n      bds.push_back(points_valid);\n    }\n  }\n  return bds;\n}\n\n/// Get the convex hull of a 2D points array, use wrapping method\ninline vec_Vec2f cal_convex_hull(const vec_Vec2f &pts) {\n  /// find left most point\n  Vec2f p0;\n  decimal_t min_x = std::numeric_limits<decimal_t>::infinity();\n  for (const auto &it : pts) {\n    if (min_x > it(0) || (min_x == it(0) && it(1) < p0(1))) {\n      min_x = it(0);\n      p0 = it;\n    }\n  }\n\n  vec_Vec2f vs;\n  vs.push_back(p0);\n\n  while (vs.back() != p0 || vs.size() == 1) {\n    const auto ref_pt = vs.back();\n    Vec2f end_pt = p0;\n    for (size_t i = 0; i < pts.size(); i++) {\n      if (pts[i] == ref_pt)\n        continue;\n      Vec2f dir = (pts[i] - ref_pt).normalized();\n      Hyperplane2D hp(ref_pt, Vec2f(-dir(1), dir(0)));\n      bool most_left_hp = true;\n      for (size_t j = 0; j < pts.size(); j++) {\n        if (hp.signed_dist(pts[j]) > 0 && pts[j] != pts[i] &&\n            pts[j] != ref_pt) {\n          // if(hp.signed_dist(pts[j]) > 0) {\n          most_left_hp = false;\n          break;\n        }\n      }\n\n      if (most_left_hp) {\n        end_pt = pts[i];\n        break;\n      }\n    }\n    // std::cout << \"add: \" << end_pt.transpose() << std::endl;\n    vs.push_back(end_pt);\n  }\n\n  return vs;\n}\n\ninline Polyhedron2D get_convex_hull(const vec_Vec2f &pts) {\n  Polyhedron2D poly;\n  Vec2f prev_dir(-1, -1);\n  for (size_t i = 0; i < pts.size() - 1; i++) {\n    size_t j = i + 1;\n    Vec2f dir = (pts[j] - pts[i]).normalized();\n    if (dir != prev_dir) {\n      poly.add(Hyperplane2D((pts[i] + pts[j]) / 2, Vec2f(-dir(1), dir(0))));\n      prev_dir = dir;\n    }\n  }\n\n  return poly;\n}\n\n/// Minkowski sum, add B to A with center Bc\ninline Polyhedron2D minkowski_sum(const Polyhedron2D &A, const Polyhedron2D &B,\n                                  const Vec2f &Bc) {\n  const auto A_vertices = cal_vertices(A);\n  const auto B_vertices = cal_vertices(B);\n\n  vec_Vec2f C_vertices;\n  for (const auto &it : A_vertices) {\n    for (const auto &itt : B_vertices)\n      C_vertices.push_back(it + itt - Bc);\n  }\n\n  return get_convex_hull(cal_convex_hull(C_vertices));\n}\n\n#endif\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/include/decomp_geometry/polyhedron.h",
    "content": "/**\n * @file polygon.h\n * @brief Polygon class\n */\n\n#ifndef DECOMP_POLYGON_H\n#define DECOMP_POLYGON_H\n\n#include <decomp_basis/data_type.h>\n\n///Hyperplane class\ntemplate <int Dim>\nstruct Hyperplane {\n  Hyperplane() {}\n  Hyperplane(const Vecf<Dim>& p, const Vecf<Dim>& n) : p_(p), n_(n) {}\n\n  /// Calculate the signed distance from point\n  decimal_t signed_dist(const Vecf<Dim>& pt) const {\n    return n_.dot(pt - p_);\n  }\n\n  /// Calculate the distance from point\n  decimal_t dist(const Vecf<Dim>& pt) const {\n    return std::abs(signed_dist(pt));\n  }\n\n  /// Point on the plane\n  Vecf<Dim> p_;\n  /// Normal of the plane, directional\n  Vecf<Dim> n_;\n};\n\n///Hyperplane2D: first is the point on the hyperplane, second is the normal\ntypedef Hyperplane<2> Hyperplane2D;\n///Hyperplane3D: first is the point on the hyperplane, second is the normal\ntypedef Hyperplane<3> Hyperplane3D;\n\n\n///Polyhedron class\ntemplate <int Dim>\nstruct Polyhedron {\n  ///Null constructor\n  Polyhedron() {}\n  ///Construct from Hyperplane array\n  Polyhedron(const vec_E<Hyperplane<Dim>>& vs) : vs_(vs) {}\n\n\n  ///Append Hyperplane\n  void add(const Hyperplane<Dim>& v) {\n    vs_.push_back(v);\n  }\n\n  /// Check if the point is inside polyhedron, non-exclusive\n  bool inside(const Vecf<Dim>& pt) const {\n    for (const auto& v : vs_) {\n      if (v.signed_dist(pt) > epsilon_) {\n        //printf(\"rejected pt: (%f, %f), d: %f\\n\",pt(0), pt(1), v.signed_dist(pt));\n        return false;\n      }\n    }\n    return true;\n  }\n\n  /// Calculate points inside polyhedron, non-exclusive\n  vec_Vecf<Dim> points_inside(const vec_Vecf<Dim> &O) const {\n    vec_Vecf<Dim> new_O;\n    for (const auto &it : O) {\n      if (inside(it))\n        new_O.push_back(it);\n    }\n    return new_O;\n  }\n\n  /// Calculate normals, used for visualization\n  vec_E<std::pair<Vecf<Dim>, Vecf<Dim>>> cal_normals() const {\n    vec_E<std::pair<Vecf<Dim>, Vecf<Dim>>> ns(vs_.size());\n    for (size_t i = 0; i < vs_.size(); i++)\n      ns[i] = std::make_pair(vs_[i].p_, vs_[i].n_); // fist is point, second is normal\n    return ns;\n  }\n\n  /// Get the hyperplane array\n  vec_E<Hyperplane<Dim>> hyperplanes() const {\n    return vs_;\n  }\n\n  /// Hyperplane array\n  vec_E<Hyperplane<Dim>> vs_; // normal must go outside\n\n};\n\n///Polyhedron2D, consists of 2D hyperplane\ntypedef Polyhedron<2> Polyhedron2D;\n///Polyhedron3D, consists of 3D hyperplane\ntypedef Polyhedron<3> Polyhedron3D;\n\n///[A, b] for \\f$Ax < b\\f$\ntemplate <int Dim>\nstruct LinearConstraint {\n  ///Null constructor\n  LinearConstraint() {}\n  /// Construct from \\f$A, b\\f$ directly, s.t \\f$Ax < b\\f$\n  LinearConstraint(const MatDNf<Dim>& A, const VecDf& b) : A_(A), b_(b) {}\n  /**\n   * @brief Construct from a inside point and hyperplane array\n   * @param p0 point that is inside\n   * @param vs hyperplane array, normal should go outside\n   */\n\tLinearConstraint(const Vecf<Dim> p0, const vec_E<Hyperplane<Dim>>& vs) {\n\t\tconst unsigned int size = vs.size();\n\t\tMatDNf<Dim> A(size, Dim);\n\t\tVecDf b(size);\n\n\t\tfor (unsigned int i = 0; i < size; i++) {\n\t\t\tauto n = vs[i].n_;\n\t\t\tdecimal_t c = vs[i].p_.dot(n);\n\t\t\tif (n.dot(p0) - c > 0) {\n\t\t\t\tn = -n;\n\t\t\t\tc = -c;\n\t\t\t}\n\t\t\tA.row(i) = n;\n\t\t\tb(i) = c;\n\t\t}\n\n\t\tA_ = A;\n\t\tb_ = b;\n\t}\n\n  /// Check if the point is inside polyhedron using linear constraint\n  bool inside(const Vecf<Dim> &pt) {\n    VecDf d = A_ * pt - b_;\n    for (unsigned int i = 0; i < d.rows(); i++) {\n      if (d(i) > 0)\n        return false;\n    }\n    return true;\n  }\n\n  /// Get \\f$A\\f$ matrix\n  MatDNf<Dim> A() const { return A_; }\n\n  /// Get \\f$b\\f$ matrix\n  VecDf b() const { return b_; }\n\n  MatDNf<Dim> A_;\n  VecDf b_;\n};\n\n///LinearConstraint 2D\ntypedef LinearConstraint<2> LinearConstraint2D;\n///LinearConstraint 3D\ntypedef LinearConstraint<3> LinearConstraint3D;\n\n#endif\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/include/decomp_ros_utils/data_ros_utils.h",
    "content": "#ifndef DECOMP_ROS_UTILS_H\n#define DECOMP_ROS_UTILS_H\n\n#include <decomp_geometry/ellipsoid.h>\n#include <decomp_geometry/polyhedron.h>\n#include <sensor_msgs/PointCloud.h>\n#include <decomp_ros_msgs/PolyhedronArray.h>\n#include <decomp_ros_msgs/EllipsoidArray.h>\n#include <nav_msgs/Path.h>\n\nnamespace DecompROS {\n\ntemplate <int Dim> nav_msgs::Path vec_to_path(const vec_Vecf<Dim> &vs) {\n  nav_msgs::Path path;\n  for (const auto& it : vs) {\n    geometry_msgs::PoseStamped pose;\n    pose.pose.position.x = it(0);\n    pose.pose.position.y = it(1);\n    pose.pose.position.z = Dim == 2 ? 0 : it(2);\n    pose.pose.orientation.w = 1.0;\n    pose.pose.orientation.x = 0.0;\n    pose.pose.orientation.y = 0.0;\n    pose.pose.orientation.z = 0.0;\n\n    path.poses.push_back(pose);\n  }\n\n  return path;\n}\n\ninline sensor_msgs::PointCloud vec_to_cloud(const vec_Vec3f &pts) {\n  sensor_msgs::PointCloud cloud;\n  cloud.points.resize(pts.size());\n\n  for (unsigned int i = 0; i < pts.size(); i++) {\n    cloud.points[i].x = pts[i](0);\n    cloud.points[i].y = pts[i](1);\n    cloud.points[i].z = pts[i](2);\n  }\n  return cloud;\n}\n\ninline vec_Vec3f cloud_to_vec(const sensor_msgs::PointCloud &cloud) {\n  vec_Vec3f pts;\n  pts.resize(cloud.points.size());\n  for (unsigned int i = 0; i < cloud.points.size(); i++) {\n    pts[i](0) = cloud.points[i].x;\n    pts[i](1) = cloud.points[i].y;\n    pts[i](2) = cloud.points[i].z;\n  }\n\n  return pts;\n}\n\ninline Polyhedron3D ros_to_polyhedron(const decomp_ros_msgs::Polyhedron& msg){\n  Polyhedron3D poly;\n  for(unsigned int i = 0; i < msg.points.size(); i++){\n    Vec3f pt(msg.points[i].x,\n             msg.points[i].y,\n             msg.points[i].z);\n    Vec3f n(msg.normals[i].x,\n            msg.normals[i].y,\n            msg.normals[i].z);\n    poly.add(Hyperplane3D(pt, n));\n  }\n  return poly;\n}\n\ninline vec_E<Polyhedron3D> ros_to_polyhedron_array(const decomp_ros_msgs::PolyhedronArray& msg) {\n  vec_E<Polyhedron3D> polys(msg.polyhedrons.size());\n  \n  for(size_t i = 0; i < msg.polyhedrons.size(); i++)\n    polys[i] = ros_to_polyhedron(msg.polyhedrons[i]);\n  \n  return polys;\n}\n\ninline decomp_ros_msgs::Polyhedron polyhedron_to_ros(const Polyhedron2D& poly){\n  decomp_ros_msgs::Polyhedron msg;\n  for (const auto &p : poly.hyperplanes()) {\n    geometry_msgs::Point pt, n;\n    pt.x = p.p_(0);\n    pt.y = p.p_(1);\n    pt.z = 0;\n    n.x = p.n_(0);\n    n.y = p.n_(1);\n    n.z = 0;\n    msg.points.push_back(pt);\n    msg.normals.push_back(n);\n  }\n\n  geometry_msgs::Point pt1, n1;\n  pt1.x = 0, pt1.y = 0, pt1.z = 0.01;\n  n1.x = 0, n1.y = 0, n1.z = 1;\n  msg.points.push_back(pt1);\n  msg.normals.push_back(n1);\n\n  geometry_msgs::Point pt2, n2;\n  pt2.x = 0, pt2.y = 0, pt2.z = -0.01;\n  n2.x = 0, n2.y = 0, n2.z = -1;\n  msg.points.push_back(pt2);\n  msg.normals.push_back(n2);\n\n  return msg;\n}\n\ninline decomp_ros_msgs::Polyhedron polyhedron_to_ros(const Polyhedron3D& poly){\n  decomp_ros_msgs::Polyhedron msg;\n  for (const auto &p : poly.hyperplanes()) {\n    geometry_msgs::Point pt, n;\n    pt.x = p.p_(0);\n    pt.y = p.p_(1);\n    pt.z = p.p_(2);\n    n.x = p.n_(0);\n    n.y = p.n_(1);\n    n.z = p.n_(2);\n    msg.points.push_back(pt);\n    msg.normals.push_back(n);\n  }\n\n  return msg;\n}\n\n\ntemplate <int Dim>\ndecomp_ros_msgs::PolyhedronArray polyhedron_array_to_ros(const vec_E<Polyhedron<Dim>>& vs){\n  decomp_ros_msgs::PolyhedronArray msg;\n  for (const auto &v : vs)\n    msg.polyhedrons.push_back(polyhedron_to_ros(v));\n  return msg;\n}\n\ntemplate <int Dim>\ndecomp_ros_msgs::EllipsoidArray ellipsoid_array_to_ros(const vec_E<Ellipsoid<Dim>>& Es) {\n  decomp_ros_msgs::EllipsoidArray ellipsoids;\n  for (unsigned int i = 0; i < Es.size(); i++) {\n    decomp_ros_msgs::Ellipsoid ellipsoid;\n    auto d = Es[i].d();\n    ellipsoid.d[0] = d(0);\n    ellipsoid.d[1] = d(1);\n    ellipsoid.d[2] = Dim == 2 ? 0:d(2);\n\n    auto C = Es[i].C();\n    for (int x = 0; x < 3; x++) {\n      for (int y = 0; y < 3; y++) {\n        if(x < Dim && y < Dim)\n          ellipsoid.E[3 * x + y] = C(x, y);\n        else\n          ellipsoid.E[3 * x + y] = 0;\n      }\n    }\n    ellipsoids.ellipsoids.push_back(ellipsoid);\n  }\n\n  return ellipsoids;\n}\n\n}\n\n#endif\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/include/decomp_util/decomp_base.h",
    "content": "/**\n * @file decomp_base.h\n * @brief Decomp Base Class\n */\n#ifndef DECOMP_BASE_H\n#define DECOMP_BASE_H\n\n#include <decomp_geometry/ellipsoid.h>\n#include <decomp_geometry/polyhedron.h>\n//#include <decomp_geometry/geometry_utils.h>\n\n/**\n * @brief Line Segment Class\n *\n * The basic element in EllipsoidDecomp\n */\ntemplate <int Dim>\nclass DecompBase {\n  public:\n    ///Null constructor\n    DecompBase() {}\n    /**\n     * @brief Adding local bounding box around line seg\n     * @param Dim Distance in corresponding axis\n     *\n     * This virtual bounding box is parallel to the line segment, the x,y,z axes are not w.r.t the world coordinate system, but instead, x-axis is parallel to the line, y-axis is perpendicular to the line and world z-axis, z-axis is perpendiculat to the line and y-axis\n     */\n    void set_local_bbox(const Vecf<Dim>& bbox) {\n      local_bbox_ = bbox;\n    }\n\n    ///Import obstacle points\n    void set_obs(const vec_Vecf<Dim> &obs) {\n      // only consider points inside local bbox\n      Polyhedron<Dim> vs;\n      add_local_bbox(vs);\n      obs_ = vs.points_inside(obs);\n    }\n\n    ///Get obstacel points\n    vec_Vecf<Dim> get_obs() const { return obs_; }\n\n    ///Get ellipsoid\n    Ellipsoid<Dim> get_ellipsoid() const { return ellipsoid_; }\n\n    ///Get polyhedron\n    Polyhedron<Dim> get_polyhedron() const { return polyhedron_; }\n\n    /**\n     * @brief Inflate the line segment\n     * @param radius the offset added to the long semi-axis\n     */\n    virtual void dilate(decimal_t radius = 0) = 0;\n\n    /**\n     * @brief Shrink the polyhedron\n     * @param shrink_distance Shrink distance\n     */\n    virtual void shrink(double shrink_distance) {}\n protected:\n    virtual void add_local_bbox(Polyhedron<Dim> &Vs) = 0;\n\n    void find_polyhedron() {\n      //**** find half-space\n      Polyhedron<Dim> Vs;\n      vec_Vecf<Dim> obs_remain = obs_;\n      while (!obs_remain.empty()) {\n        const auto v = ellipsoid_.closest_hyperplane(obs_remain);\n        Vs.add(v);\n        vec_Vecf<Dim> obs_tmp;\n        for (const auto &it : obs_remain) {\n          if (v.signed_dist(it) < 0)\n            obs_tmp.push_back(it);\n        }\n        obs_remain = obs_tmp;\n        /*\n           std::cout << \"a: \" << a.transpose() << std::endl;\n           std::cout << \"b: \" << b << std::endl;\n           */\n      }\n\n      polyhedron_ = Vs;\n    }\n\n    /// Obstacles, input\n    vec_Vecf<Dim> obs_;\n\n    /// Output ellipsoid\n    Ellipsoid<Dim> ellipsoid_;\n    /// Output polyhedron\n    Polyhedron<Dim> polyhedron_;\n\n    /// Local bounding box along the line segment\n    Vecf<Dim> local_bbox_{Vecf<Dim>::Zero()};\n};\n#endif\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/include/decomp_util/ellipsoid_decomp.h",
    "content": "/**\n * @file ellipsoid_decomp.h\n * @brief EllipsoidDecomp Class\n */\n#ifndef ELLIPSOID_DECOMP_H\n#define ELLIPSOID_DECOMP_H\n\n#include <memory>\n#include <decomp_util/line_segment.h>\n\n/**\n * @brief EllipsoidDecomp Class\n *\n * EllipsoidDecomp takes input as a given path and find the Safe Flight Corridor around it using Ellipsoids\n */\ntemplate <int Dim>\nclass EllipsoidDecomp {\npublic:\n ///Simple constructor\n EllipsoidDecomp() {}\n /**\n  * @brief Basic constructor\n  * @param origin The origin of the global bounding box\n  * @param dim The dimension of the global bounding box\n  */\n EllipsoidDecomp(const Vecf<Dim> &origin, const Vecf<Dim> &dim) {\n   global_bbox_min_ = origin;\n   global_bbox_max_ = origin + dim;\n }\n\n ///Set obstacle points\n void set_obs(const vec_Vecf<Dim> &obs) { obs_ = obs; }\n\n ///Set dimension of bounding box\n void set_local_bbox(const Vecf<Dim>& bbox) { local_bbox_ = bbox; }\n\n ///Get the path that is used for dilation\n vec_Vecf<Dim> get_path() const { return path_; }\n\n ///Get the Safe Flight Corridor\n vec_E<Polyhedron<Dim>> get_polyhedrons() const { return polyhedrons_; }\n\n ///Get the ellipsoids\n vec_E<Ellipsoid<Dim>> get_ellipsoids() const { return ellipsoids_; }\n\n ///Get the constraints of SFC as \\f$Ax\\leq b \\f$\n vec_E<LinearConstraint<Dim>> get_constraints() const {\n   vec_E<LinearConstraint<Dim>> constraints;\n   constraints.resize(polyhedrons_.size());\n   for (unsigned int i = 0; i < polyhedrons_.size(); i++){\n     const Vecf<Dim> pt = (path_[i] + path_[i+1])/2;\n     constraints[i] = LinearConstraint<Dim>(pt, polyhedrons_[i].hyperplanes());\n   }\n   return constraints;\n }\n\n /**\n  * @brief Decomposition thread\n  * @param path The path to dilate\n  * @param offset_x offset added to the long semi-axis, default is 0\n  */\n void dilate(const vec_Vecf<Dim> &path, double offset_x = 0) {\n   const unsigned int N = path.size() - 1;\n   lines_.resize(N);\n   ellipsoids_.resize(N);\n   polyhedrons_.resize(N);\n\n   for (unsigned int i = 0; i < N; i++) {\n     lines_[i] = std::make_shared<LineSegment<Dim>>(path[i], path[i+1]);\n     lines_[i]->set_local_bbox(local_bbox_);\n     lines_[i]->set_obs(obs_);\n     lines_[i]->dilate(offset_x);\n\n     ellipsoids_[i] = lines_[i]->get_ellipsoid();\n     polyhedrons_[i] = lines_[i]->get_polyhedron();\n   }\n\n\n   path_ = path;\n\n   if(global_bbox_min_.norm() != 0 || global_bbox_max_.norm() != 0) {\n     for(auto& it: polyhedrons_)\n       add_global_bbox(it);\n   }\n\n }\n\nprotected:\n template<int U = Dim>\n   typename std::enable_if<U == 2>::type\n   add_global_bbox(Polyhedron<Dim> &Vs) {\n     //**** add bound along X, Y axis\n\n     //*** X\n     Vs.add(Hyperplane2D(Vec2f(global_bbox_max_(0), 0), Vec2f(1, 0)));\n     Vs.add(Hyperplane2D(Vec2f(global_bbox_min_(0), 0), Vec2f(-1, 0)));\n     //*** Y\n     Vs.add(Hyperplane2D(Vec2f(0, global_bbox_max_(1)), Vec2f(0, 1)));\n     Vs.add(Hyperplane2D(Vec2f(0, global_bbox_min_(1)), Vec2f(0, -1)));\n   }\n\n template<int U = Dim>\n   typename std::enable_if<U == 3>::type\n   add_global_bbox(Polyhedron<Dim> &Vs) {\n     //**** add bound along X, Y, Z axis\n     //*** Z\n     Vs.add(Hyperplane3D(Vec3f(0, 0, global_bbox_max_(2)), Vec3f(0, 0, 1)));\n     Vs.add(Hyperplane3D(Vec3f(0, 0, global_bbox_min_(2)), Vec3f(0, 0, -1)));\n\n     //*** X\n     Vs.add(Hyperplane3D(Vec3f(global_bbox_max_(0), 0, 0), Vec3f(1, 0, 0)));\n     Vs.add(Hyperplane3D(Vec3f(global_bbox_min_(0), 0, 0), Vec3f(-1, 0, 0)));\n     //*** Y\n     Vs.add(Hyperplane3D(Vec3f(0, global_bbox_max_(1), 0), Vec3f(0, 1, 0)));\n     Vs.add(Hyperplane3D(Vec3f(0, global_bbox_max_(1), 0), Vec3f(0, -1, 0)));\n   }\n\n vec_Vecf<Dim> path_;\n vec_Vecf<Dim> obs_;\n\n vec_E<Ellipsoid<Dim>> ellipsoids_;\n vec_E<Polyhedron<Dim>> polyhedrons_;\n std::vector<std::shared_ptr<LineSegment<Dim>>> lines_;\n\n Vecf<Dim> local_bbox_{Vecf<Dim>::Zero()};\n Vecf<Dim> global_bbox_min_{Vecf<Dim>::Zero()}; // bounding box params\n Vecf<Dim> global_bbox_max_{Vecf<Dim>::Zero()};\n\n};\n\ntypedef EllipsoidDecomp<2> EllipsoidDecomp2D;\n\ntypedef EllipsoidDecomp<3> EllipsoidDecomp3D;\n#endif\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/include/decomp_util/iterative_decomp.h",
    "content": "/**\n * @file iterative_decomp.h\n * @brief IterativeDecomp Class\n */\n#ifndef ITERATIVE_DECOMP_H\n#define ITERATIVE_DECOMP_H\n\n#include <decomp_util/ellipsoid_decomp.h>\n\n/**\n * @brief IterativeDecomp Class\n *\n * Iteratively calls ElliseDecomp to form a safer Safe Flight Corridor that is away from obstacles\n */\ntemplate <int Dim>\nclass IterativeDecomp : public EllipsoidDecomp<Dim>\n{\n  public:\n    ///Simple constructor\n    IterativeDecomp() {}\n    /**\n     * @brief Basic constructor\n     * @param origin The origin of the global bounding box\n     * @param dim The dimension of the global bounding box\n     */\n    IterativeDecomp(const Vecf<Dim> &origin, const Vecf<Dim> &dim) :\n        EllipsoidDecomp<Dim>(origin, dim) {}\n    /**\n     * @brief Decomposition thread\n     * @param path_raw The path to dilate\n     * @param iter_num Max iteration number\n     * @param offset_x offset added to the long semi-axis, default is 0\n     * @param res Resolution to downsample the path\n     */\n    void dilate_iter(const vec_Vecf<Dim> &path_raw, int iter_num = 5,\n                decimal_t res = 0, decimal_t offset_x = 0) {\n      vec_Vecf<Dim> path = res > 0 ? downsample(path_raw, res) : path_raw;\n      this->dilate(path, offset_x);\n      vec_Vecf<Dim> new_path = simplify(path);\n      for (int i = 0; i < iter_num; i++) {\n        if (new_path.size() == path.size())\n          break;\n        else {\n          path = new_path;\n          this->dilate(path, offset_x);\n          new_path = simplify(path);\n        }\n      }\n    }\n\n  protected:\n    /// Uniformly sample path into many segments\n    vec_Vecf<Dim> downsample(const vec_Vecf<Dim> &ps, decimal_t d) {\n      // subdivide according to length\n      if (ps.size() < 2)\n        return ps;\n      vec_Vecf<Dim> path;\n      for (unsigned int i = 1; i < ps.size(); i++) {\n        decimal_t dist = (ps[i] - ps[i - 1]).norm();\n        int cnt = std::ceil(dist / d);\n        for (int j = 0; j < cnt; j++)\n          path.push_back(ps[i - 1] + j * (ps[i] - ps[i - 1]) / cnt);\n      }\n      path.push_back(ps.back());\n      return path;\n    }\n\n    /// Get closest distance\n    decimal_t cal_closest_dist(const Vecf<Dim>& pt, const Polyhedron<Dim>& vs){\n      decimal_t dist = std::numeric_limits<decimal_t>::infinity();\n      for(const auto& it: vs.hyperplanes()){\n        decimal_t d = std::abs(it.n_.dot(pt - it.p_));\n        if(d < dist)\n          dist = d;\n      }\n      return dist;\n    }\n\n    /// Remove redundant waypoints\n    vec_Vecf<Dim> simplify(const vec_Vecf<Dim>& path) {\n\t\t\tif(path.size() <= 2)\n\t\t\t\treturn path;\n\n\t\t\tVecf<Dim> ref_pt = path.front();\n\t\t\tvec_Vecf<Dim> new_path;\n\t\t\tnew_path.push_back(ref_pt);\n\n\t\t\tfor(size_t i = 2; i < path.size(); i ++){\n\t\t\t\tif(this->polyhedrons_[i-1].inside(ref_pt) &&\n\t\t\t\t\t cal_closest_dist(ref_pt, this->polyhedrons_[i-1]) > 0.1) {\n\t\t\t\t}\n\t\t\t\telse{\n\t\t\t\t\tref_pt = path[i-1];\n\t\t\t\t\tnew_path.push_back(ref_pt);\n\t\t\t\t}\n\t\t\t}\n\t\t\tnew_path.push_back(path.back());\n\t\t\treturn new_path;\n\t\t}\n};\n\ntypedef IterativeDecomp<2> IterativeDecomp2D;\n\ntypedef IterativeDecomp<3> IterativeDecomp3D;\n#endif\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/include/decomp_util/line_segment.h",
    "content": "/**\n * @file line_segment.h\n * @brief LineSegment Class\n */\n#ifndef LINE_SEGMENT_H\n#define LINE_SEGMENT_H\n\n#include <decomp_util/decomp_base.h>\n#include <decomp_geometry/geometric_utils.h>\n\n/**\n * @brief Line Segment Class\n *\n * The basic element in EllipsoidDecomp\n */\ntemplate <int Dim>\nclass LineSegment : public DecompBase<Dim> {\n  public:\n    ///Simple constructor\n    LineSegment() {};\n    /**\n     * @brief Basic constructor\n     * @param p1 One end of the line seg\n     * @param p2 The other end of the line seg\n     */\n    LineSegment(const Vecf<Dim> &p1, const Vecf<Dim> &p2) : p1_(p1), p2_(p2) {}\n    /**\n     * @brief Infalte the line segment\n     * @param radius the offset added to the long semi-axis\n     */\n    void dilate(decimal_t radius) {\n      find_ellipsoid(radius);\n      this->find_polyhedron();\n      add_local_bbox(this->polyhedron_);\n    }\n\n    /// Get the line\n    vec_Vecf<Dim> get_line_segment() const {\n      vec_Vecf<Dim> line;\n      line.push_back(p1_);\n      line.push_back(p2_);\n      return line;\n    }\n\n  protected:\n    ///Add the bounding box\n    void add_local_bbox(Polyhedron<Dim> &Vs) {\n      if(this->local_bbox_.norm() == 0)\n        return;\n      //**** virtual walls parallel to path p1->p2\n      Vecf<Dim> dir = (p2_ - p1_).normalized();\n      Vecf<Dim> dir_h = Vecf<Dim>::Zero();\n      dir_h(0) = dir(1), dir_h(1) = -dir(0);\n      if (dir_h.norm() == 0) {\n        if(Dim == 2)\n          dir_h << -1, 0;\n        else\n          dir_h << -1, 0, 0;\n      }\n      dir_h = dir_h.normalized();\n\n      // along x\n      Vecf<Dim> pp1 = p1_ + dir_h * this->local_bbox_(1);\n      Vecf<Dim> pp2 = p1_ - dir_h * this->local_bbox_(1);\n      Vs.add(Hyperplane<Dim>(pp1, dir_h));\n      Vs.add(Hyperplane<Dim>(pp2, -dir_h));\n\n      // along y\n      Vecf<Dim> pp3 = p2_ + dir * this->local_bbox_(0);\n      Vecf<Dim> pp4 = p1_ - dir * this->local_bbox_(0);\n      Vs.add(Hyperplane<Dim>(pp3, dir));\n      Vs.add(Hyperplane<Dim>(pp4, -dir));\n\n      // along z\n      if(Dim > 2) {\n        Vecf<Dim> dir_v;\n        dir_v(0) = dir(1) * dir_h(2) - dir(2) * dir_h(1);\n        dir_v(1) = dir(2) * dir_h(0) - dir(0) * dir_h(2);\n        dir_v(2) = dir(0) * dir_h(1) - dir(1) * dir_h(0);\n        Vecf<Dim> pp5 = p1_ + dir_v * this->local_bbox_(2);\n        Vecf<Dim> pp6 = p1_ - dir_v * this->local_bbox_(2);\n        Vs.add(Hyperplane<Dim>(pp5, dir_v));\n        Vs.add(Hyperplane<Dim>(pp6, -dir_v));\n      }\n    }\n\n    /// Find ellipsoid in 2D\n    template<int U = Dim>\n      typename std::enable_if<U == 2>::type\n      find_ellipsoid(double offset_x) {\n        const decimal_t f = (p1_ - p2_).norm() / 2;\n        Matf<Dim, Dim> C = f * Matf<Dim, Dim>::Identity();\n        Vecf<Dim> axes = Vecf<Dim>::Constant(f);\n        C(0, 0) += offset_x;\n        axes(0) += offset_x;\n\n        if(axes(0) > 0) {\n          double ratio = axes(1) / axes(0);\n          axes *= ratio;\n          C *= ratio;\n        }\n\n        const auto Ri = vec2_to_rotation(p2_ - p1_);\n        C = Ri * C * Ri.transpose();\n\n        Ellipsoid<Dim> E(C, (p1_ + p2_) / 2);\n\n        auto obs = E.points_inside(this->obs_);\n\n        auto obs_inside = obs;\n        //**** decide short axes\n        while (!obs_inside.empty()) {\n          const auto pw = E.closest_point(obs_inside);\n          Vecf<Dim> p = Ri.transpose() * (pw - E.d()); // to ellipsoid frame\n          if(p(0) < axes(0))\n            axes(1) = std::abs(p(1)) / std::sqrt(1 - std::pow(p(0) / axes(0), 2));\n          Matf<Dim, Dim> new_C = Matf<Dim, Dim>::Identity();\n          new_C(0, 0) = axes(0);\n          new_C(1, 1) = axes(1);\n          E.C_ = Ri * new_C * Ri.transpose();\n\n          vec_Vecf<Dim> obs_new;\n          for(const auto &it: obs_inside) {\n            if(1 - E.dist(it) > epsilon_)\n              obs_new.push_back(it);\n          }\n          obs_inside = obs_new;\n        }\n\n        this->ellipsoid_ = E;\n      }\n\n    /// Find ellipsoid in 3D\n    template<int U = Dim>\n      typename std::enable_if<U == 3>::type\n      find_ellipsoid(double offset_x) {\n      const decimal_t f = (p1_ - p2_).norm() / 2;\n      Matf<Dim, Dim> C = f * Matf<Dim, Dim>::Identity();\n      Vecf<Dim> axes = Vecf<Dim>::Constant(f);\n      C(0, 0) += offset_x;\n      axes(0) += offset_x;\n\n      if(axes(0) > 0) {\n        double ratio = axes(1) / axes(0);\n        axes *= ratio;\n        C *= ratio;\n      }\n\n      const auto Ri = vec3_to_rotation(p2_ - p1_);\n      C = Ri * C * Ri.transpose();\n\n      Ellipsoid<Dim> E(C, (p1_ + p2_) / 2);\n      auto Rf = Ri;\n\n      auto obs = E.points_inside(this->obs_);\n      auto obs_inside = obs;\n      //**** decide short axes\n      while (!obs_inside.empty()) {\n        const auto pw = E.closest_point(obs_inside);\n        Vecf<Dim> p = Ri.transpose() * (pw - E.d()); // to ellipsoid frame\n        const decimal_t roll = atan2(p(2), p(1));\n        Rf = Ri * Quatf(cos(roll / 2), sin(roll / 2), 0, 0);\n        p = Rf.transpose() * (pw - E.d());\n\n        if(p(0) < axes(0))\n          axes(1) = std::abs(p(1)) / std::sqrt(1 - std::pow(p(0) / axes(0), 2));\n        Matf<Dim, Dim> new_C = Matf<Dim, Dim>::Identity();\n        new_C(0, 0) = axes(0);\n        new_C(1, 1) = axes(1);\n        new_C(2, 2) = axes(1);\n        E.C_ = Rf * new_C * Rf.transpose();\n\n        vec_Vecf<Dim> obs_new;\n        for(const auto &it: obs_inside) {\n          if(1 - E.dist(it) > epsilon_)\n            obs_new.push_back(it);\n        }\n        obs_inside = obs_new;\n      }\n\n      //**** reset ellipsoid with old axes(2)\n      C = f * Matf<Dim, Dim>::Identity();\n      C(0, 0) = axes(0);\n      C(1, 1) = axes(1);\n      C(2, 2) = axes(2);\n      E.C_ = Rf * C * Rf.transpose();\n      obs_inside = E.points_inside(obs);\n\n      while (!obs_inside.empty()) {\n        const auto pw = E.closest_point(obs_inside);\n        Vec3f p = Rf.transpose() * (pw - E.d());\n        decimal_t dd = 1 - std::pow(p(0) / axes(0), 2) -\n          std::pow(p(1) / axes(1), 2);\n        if(dd > epsilon_)\n          axes(2) = std::abs(p(2)) / std::sqrt(dd);\n        Matf<Dim, Dim> new_C = Matf<Dim, Dim>::Identity();\n        new_C(0, 0) = axes(0);\n        new_C(1, 1) = axes(1);\n        new_C(2, 2) = axes(2);\n        E.C_ = Rf * new_C * Rf.transpose();\n\n        vec_Vecf<Dim> obs_new;\n        for(const auto &it: obs_inside) {\n          if(1 - E.dist(it) > epsilon_)\n            obs_new.push_back(it);\n        }\n        obs_inside = obs_new;\n      }\n\n      this->ellipsoid_ = E;\n    }\n\n    /// One end of line segment, input\n    Vecf<Dim> p1_;\n    /// The other end of line segment, input\n    Vecf<Dim> p2_;\n};\n\ntypedef LineSegment<2> LineSegment2D;\n\ntypedef LineSegment<3> LineSegment3D;\n#endif\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/include/decomp_util/seed_decomp.h",
    "content": "/**\n * @file seed_decomp.h\n * @brief SeedDecomp Class\n */\n#ifndef SEED_DECOMP_H\n#define SEED_DECOMP_H\n\n#include <decomp_util/decomp_base.h>\n\n/**\n * @brief Seed Decomp Class\n *\n * Dilate around the given point\n */\ntemplate <int Dim>\nclass SeedDecomp : public DecompBase<Dim> {\n  public:\n    ///Simple constructor\n    SeedDecomp() {};\n    /**\n     * @brief Basic constructor\n     * @param p1 One end of the line seg\n     * @param p2 The other end of the line seg\n     */\n    SeedDecomp(const Vecf<Dim> &p) : p_(p) {}\n    /**\n     * @brief Inflate the seed with a sphere\n     * @param radius Robot radius\n     */\n    void dilate(decimal_t radius) {\n      this->ellipsoid_ = Ellipsoid<Dim>(radius * Matf<Dim, Dim>::Identity(), p_);\n      this->find_polyhedron();\n      add_local_bbox(this->polyhedron_);\n    }\n\n    /// Get the center\n    Vecf<Dim> get_seed() const {\n      return p_;\n    }\n\n  protected:\n    ///Add the bounding box\n    void add_local_bbox(Polyhedron<Dim> &Vs) {\n      if(this->local_bbox_.norm() == 0)\n        return;\n\n      //**** virtual walls x-y-z\n      Vecf<Dim> dir = Vecf<Dim>::UnitX();\n      Vecf<Dim> dir_h = Vecf<Dim>::UnitY();\n\n      Vecf<Dim> pp1 = p_ + dir_h * this->local_bbox_(1);\n      Vecf<Dim> pp2 = p_ - dir_h * this->local_bbox_(1);\n      Vs.add(Hyperplane<Dim>(pp1, dir_h));\n      Vs.add(Hyperplane<Dim>(pp2, -dir_h));\n\n      // along y\n      Vecf<Dim> pp3 = p_ + dir * this->local_bbox_(0);\n      Vecf<Dim> pp4 = p_ - dir * this->local_bbox_(0);\n      Vs.add(Hyperplane<Dim>(pp3, dir));\n      Vs.add(Hyperplane<Dim>(pp4, -dir));\n\n      // along z\n      if(Dim > 2) {\n        Vecf<Dim> dir_v = Vecf<Dim>::UnitZ();\n        Vecf<Dim> pp5 = p_ + dir_v * this->local_bbox_(2);\n        Vecf<Dim> pp6 = p_ - dir_v * this->local_bbox_(2);\n        Vs.add(Hyperplane<Dim>(pp5, dir_v));\n        Vs.add(Hyperplane<Dim>(pp6, -dir_v));\n      }\n    }\n\n    ///Seed location\n    Vecf<Dim> p_;\n};\n\ntypedef SeedDecomp<2> SeedDecomp2D;\n\ntypedef SeedDecomp<3> SeedDecomp3D;\n\n#endif\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/package.xml",
    "content": "<?xml version=\"1.0\"?>\n<package format=\"2\">\n  <name>decomp_ros_utils</name>\n  <version>0.0.0</version>\n  <description>The decomp_ros_utils package</description>\n\n  <maintainer email=\"sikang@todo.todo\">sikang</maintainer>\n\n  <license>TODO</license>\n\n  <buildtool_depend>catkin</buildtool_depend>\n  <build_depend>catkin_simple</build_depend>\n  <depend>roscpp</depend>\n  <depend>rviz</depend>\n  <depend>decomp_util</depend>\n  <depend>decomp_ros_msgs</depend>\n\n  <export>\n    <rviz plugin=\"${prefix}/plugin_description.xml\"/>\n  </export>\n</package>\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/plugin_description.xml",
    "content": "<library path=\"lib/libdecomp_rviz_plugins\">\n  <class name=\"decomp_rviz_plugins/EllipsoidArray\"\n    type=\"decomp_rviz_plugins::EllipsoidArrayDisplay\"\n    base_class_type=\"rviz::Display\">\n    <description>\n      visualize decomp_ros_msgs/EllipsoidArray msg.\n    </description>\n  </class>\n\n  <class name=\"decomp_rviz_plugins/PolyhedronArray\"\n    type=\"decomp_rviz_plugins::PolyhedronArrayDisplay\"\n    base_class_type=\"rviz::Display\">\n    <description>\n      visualize decomp_ros_msgs/PolyhedronArray msg.\n    </description>\n  </class>\n\n</library>\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/src/bound_visual.cpp",
    "content": "#include \"bound_visual.h\"\n\nnamespace decomp_rviz_plugins {\n  BoundVisual::BoundVisual(Ogre::SceneManager *scene_manager,\n                           Ogre::SceneNode *parent_node) {\n    scene_manager_ = scene_manager;\n    frame_node_ = parent_node->createChildSceneNode();\n  }\n\n  BoundVisual::~BoundVisual() { scene_manager_->destroySceneNode(frame_node_); }\n\n  void BoundVisual::setMessage(const vec_E<vec_Vec3f>& bds) {\n    objs_.clear();\n\n    if (bds.empty())\n      return;\n\n    size_t num_faces = bds.size();\n    objs_.resize(num_faces);\n    for (auto &it : objs_)\n      it.reset(new rviz::BillboardLine(scene_manager_, frame_node_));\n\n    int cnt = 0;\n    for (const auto &vs : bds) {\n      for (unsigned int i = 0; i <= vs.size(); i++) {\n        if (i < vs.size()) {\n          if(!std::isnan(vs[i](0)))\n            objs_[cnt]->addPoint(Ogre::Vector3(vs[i](0), vs[i](1), vs[i](2)));\n        }\n        else {\n          if(!std::isnan(vs[0](0)))\n          objs_[cnt]->addPoint(Ogre::Vector3(vs[0](0), vs[0](1), vs[0](2)));\n        }\n      }\n      cnt++;\n    }\n  }\n\n  void BoundVisual::setFramePosition(const Ogre::Vector3 &position) {\n    frame_node_->setPosition(position);\n  }\n\n  void BoundVisual::setFrameOrientation(const Ogre::Quaternion &orientation) {\n    frame_node_->setOrientation(orientation);\n  }\n\n  void BoundVisual::setColor(float r, float g, float b, float a) {\n    for (auto &it : objs_)\n      it->setColor(r, g, b, a);\n  }\n\n  void BoundVisual::setScale(float s) {\n    for (auto &it : objs_)\n      it->setLineWidth(s);\n  }\n}\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/src/bound_visual.h",
    "content": "#ifndef BOUND_VISUAL_H\n#define BOUND_VISUAL_H\n\n#include <decomp_basis/data_type.h>\n\n#include <OGRE/OgreVector3.h>\n#include <OGRE/OgreSceneNode.h>\n#include <OGRE/OgreSceneManager.h>\n\n#include <rviz/ogre_helpers/billboard_line.h>\n\n\nnamespace decomp_rviz_plugins {\nclass BoundVisual {\npublic:\n  BoundVisual(Ogre::SceneManager *scene_manager, Ogre::SceneNode *parent_node);\n  ~BoundVisual();\n\n  void setMessage(const vec_E<vec_Vec3f> &bds);\n  void setFramePosition(const Ogre::Vector3 &position);\n  void setFrameOrientation(const Ogre::Quaternion &orientation);\n\n  void setColor(float r, float g, float b, float a);\n  void setScale(float s);\n\nprivate:\n  std::vector<std::unique_ptr<rviz::BillboardLine>> objs_;\n\n  Ogre::SceneNode *frame_node_;\n\n  Ogre::SceneManager *scene_manager_;\n};\n}\n\n#endif\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/src/ellipsoid_array_display.cpp",
    "content": "#include <tf/transform_listener.h>\n#include \"ellipsoid_array_display.h\"\n\nnamespace decomp_rviz_plugins {\n\nEllipsoidArrayDisplay::EllipsoidArrayDisplay() {\n  color_property_ = new rviz::ColorProperty(\"Color\", QColor(204, 51, 204),\n                                            \"Color of ellipsoids.\",\n                                            this, SLOT(updateColorAndAlpha()));\n  alpha_property_ = new rviz::FloatProperty(\n      \"Alpha\", 0.5, \"0 is fully transparent, 1.0 is fully opaque.\", this,\n      SLOT(updateColorAndAlpha()));\n\n}\n\nvoid EllipsoidArrayDisplay::onInitialize() {\n  MFDClass::onInitialize();\n}\n\nEllipsoidArrayDisplay::~EllipsoidArrayDisplay() {}\n\nvoid EllipsoidArrayDisplay::reset() {\n  MFDClass::reset();\n  visual_ = nullptr;\n}\n\nvoid EllipsoidArrayDisplay::updateColorAndAlpha() {\n  float alpha = alpha_property_->getFloat();\n  Ogre::ColourValue color = color_property_->getOgreColor();\n\n  if (visual_)\n    visual_->setColor(color.r, color.g, color.b, alpha);\n}\n\nvoid EllipsoidArrayDisplay::processMessage(const decomp_ros_msgs::EllipsoidArray::ConstPtr &msg) {\n  Ogre::Quaternion orientation;\n  Ogre::Vector3 position;\n  if (!context_->getFrameManager()->getTransform(\n          msg->header.frame_id, msg->header.stamp, position, orientation)) {\n    ROS_DEBUG(\"Error transforming from frame '%s' to frame '%s'\",\n              msg->header.frame_id.c_str(), qPrintable(fixed_frame_));\n    return;\n  }\n\n  std::shared_ptr<EllipsoidArrayVisual> visual;\n  visual.reset(new EllipsoidArrayVisual(context_->getSceneManager(), scene_node_));\n\n  visual->setMessage(msg);\n  visual->setFramePosition(position);\n  visual->setFrameOrientation(orientation);\n\n  float alpha = alpha_property_->getFloat();\n  Ogre::ColourValue color = color_property_->getOgreColor();\n  visual->setColor(color.r, color.g, color.b, alpha);\n\n  visual_ = visual;\n}\n\n}\n\n#include <pluginlib/class_list_macros.h>\nPLUGINLIB_EXPORT_CLASS(decomp_rviz_plugins::EllipsoidArrayDisplay, rviz::Display)\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/src/ellipsoid_array_display.h",
    "content": "#include <OGRE/OgreSceneNode.h>\n#include <OGRE/OgreSceneManager.h>\n#include <rviz/visualization_manager.h>\n#include <rviz/properties/color_property.h>\n#include <rviz/properties/float_property.h>\n#include <rviz/frame_manager.h>\n\n#include <rviz/load_resource.h>\n\n#include <decomp_ros_msgs/EllipsoidArray.h>\n#include <rviz/message_filter_display.h>\n#include \"ellipsoid_array_visual.h\"\n\nnamespace decomp_rviz_plugins {\n\nclass EllipsoidArrayVisual;\n\nclass EllipsoidArrayDisplay\n    : public rviz::MessageFilterDisplay<decomp_ros_msgs::EllipsoidArray> {\n  Q_OBJECT\npublic:\n  EllipsoidArrayDisplay();\n  ~EllipsoidArrayDisplay();\n\nprotected:\n  void onInitialize();\n\n  void reset();\n\nprivate Q_SLOTS:\n  void updateColorAndAlpha();\n\nprivate:\n  void processMessage(const decomp_ros_msgs::EllipsoidArray::ConstPtr &msg);\n\n  std::shared_ptr<EllipsoidArrayVisual> visual_;\n\n  rviz::ColorProperty *color_property_;\n  rviz::FloatProperty *alpha_property_;\n};\n}\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/src/ellipsoid_array_visual.cpp",
    "content": "#include \"ellipsoid_array_visual.h\"\n\nnamespace decomp_rviz_plugins {\n  EllipsoidArrayVisual::EllipsoidArrayVisual(Ogre::SceneManager *scene_manager,\n                                     Ogre::SceneNode *parent_node) {\n    scene_manager_ = scene_manager;\n    frame_node_ = parent_node->createChildSceneNode();\n  }\n\n  EllipsoidArrayVisual::~EllipsoidArrayVisual() {\n    scene_manager_->destroySceneNode(frame_node_);\n  }\n\n  void EllipsoidArrayVisual::setMessage(const decomp_ros_msgs::EllipsoidArray::ConstPtr &msg) {\n    objs_.clear();\n\n    if (msg->ellipsoids.empty())\n      return;\n\n    for (const auto& it: msg->ellipsoids) {\n      if(std::isnan(it.d[0]) ||\n         std::isnan(it.d[1]) ||\n         std::isnan(it.d[2]))\n        return;\n      for (int i = 0; i < 3; i++)\n        for (int j = 0; j < 3; j++)\n          if(std::isnan(it.E[3 * i + j]))\n            return;\n    }\n\n    objs_.resize(msg->ellipsoids.size());\n\n    for (auto &it : objs_)\n      it.reset(new rviz::Shape(rviz::Shape::Type::Sphere, scene_manager_,\n                               frame_node_));\n\n    int cnt = 0;\n    for (const auto &it : msg->ellipsoids) {\n      Mat3f E;\n      for (int i = 0; i < 3; i++)\n        for (int j = 0; j < 3; j++)\n          E(i, j) = it.E[3 * i + j];\n      Eigen::SelfAdjointEigenSolver<Mat3f> es(E);\n\n      Ogre::Vector3 scale(2 * es.eigenvalues()[0], 2 * es.eigenvalues()[1],\n                          2 * es.eigenvalues()[2]);\n      objs_[cnt]->setScale(scale);\n\n      Ogre::Vector3 d(it.d[0], it.d[1], it.d[2]);\n      objs_[cnt]->setPosition(d);\n\n      Quatf q(es.eigenvectors().determinant() * es.eigenvectors());\n      Ogre::Quaternion o(q.w(), q.x(), q.y(), q.z());\n      objs_[cnt]->setOrientation(o);\n      cnt++;\n    }\n  }\n\n  void EllipsoidArrayVisual::setFramePosition(const Ogre::Vector3 &position) {\n    frame_node_->setPosition(position);\n  }\n\n  void EllipsoidArrayVisual::setFrameOrientation(\n                                             const Ogre::Quaternion &orientation) {\n    frame_node_->setOrientation(orientation);\n  }\n\n  void EllipsoidArrayVisual::setColor(float r, float g, float b, float a) {\n    for (auto &it : objs_)\n      it->setColor(r, g, b, a);\n  }\n}\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/src/ellipsoid_array_visual.h",
    "content": "#ifndef ELLIPSOIDS_VISUAL_H\n#define ELLIPSOIDS_VISUAL_H\n\n#include <decomp_ros_msgs/EllipsoidArray.h>\n#include <decomp_geometry/ellipsoid.h>\n#include <Eigen/Eigenvalues>\n\n#include <OGRE/OgreVector3.h>\n#include <OGRE/OgreSceneNode.h>\n#include <OGRE/OgreSceneManager.h>\n\n#include <rviz/ogre_helpers/shape.h>\n\nnamespace decomp_rviz_plugins {\n  class EllipsoidArrayVisual {\n    public:\n      EllipsoidArrayVisual(Ogre::SceneManager *scene_manager,\n                       Ogre::SceneNode *parent_node);\n\n      virtual ~EllipsoidArrayVisual();\n\n      void setMessage(const decomp_ros_msgs::EllipsoidArray::ConstPtr &msg);\n\n      void setFramePosition(const Ogre::Vector3 &position);\n      void setFrameOrientation(const Ogre::Quaternion &orientation);\n\n      void setColor(float r, float g, float b, float a);\n\n    private:\n      std::vector<std::unique_ptr<rviz::Shape>> objs_;\n\n      Ogre::SceneNode *frame_node_;\n\n      Ogre::SceneManager *scene_manager_;\n  };\n}\n#endif\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/src/mesh_visual.cpp",
    "content": "#include \"mesh_visual.h\"\n\nnamespace decomp_rviz_plugins {\n  MeshVisual::MeshVisual(Ogre::SceneManager *scene_manager,\n                         Ogre::SceneNode *parent_node) {\n    scene_manager_ = scene_manager;\n    frame_node_ = parent_node->createChildSceneNode();\n    obj_.reset(new rviz::MeshShape(scene_manager_, frame_node_));\n  }\n\n  MeshVisual::~MeshVisual() { scene_manager_->destroySceneNode(frame_node_); }\n\n  void MeshVisual::setMessage(const vec_E<vec_Vec3f> &bds) {\n    obj_->clear();\n\n    if (bds.empty())\n      return;\n\n    obj_->beginTriangles();\n    int free_cnt = 0;\n    for (const auto &vs: bds) {\n      if (vs.size() > 2) {\n        Vec3f p0 = vs[0];\n        Vec3f p1 = vs[1];\n        Vec3f p2 = vs[2];\n        Vec3f n = (p2-p0).cross(p1-p0);\n        n = n.normalized();\n        if(std::isnan(n(0)))\n          n = Vec3f(0, 0, -1);\n\n        int ref_cnt = free_cnt;\n        Ogre::Vector3 normal(n(0), n(1), n(2));\n        for (unsigned int i = 0; i < vs.size(); i++) {\n          obj_->addVertex(Ogre::Vector3(vs[i](0), vs[i](1), vs[i](2)), normal);\n          if (i > 1 && i < vs.size())\n            obj_->addTriangle(ref_cnt, free_cnt - 1, free_cnt);\n          free_cnt++;\n        }\n      }\n    }\n    obj_->endTriangles();\n  }\n\n  // Position and orientation are passed through to the SceneNode.\n  void MeshVisual::setFramePosition(const Ogre::Vector3 &position) {\n    frame_node_->setPosition(position);\n  }\n\n  void MeshVisual::setFrameOrientation(const Ogre::Quaternion &orientation) {\n    frame_node_->setOrientation(orientation);\n  }\n\n  void MeshVisual::setColor(float r, float g, float b, float a) {\n    obj_->setColor(r, g, b, a);\n  }\n}\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/src/mesh_visual.h",
    "content": "#ifndef MESH_VISUAL_H\n#define MESH_VISUAL_H\n\n#include <decomp_geometry/polyhedron.h>\n#include <Eigen/Eigenvalues>\n\n#include <OGRE/OgreVector3.h>\n#include <OGRE/OgreSceneNode.h>\n#include <OGRE/OgreSceneManager.h>\n\n#include <rviz/ogre_helpers/mesh_shape.h>\n\nnamespace decomp_rviz_plugins {\n  class MeshVisual {\n    public:\n      MeshVisual(Ogre::SceneManager *scene_manager, Ogre::SceneNode *parent_node);\n\n      virtual ~MeshVisual();\n\n      void setMessage(const vec_E<vec_Vec3f> &bds);\n      void setFramePosition(const Ogre::Vector3 &position);\n      void setFrameOrientation(const Ogre::Quaternion &orientation);\n\n      void setColor(float r, float g, float b, float a);\n\n    private:\n      std::unique_ptr<rviz::MeshShape> obj_;\n\n      Ogre::SceneNode *frame_node_;\n\n      Ogre::SceneManager *scene_manager_;\n  };\n}\n\n#endif\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/src/polyhedron_array_display.cpp",
    "content": "#include \"polyhedron_array_display.h\"\n#include <ros/ros.h>\n#include <ros/console.h>\n\nnamespace decomp_rviz_plugins {\n\nPolyhedronArrayDisplay::PolyhedronArrayDisplay() {\n  mesh_color_property_ =\n      new rviz::ColorProperty(\"MeshColor\", QColor(0, 170, 255), \"Mesh color.\",\n                              this, SLOT(updateMeshColorAndAlpha()));\n  bound_color_property_ =\n      new rviz::ColorProperty(\"BoundColor\", QColor(255, 0, 0), \"Bound color.\",\n                              this, SLOT(updateBoundColorAndAlpha()));\n\n  alpha_property_ = new rviz::FloatProperty(\n      \"Alpha\", 0.2,\n      \"0 is fully transparent, 1.0 is fully opaque, only affect mesh\", this,\n      SLOT(updateMeshColorAndAlpha()));\n\n  scale_property_ = new rviz::FloatProperty(\"Scale\", 0.1, \"bound scale.\", this,\n                                            SLOT(updateScale()));\n\n  vs_scale_property_ = new rviz::FloatProperty(\"VsScale\", 1.0, \"Vs scale.\", this,\n                                               SLOT(updateVsScale()));\n\n  vs_color_property_ =\n    new rviz::ColorProperty(\"VsColor\", QColor(0, 255, 0), \"Vs color.\",\n                            this, SLOT(updateVsColorAndAlpha()));\n\n\n\n  state_property_ = new rviz::EnumProperty(\n      \"State\", \"Mesh\", \"A Polygon can be represented as two states: Mesh and \"\n                       \"Bound, this option allows selecting visualizing Polygon\"\n                       \"in corresponding state\",\n      this, SLOT(updateState()));\n  state_property_->addOption(\"Mesh\", 0);\n  state_property_->addOption(\"Bound\", 1);\n  state_property_->addOption(\"Both\", 2);\n  state_property_->addOption(\"Vs\", 3);\n\n}\n\nvoid PolyhedronArrayDisplay::onInitialize() { MFDClass::onInitialize(); }\n\nPolyhedronArrayDisplay::~PolyhedronArrayDisplay() {}\n\nvoid PolyhedronArrayDisplay::reset() {\n  MFDClass::reset();\n  visual_mesh_ = nullptr;\n  visual_bound_ = nullptr;\n  visual_vector_ = nullptr;\n}\n\nvoid PolyhedronArrayDisplay::processMessage(const decomp_ros_msgs::PolyhedronArray::ConstPtr &msg) {\n/*  if (!context_->getFrameManager()->getTransform(\n          msg->header.frame_id, msg->header.stamp, position_, orientation_)) {\n    ROS_DEBUG(\"Error transforming from frame '%s' to frame '%s'\",\n              msg->header.frame_id.c_str(), qPrintable(fixed_frame_));\n    return;\n  }\n*/\n\n  //ros::Time time_1 = ros::Time::now();  \n  vertices_.clear();\n  //vs_.clear();\n\n  if(msg->ids.size() == 0)\n  { \n    for(auto bds: bdss_)\n      bds.clear();\n\n    poly_ids_.clear();\n    bdss_.clear();\n\n    return;\n  }\n\n  const auto polys = DecompROS::ros_to_polyhedron_array(*msg);\n  /*for(int i = 0; i < (int)polys.size(); i++)\n    printf(\"id: %d\\n\", msg->ids[i]);*/\n\n  for(int i = 0; i < (int)polys.size(); i++)\n  { \n    auto polyhedron = polys[i];\n    int  id = msg->ids[i];\n\n    bool is_insert = true;\n    for(auto index: poly_ids_)\n      if(index == id)\n        is_insert = false;\n\n    if(is_insert)\n    {\n      //std::cout<<\"insert one polyhedron, id : \"<<id<<std::endl;\n      vec_E<vec_Vec3f> bds = cal_vertices(polyhedron);\n      //vertices_.insert(vertices_.end(), bds.begin(), bds.end());\n      bdss_.push_back(bds);\n      poly_ids_.push_back(id);\n    }\n\n    /*cout<<\"polyhedron.id: \"<<polyhedron.id<<endl;\n    poly_id_. insert(polyhedron.id);*/\n\n    /*const auto vs = polyhedron.cal_normals();\n    vs_.insert(vs_.end(), vs.begin(), vs.end());*/\n  }\n\n  vec_E<int>       poly_ids_tmp;\n  vec_E< vec_E<vec_Vec3f> >  bdss_tmp;\n\n  for(int i = 0; i < (int)poly_ids_.size(); i++)\n  { \n    int poly_id = poly_ids_[i];\n\n    bool is_delete = true;\n    for(auto msg_id: msg->ids)\n      if(poly_id == msg_id)\n        is_delete = false;\n\n    if(!is_delete)\n    { \n      bdss_tmp.push_back(bdss_[i]);\n      poly_ids_tmp.push_back(poly_ids_[i]);\n    }\n    else\n    {\n      //std::cout<<\"delete one polyhedron\"<<std::endl;\n    }\n  }\n  \n  for(auto bds: bdss_tmp)\n    for(auto bd: bds)\n      vertices_.push_back(bd);\n        \n  poly_ids_ = poly_ids_tmp;\n  bdss_     = bdss_tmp;\n\n  int state = state_property_->getOptionInt();\n  visualizeMessage(state);\n\n  //ros::Time time_2 = ros::Time::now();\n  //ROS_WARN(\"time in processMessage is %f\",   (time_2 - time_1).toSec());\n}\n\nvoid PolyhedronArrayDisplay::visualizeMesh() {\n  std::shared_ptr<MeshVisual> visual_mesh;\n  visual_mesh.reset(new MeshVisual(context_->getSceneManager(), scene_node_));\n\n  visual_mesh->setMessage(vertices_);\n  visual_mesh->setFramePosition(position_);\n  visual_mesh->setFrameOrientation(orientation_);\n\n  float alpha = alpha_property_->getFloat();\n  Ogre::ColourValue color = mesh_color_property_->getOgreColor();\n  visual_mesh->setColor(color.r, color.g, color.b, alpha);\n  visual_mesh_ = visual_mesh;\n}\n\nvoid PolyhedronArrayDisplay::visualizeBound() {\n  std::shared_ptr<BoundVisual> visual_bound;\n  visual_bound.reset(new BoundVisual(context_->getSceneManager(), scene_node_));\n\n  visual_bound->setMessage(vertices_);\n  visual_bound->setFramePosition(position_);\n  visual_bound->setFrameOrientation(orientation_);\n\n  Ogre::ColourValue color = bound_color_property_->getOgreColor();\n  visual_bound->setColor(color.r, color.g, color.b, 1.0);\n  float scale = scale_property_->getFloat();\n  visual_bound->setScale(scale);\n\n  visual_bound_ = visual_bound;\n}\n\nvoid PolyhedronArrayDisplay::visualizeVs() {\n  std::shared_ptr<VectorVisual> visual_vector;\n  visual_vector.reset(new VectorVisual(context_->getSceneManager(), scene_node_));\n\n  visual_vector->setMessage(vs_);\n  visual_vector->setFramePosition(position_);\n  visual_vector->setFrameOrientation(orientation_);\n\n  Ogre::ColourValue color = vs_color_property_->getOgreColor();\n  visual_vector->setColor(color.r, color.g, color.b, 1.0);\n\n  visual_vector_ = visual_vector;\n}\n\nvoid PolyhedronArrayDisplay::visualizeMessage(int state) {\n  switch (state) {\n  case 0:\n    visual_bound_ = nullptr;\n    visual_vector_ = nullptr;\n    visualizeMesh();\n    break;\n  case 1:\n    visual_mesh_ = nullptr;\n    visual_vector_ = nullptr;\n    visualizeBound();\n    break;\n  case 2:\n    visual_vector_ = nullptr;\n    visualizeMesh();\n    visualizeBound();\n    break;\n  case 3:\n    visualizeVs();\n    break;\n  default:\n    std::cout << \"Invalid State: \" << state << std::endl;\n  }\n}\n\nvoid PolyhedronArrayDisplay::updateMeshColorAndAlpha() {\n  float alpha = alpha_property_->getFloat();\n  Ogre::ColourValue color = mesh_color_property_->getOgreColor();\n\n  if(visual_mesh_)\n    visual_mesh_->setColor(color.r, color.g, color.b, alpha);\n}\n\nvoid PolyhedronArrayDisplay::updateBoundColorAndAlpha() {\n  Ogre::ColourValue color = bound_color_property_->getOgreColor();\n  if(visual_bound_)\n    visual_bound_->setColor(color.r, color.g, color.b, 1.0);\n}\n\n\nvoid PolyhedronArrayDisplay::updateState() {\n  int state = state_property_->getOptionInt();\n  visualizeMessage(state);\n}\n\nvoid PolyhedronArrayDisplay::updateScale() {\n  float s = scale_property_->getFloat();\n  if(visual_bound_)\n    visual_bound_->setScale(s);\n}\n\nvoid PolyhedronArrayDisplay::updateVsScale() {\n  float s = vs_scale_property_->getFloat();\n  if(visual_vector_)\n    visual_vector_->setScale(s);\n}\n\nvoid PolyhedronArrayDisplay::updateVsColorAndAlpha() {\n  Ogre::ColourValue color = vs_color_property_->getOgreColor();\n  if(visual_vector_)\n    visual_vector_->setColor(color.r, color.g, color.b, 1);\n}\n}\n\n#include <pluginlib/class_list_macros.h>\nPLUGINLIB_EXPORT_CLASS(decomp_rviz_plugins::PolyhedronArrayDisplay, rviz::Display)\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/src/polyhedron_array_display.h",
    "content": "#include <decomp_ros_msgs/PolyhedronArray.h>\n#include <rviz/message_filter_display.h>\n\n#include <rviz/properties/color_property.h>\n#include <rviz/properties/float_property.h>\n#include <rviz/properties/int_property.h>\n#include <rviz/properties/enum_property.h>\n#include <rviz/visualization_manager.h>\n#include <rviz/frame_manager.h>\n#include <OGRE/OgreSceneNode.h>\n#include <OGRE/OgreSceneManager.h>\n\n#include <rviz/load_resource.h>\n\n#include \"mesh_visual.h\"\n#include \"bound_visual.h\"\n#include \"vector_visual.h\"\n#include <decomp_ros_utils/data_ros_utils.h>\n#include <decomp_geometry/geometric_utils.h>\n\nnamespace decomp_rviz_plugins {\nclass PolyhedronArrayDisplay\n    : public rviz::MessageFilterDisplay<decomp_ros_msgs::PolyhedronArray> {\n  Q_OBJECT\npublic:\n  PolyhedronArrayDisplay();\n  virtual ~PolyhedronArrayDisplay();\n\nprotected:\n  virtual void onInitialize();\n\n  virtual void reset();\n\nprivate Q_SLOTS:\n  void updateMeshColorAndAlpha();\n  void updateBoundColorAndAlpha();\n  void updateVsColorAndAlpha();\n  void updateState();\n  void updateScale();\n  void updateVsScale();\n\nprivate:\n  void processMessage(const decomp_ros_msgs::PolyhedronArray::ConstPtr &msg);\n  void visualizeMessage(int state);\n  void visualizeMesh();\n  void visualizeBound();\n  void visualizeVs();\n\n  std::shared_ptr<MeshVisual> visual_mesh_;\n  std::shared_ptr<BoundVisual> visual_bound_;\n  std::shared_ptr<VectorVisual> visual_vector_;\n\n  rviz::ColorProperty *mesh_color_property_;\n  rviz::ColorProperty *bound_color_property_;\n  rviz::ColorProperty *vs_color_property_;\n  rviz::FloatProperty *alpha_property_;\n  rviz::FloatProperty *scale_property_;\n  rviz::FloatProperty *vs_scale_property_;\n  rviz::EnumProperty *state_property_;\n\n  Ogre::Vector3 position_;\n  Ogre::Quaternion orientation_;\n\n  vec_E<vec_Vec3f> vertices_;\n  vec_E<int> poly_ids_;\n  vec_E< vec_E<vec_Vec3f> > bdss_;\n  vec_E<std::pair<Vec3f, Vec3f>> vs_;\n\n};\n\n}\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/src/vector_visual.cpp",
    "content": "#include \"vector_visual.h\"\nnamespace decomp_rviz_plugins {\n  VectorVisual::VectorVisual(Ogre::SceneManager *scene_manager,\n                           Ogre::SceneNode *parent_node) {\n    scene_manager_ = scene_manager;\n    frame_node_ = parent_node->createChildSceneNode();\n  }\n\n  VectorVisual::~VectorVisual() { scene_manager_->destroySceneNode(frame_node_); }\n\n  void VectorVisual::setMessage(const vec_E<std::pair<Vec3f, Vec3f>> &vs) {\n    objs_.clear();\n    vs_ = vs;\n    if (vs.empty())\n      return;\n    objs_.resize(vs.size());\n    for (auto &it : objs_)\n      it.reset(new rviz::Arrow(scene_manager_, frame_node_));\n\n    int cnt = 0;\n    for (const auto &v : vs) {\n      Vec3f n = v.second.normalized();\n      objs_[cnt]->setDirection(Ogre::Vector3(n(0), n(1), n(2)));\n      objs_[cnt]->setPosition(Ogre::Vector3(v.first(0), v.first(1), v.first(2)));\n      objs_[cnt]->setScale(s_ * Ogre::Vector3(1.0, 1.0, 1.0));\n\n      float d = s_ * v.second.norm();\n      float shaft_length = 0.7 * d;\n      float head_length = 0.3 * d;\n      objs_[cnt]->set(shaft_length, d / 8, head_length, d / 5);\n      cnt ++;\n    }\n  }\n\n  void VectorVisual::setFramePosition(const Ogre::Vector3 &position) {\n    frame_node_->setPosition(position);\n  }\n\n  void VectorVisual::setFrameOrientation(const Ogre::Quaternion &orientation) {\n    frame_node_->setOrientation(orientation);\n  }\n\n  void VectorVisual::setColor(float r, float g, float b, float a) {\n    for (auto &it : objs_)\n      it->setColor(r, g, b, a);\n  }\n\n  void VectorVisual::setScale(float s) {\n    s_ = s;\n    for (unsigned int i = 0; i < objs_.size(); i++){\n      float d = s_ * vs_[i].second.norm();\n      float shaft_length = 0.7 * d;\n      float head_length = 0.3 * d;\n      objs_[i]->set(shaft_length, d / 8, head_length, d / 5);\n    }\n  }\n}\n"
  },
  {
    "path": "src/uav_simulator/DecompRos/decomp_ros_utils/src/vector_visual.h",
    "content": "#ifndef VECTOR_VISUAL_H\n#define VECTOR_VISUAL_H\n\n#include <decomp_basis/data_type.h>\n#include <OGRE/OgreVector3.h>\n#include <OGRE/OgreSceneNode.h>\n#include <OGRE/OgreSceneManager.h>\n#include <rviz/ogre_helpers/arrow.h>\n\nnamespace decomp_rviz_plugins {\nclass VectorVisual {\npublic:\n  VectorVisual(Ogre::SceneManager *scene_manager, Ogre::SceneNode *parent_node);\n  ~VectorVisual();\n\n  void setMessage(const vec_E<std::pair<Vec3f, Vec3f>> &vs);\n  void setFramePosition(const Ogre::Vector3 &position);\n  void setFrameOrientation(const Ogre::Quaternion &orientation);\n\n  void setColor(float r, float g, float b, float a);\n  void setScale(float s);\n\nprivate:\n  std::vector<std::unique_ptr<rviz::Arrow>> objs_;\n\n  Ogre::SceneNode *frame_node_;\n\n  Ogre::SceneManager *scene_manager_;\n\n  float s_ = 1.0;\n  vec_E<std::pair<Vec3f, Vec3f>> vs_;\n};\n}\n\n#endif\n"
  },
  {
    "path": "src/uav_simulator/map_generator/CMakeLists.txt",
    "content": "cmake_minimum_required(VERSION 3.0.2)\nproject(map_generator)\n\nadd_compile_options(-std=c++17)\nset(CMAKE_BUILD_TYPE \"Release\")\nset(CMAKE_CXX_FLAGS_RELEASE \"-O3 -Wall\")\n\nfind_package(catkin REQUIRED COMPONENTS\n  roscpp\n  pcl_ros\n  pcl_conversions\n)\n\ncatkin_package(\n  INCLUDE_DIRS ${EIGEN3_INCLUDE_DIR}\n  CATKIN_DEPENDS roscpp pcl_ros pcl_conversions\n)\n\ninclude_directories(\n  # include\n  ${catkin_INCLUDE_DIRS}\n)\n\nadd_executable(${PROJECT_NAME}_node\n  src/${PROJECT_NAME}_node.cpp\n)\n\ntarget_link_libraries(${PROJECT_NAME}_node\n  ${catkin_LIBRARIES}\n  ${PCL_LIBRARIES}\n)"
  },
  {
    "path": "src/uav_simulator/map_generator/config/map.pcd",
    "content": "# .PCD v0.7 - Point Cloud Data file format\nVERSION 0.7\nFIELDS x y z\nSIZE 4 4 4\nTYPE F F F\nCOUNT 1 1 1\nWIDTH 83000\nHEIGHT 1\nVIEWPOINT 0 0 0 1 0 0 0\nPOINTS 83000\nDATA ascii\n3.25123 -6.8488407 0.0062479819\n3.2521319 -6.8389292 0.026684426\n3.2521572 -6.8294196 0.047439754\n3.2513056 -6.8203173 0.068500996\n3.249578 -6.8116288 0.089854993\n3.2469752 -6.803359 0.11148839\n3.2434988 -6.7955132 0.13338767\n3.2391512 -6.788096 0.15553916\n3.2339349 -6.7811122 0.17792898\n3.2278533 -6.7745667 0.20054318\n3.2209101 -6.7684631 0.22336762\n3.2131097 -6.762805 0.246388\n3.204457 -6.757596 0.26958996\n3.1949575 -6.75284 0.292959\n3.1846168 -6.7485394 0.31648052\n3.1734419 -6.7446971 0.34013981\n3.1614392 -6.7413154 0.36392209\n3.1486163 -6.7383962 0.38781247\n3.1349816 -6.7359419 0.41179606\n3.1205435 -6.7339535 0.43585786\n3.1053107 -6.7324324 0.45998281\n3.089293 -6.73138 0.48415583\n3.0725005 -6.7307959 0.50836188\n3.0549433 -6.7306814 0.53258574\n3.0366328 -6.7310362 0.55681229\n3.01758 -6.7318606 0.58102649\n2.9977975 -6.7331529 0.60521305\n2.9772968 -6.7349138 0.62935698\n2.9560914 -6.7371407 0.65344316\n2.9341943 -6.7398334 0.67745644\n2.9116189 -6.7429895 0.70138198\n2.8883801 -6.7466078 0.72520471\n2.8644915 -6.7506852 0.74890977\n2.8399684 -6.7552195 0.77248234\n2.8148265 -6.7602077 0.79590768\n2.7890809 -6.7656469 0.81917119\n2.762748 -6.7715335 0.84225827\n2.7358444 -6.777864 0.86515456\n2.7083867 -6.7846346 0.88784575\n2.680392 -6.791841 0.9103176\n2.6518779 -6.7994781 0.93255609\n2.6228623 -6.8075418 0.95454729\n2.5933633 -6.8160267 0.97627753\n2.5633991 -6.8249278 0.99773318\n2.5329888 -6.8342395 1.0189009\n2.5021513 -6.8439555 1.0397673\n2.4709055 -6.8540707 1.0603195\n2.4392715 -6.8645778 1.0805446\n2.4072685 -6.8754702 1.1004299\n2.374917 -6.8867421 1.119963\n2.342237 -6.8983855 1.1391319\n2.3092487 -6.9103937 1.1579243\n2.2759733 -6.9227586 1.1763287\n2.2424309 -6.935473 1.1943333\n2.2086432 -6.9485292 1.2119272\n2.1746306 -6.9619184 1.2290992\n2.140415 -6.9756322 1.2458386\n2.1060171 -6.9896622 1.262135\n2.0714591 -7.0040002 1.2779782\n2.036762 -7.0186367 1.2933581\n2.0019479 -7.0335622 1.3082654\n1.9670383 -7.048768 1.3226906\n1.9320551 -7.0642447 1.3366247\n1.8970203 -7.0799818 1.350059\n1.8619556 -7.0959706 1.3629853\n1.8268831 -7.1121998 1.3753952\n1.7918246 -7.1286602 1.3872811\n1.756802 -7.1453414 1.3986355\n1.7218372 -7.1622329 1.4094514\n1.686952 -7.1793237 1.419722\n1.6521683 -7.1966038 1.4294409\n1.6175078 -7.2140622 1.438602\n1.5829922 -7.2316875 1.4471995\n1.548643 -7.2494693 1.4552282\n1.5144818 -7.2673965 1.462683\n1.4805298 -7.2854571 1.4695592\n1.4468082 -7.3036408 1.4758525\n1.4133382 -7.3219357 1.481559\n1.3801407 -7.3403301 1.4866751\n1.3472364 -7.3588133 1.4911978\n1.3146459 -7.3773732 1.495124\n1.2823895 -7.3959985 1.4984514\n1.2504874 -7.4146771 1.5011779\n1.2189596 -7.4333973 1.5033017\n1.1878257 -7.4521484 1.5048215\n1.1571052 -7.4709177 1.5057366\n1.1268173 -7.4896936 1.5060461\n1.0969809 -7.5084648 1.5057499\n1.0676148 -7.5272188 1.5048482\n1.0387372 -7.5459447 1.5033417\n1.0103661 -7.5646305 1.5012311\n0.98251933 -7.5832648 1.4985179\n0.95521432 -7.6018353 1.4952037\n0.92846805 -7.6203308 1.4912907\n0.90229732 -7.6387396 1.4867812\n0.8767184 -7.6570501 1.481678\n0.85174733 -7.6752515 1.4759845\n0.82739967 -7.6933312 1.469704\n0.80369067 -7.7112794 1.4628406\n0.78063518 -7.729084 1.4553986\n0.75824761 -7.7467337 1.4473825\n0.73654187 -7.7642179 1.4387974\n0.71553153 -7.7815256 1.4296486\n0.69522983 -7.798646 1.4199419\n0.67564934 -7.8155684 1.4096835\n0.65680242 -7.8322821 1.3988794\n0.63870072 -7.8487768 1.3875368\n0.62135565 -7.8650417 1.3756626\n0.60477793 -7.8810673 1.3632641\n0.58897805 -7.8968434 1.3503492\n0.57396585 -7.9123602 1.336926\n0.55975068 -7.9276075 1.3230028\n0.54634148 -7.9425764 1.3085883\n0.53374654 -7.9572573 1.2936915\n0.52197379 -7.9716406 1.2783219\n0.51103061 -7.9857183 1.2624888\n0.50092375 -7.9994807 1.2462022\n0.49165964 -8.0129194 1.2294725\n0.483244 -8.0260267 1.21231\n0.47568211 -8.0387936 1.1947253\n0.46897867 -8.0512114 1.1767294\n0.46313792 -8.0632744 1.1583338\n0.45816347 -8.0749741 1.1395497\n0.45405841 -8.0863018 1.1203891\n0.45082536 -8.0972528 1.1008639\n0.44846633 -8.1078186 1.0809861\n0.44698274 -8.1179934 1.0607685\n0.44637561 -8.1277704 1.0402234\n0.44664523 -8.1371441 1.0193636\n0.44779146 -8.1461077 0.9982025\n0.4498136 -8.1546555 0.97675306\n0.45271039 -8.1627836 0.95502871\n0.45648 -8.1704855 0.93304306\n0.4611201 -8.1777573 0.91080987\n0.46662775 -8.1845942 0.88834304\n0.47299954 -8.1909924 0.86565655\n0.48023146 -8.1969461 0.84276462\n0.48831901 -8.2024536 0.81968153\n0.49725714 -8.20751 0.79642177\n0.50704026 -8.2121134 0.77299982\n0.51766229 -8.21626 0.7494303\n0.52911651 -8.2199478 0.72572803\n0.54139578 -8.2231741 0.70190775\n0.55449247 -8.2259378 0.67798436\n0.56839842 -8.2282352 0.65397274\n0.58310485 -8.2300663 0.62988806\n0.59860265 -8.2314301 0.60574526\n0.61488205 -8.2323256 0.58155942\n0.63193297 -8.2327518 0.55734575\n0.64974469 -8.232708 0.53311926\n0.66830611 -8.2321949 0.50889516\n0.68760562 -8.2312136 0.48468858\n0.70763117 -8.229763 0.46051466\n0.72837019 -8.2278461 0.43638849\n0.7498098 -8.225462 0.41232514\n0.77193654 -8.2226133 0.38833967\n0.79473656 -8.2193012 0.36444706\n0.8181957 -8.2155285 0.34066224\n0.84229922 -8.211297 0.31700009\n0.86703211 -8.2066097 0.29347539\n0.89237893 -8.2014694 0.27010283\n0.9183237 -8.1958799 0.24689703\n0.94485039 -8.1898432 0.22387248\n0.97194231 -8.1833639 0.20104359\n0.99958253 -8.1764469 0.17842461\n1.0277538 -8.169095 0.15602969\n1.0564386 -8.161314 0.13387281\n1.0856189 -8.1531076 0.11196782\n1.1152763 -8.1444826 0.090328425\n1.1453927 -8.1354427 0.068968132\n1.1759489 -8.1259947 0.0479003\n1.2069259 -8.1161432 0.027138097\n1.2383044 -8.105896 0.006694492\n9.7569227 0.56963092 0.32452253\n9.7380877 0.59135652 0.30086723\n9.7185307 0.61319304 0.27790406\n9.6982641 0.63512677 0.25564739\n9.6773005 0.65714413 0.23411112\n9.655652 0.67923123 0.21330872\n9.6333323 0.70137435 0.19325317\n9.6103563 0.72355962 0.17395703\n9.5867386 0.7457732 0.15543234\n9.5624924 0.7680012 0.13769069\n9.5376339 0.79022968 0.12074316\n9.5121784 0.81244481 0.10460034\n9.4861422 0.83463269 0.08927232\n9.4595404 0.85677946 0.074768685\n9.4323912 0.87887126 0.061098497\n9.4047098 0.90089422 0.048270296\n9.3765154 0.92283469 0.036292098\n9.3478241 0.94467896 0.025171397\n9.318655 0.96641326 0.014915138\n9.2890263 0.98802412 0.0055297306\n8.6300278 1.3743521 0.0056639472\n8.5953493 1.3904412 0.015062452\n8.5606623 1.4061418 0.025331717\n8.5259886 1.4214441 0.036465324\n8.4913502 1.4363385 0.048456315\n8.456769 1.4508158 0.061297197\n8.422266 1.4648669 0.074979946\n8.3878632 1.4784828 0.089496009\n8.3535824 1.4916553 0.10483631\n8.3194437 1.5043762 0.12099127\n8.28547 1.5166372 0.13795078\n8.2516823 1.5284311 0.15570426\n8.2181005 1.53975 0.17424059\n8.1847467 1.5505872 0.1935482\n8.1516409 1.5609359 0.21361503\n8.1188049 1.5707895 0.23442854\n8.0862589 1.5801419 0.25597569\n8.0540228 1.5889872 0.27824306\n8.0221167 1.59732 0.30121669\n7.990561 1.6051351 0.32488227\n7.9593754 1.6124274 0.34922495\n7.9285789 1.6191925 0.37422958\n7.898191 1.6254262 0.3998805\n7.8682303 1.6311245 0.42616168\n7.8387165 1.6362839 0.45305666\n7.8096676 1.6409012 0.48054871\n7.7811012 1.6449735 0.50862062\n7.753036 1.6484982 0.53725475\n7.7254891 1.6514732 0.56643337\n7.6984777 1.6538966 0.59613812\n7.6720185 1.6557667 0.62635052\n7.6461287 1.6570826 0.65705162\n7.6208234 1.6578434 0.68822223\n7.5961199 1.6580485 0.71984291\n7.572032 1.6576979 0.75189394\n7.5485759 1.6567918 0.78435516\n7.5257664 1.6553308 0.81720638\n7.5036168 1.6533158 0.85042709\n7.482141 1.6507479 0.88399643\n7.4613533 1.6476289 0.91789347\n7.4412665 1.6439606 0.95209706\n7.4218926 1.6397455 0.9865858\n7.403244 1.634986 1.0213381\n7.3853326 1.6296853 1.0563323\n7.3681688 1.6238465 1.0915465\n7.3517642 1.6174734 1.1269587\n7.3361287 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2.2605803\n7.2887368 1.1294099 2.2895696\n7.3016801 1.1089785 2.3180084\n7.3154235 1.0883346 2.3458784\n7.3299589 1.0674908 2.3731627\n7.3452773 1.0464605 2.3998439\n7.3613687 1.0252566 2.4259057\n7.3782229 1.0038925 2.4513314\n7.3958302 0.98238158 2.4761052\n7.4141784 0.96073711 2.5002117\n7.4332571 0.93897271 2.5236359\n7.4530544 0.91710198 2.5463631\n7.473557 0.89513856 2.5683789\n7.4947529 0.87309623 2.5896699\n7.5166283 0.85098875 2.6102226\n7.5391698 0.82882988 2.6300242\n7.5623636 0.80663353 2.6490622\n7.5861945 0.78441358 2.6673248\n7.6106482 0.76218385 2.6848006\n7.6357093 0.73995829 2.7014787\n7.6613622 0.71775079 2.7173486\n7.6875901 0.69557524 2.7324004\n7.7143779 0.6734454 2.7466247\n7.7417078 0.65137523 2.7600126\n7.7695637 0.62937844 2.7725558\n7.7979269 0.60746878 2.7842464\n7.8267808 0.58565998 2.7950773\n7.8561068 0.56396568 2.8050413\n7.8858867 0.54239941 2.8141325\n7.9161019 0.52097464 2.8223453\n7.946734 0.49970478 2.8296742\n7.9777627 0.47860309 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2.401\n9.151 5.151 2.451\n9.151 5.151 2.5009999\n9.151 5.151 2.5510001\n9.151 5.151 2.6010001\n9.151 5.151 2.651\n9.151 5.151 2.701\n9.151 5.151 2.7509999\n9.151 5.151 2.8010001\n9.151 5.151 2.8510001\n9.151 5.151 2.901\n9.151 5.151 2.951\n9.2510004 4.5510001 0.001\n9.2510004 4.5510001 0.050999999\n9.2510004 4.5510001 0.101\n9.2510004 4.5510001 0.15099999\n9.2510004 4.5510001 0.20100001\n9.2510004 4.5510001 0.25099999\n9.2510004 4.5510001 0.301\n9.2510004 4.5510001 0.35100001\n9.2510004 4.5510001 0.40099999\n9.2510004 4.5510001 0.45100001\n9.2510004 4.5510001 0.50099999\n9.2510004 4.5510001 0.551\n9.2510004 4.5510001 0.60100001\n9.2510004 4.5510001 0.65100002\n9.2510004 4.5510001 0.70099998\n9.2510004 4.5510001 0.75099999\n9.2510004 4.5510001 0.801\n9.2510004 4.5510001 0.85100001\n9.2510004 4.5510001 0.90100002\n9.2510004 4.5510001 0.95099998\n9.2510004 4.5510001 1.001\n9.2510004 4.5510001 1.051\n9.2510004 4.5510001 1.101\n9.2510004 4.5510001 1.151\n9.2510004 4.5510001 1.201\n9.2510004 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2.3010001\n4.7509999 9.4510002 2.3510001\n4.8509998 9.151 0.001\n4.8509998 9.151 0.050999999\n4.8509998 9.151 0.101\n4.8509998 9.151 0.15099999\n4.8509998 9.151 0.20100001\n4.8509998 9.151 0.25099999\n4.8509998 9.151 0.301\n4.8509998 9.151 0.35100001\n4.8509998 9.151 0.40099999\n4.8509998 9.151 0.45100001\n4.8509998 9.151 0.50099999\n4.8509998 9.151 0.551\n4.8509998 9.151 0.60100001\n4.8509998 9.151 0.65100002\n4.8509998 9.151 0.70099998\n4.8509998 9.151 0.75099999\n4.8509998 9.151 0.801\n4.8509998 9.151 0.85100001\n4.8509998 9.151 0.90100002\n4.8509998 9.151 0.95099998\n4.8509998 9.151 1.001\n4.8509998 9.151 1.051\n4.8509998 9.151 1.101\n4.8509998 9.151 1.151\n4.8509998 9.151 1.201\n4.8509998 9.151 1.251\n4.8509998 9.151 1.301\n4.8509998 9.151 1.351\n4.8509998 9.151 1.401\n4.8509998 9.151 1.451\n4.8509998 9.151 1.501\n4.8509998 9.151 1.551\n4.8509998 9.151 1.601\n4.8509998 9.151 1.651\n4.8509998 9.151 1.701\n4.8509998 9.151 1.751\n4.8509998 9.151 1.801\n4.8509998 9.151 1.851\n4.8509998 9.151 1.901\n4.8509998 9.151 1.951\n4.8509998 9.151 2.0009999\n4.8509998 9.151 2.0510001\n4.8509998 9.151 2.1010001\n4.8509998 9.151 2.151\n4.8509998 9.151 2.201\n4.8509998 9.151 2.2509999\n4.8509998 9.151 2.3010001\n4.8509998 9.151 2.3510001\n4.8509998 9.151 2.401\n4.8509998 9.151 2.451\n4.8509998 9.2510004 0.001\n4.8509998 9.2510004 0.050999999\n4.8509998 9.2510004 0.101\n4.8509998 9.2510004 0.15099999\n4.8509998 9.2510004 0.20100001\n4.8509998 9.2510004 0.25099999\n4.8509998 9.2510004 0.301\n4.8509998 9.2510004 0.35100001\n4.8509998 9.2510004 0.40099999\n4.8509998 9.2510004 0.45100001\n4.8509998 9.2510004 0.50099999\n4.8509998 9.2510004 0.551\n4.8509998 9.2510004 0.60100001\n4.8509998 9.2510004 0.65100002\n4.8509998 9.2510004 0.70099998\n4.8509998 9.2510004 0.75099999\n4.8509998 9.2510004 0.801\n4.8509998 9.2510004 0.85100001\n4.8509998 9.2510004 0.90100002\n4.8509998 9.2510004 0.95099998\n4.8509998 9.2510004 1.001\n4.8509998 9.2510004 1.051\n4.8509998 9.2510004 1.101\n4.8509998 9.2510004 1.151\n4.8509998 9.2510004 1.201\n4.8509998 9.2510004 1.251\n4.8509998 9.2510004 1.301\n4.8509998 9.2510004 1.351\n4.8509998 9.2510004 1.401\n4.8509998 9.2510004 1.451\n4.8509998 9.2510004 1.501\n4.8509998 9.2510004 1.551\n4.8509998 9.2510004 1.601\n4.8509998 9.2510004 1.651\n4.8509998 9.2510004 1.701\n4.8509998 9.2510004 1.751\n4.8509998 9.3509998 0.001\n4.8509998 9.3509998 0.050999999\n4.8509998 9.3509998 0.101\n4.8509998 9.3509998 0.15099999\n4.8509998 9.3509998 0.20100001\n4.8509998 9.3509998 0.25099999\n4.8509998 9.3509998 0.301\n4.8509998 9.3509998 0.35100001\n4.8509998 9.3509998 0.40099999\n4.8509998 9.3509998 0.45100001\n4.8509998 9.3509998 0.50099999\n4.8509998 9.3509998 0.551\n4.8509998 9.3509998 0.60100001\n4.8509998 9.3509998 0.65100002\n4.8509998 9.3509998 0.70099998\n4.8509998 9.3509998 0.75099999\n4.8509998 9.3509998 0.801\n4.8509998 9.3509998 0.85100001\n4.8509998 9.3509998 0.90100002\n4.8509998 9.3509998 0.95099998\n4.8509998 9.3509998 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0.65100002\n4.8509998 9.4510002 0.70099998\n4.8509998 9.4510002 0.75099999\n4.8509998 9.4510002 0.801\n4.8509998 9.4510002 0.85100001\n4.8509998 9.4510002 0.90100002\n4.8509998 9.4510002 0.95099998\n4.8509998 9.4510002 1.001\n4.8509998 9.4510002 1.051\n4.8509998 9.4510002 1.101\n4.8509998 9.4510002 1.151\n4.8509998 9.4510002 1.201\n4.8509998 9.4510002 1.251\n4.9510002 9.151 0.001\n4.9510002 9.151 0.050999999\n4.9510002 9.151 0.101\n4.9510002 9.151 0.15099999\n4.9510002 9.151 0.20100001\n4.9510002 9.151 0.25099999\n4.9510002 9.151 0.301\n4.9510002 9.151 0.35100001\n4.9510002 9.151 0.40099999\n4.9510002 9.151 0.45100001\n4.9510002 9.151 0.50099999\n4.9510002 9.151 0.551\n4.9510002 9.151 0.60100001\n4.9510002 9.151 0.65100002\n4.9510002 9.151 0.70099998\n4.9510002 9.151 0.75099999\n4.9510002 9.151 0.801\n4.9510002 9.151 0.85100001\n4.9510002 9.151 0.90100002\n4.9510002 9.151 0.95099998\n4.9510002 9.151 1.001\n4.9510002 9.151 1.051\n4.9510002 9.151 1.101\n4.9510002 9.151 1.151\n4.9510002 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9.2510004 0.20100001\n4.9510002 9.2510004 0.25099999\n4.9510002 9.2510004 0.301\n4.9510002 9.2510004 0.35100001\n4.9510002 9.2510004 0.40099999\n4.9510002 9.2510004 0.45100001\n4.9510002 9.2510004 0.50099999\n4.9510002 9.2510004 0.551\n4.9510002 9.2510004 0.60100001\n4.9510002 9.2510004 0.65100002\n4.9510002 9.2510004 0.70099998\n4.9510002 9.2510004 0.75099999\n4.9510002 9.2510004 0.801\n4.9510002 9.2510004 0.85100001\n4.9510002 9.2510004 0.90100002\n4.9510002 9.2510004 0.95099998\n4.9510002 9.2510004 1.001\n4.9510002 9.2510004 1.051\n4.9510002 9.2510004 1.101\n4.9510002 9.2510004 1.151\n4.9510002 9.2510004 1.201\n4.9510002 9.2510004 1.251\n4.9510002 9.2510004 1.301\n4.9510002 9.2510004 1.351\n4.9510002 9.2510004 1.401\n4.9510002 9.2510004 1.451\n4.9510002 9.2510004 1.501\n4.9510002 9.2510004 1.551\n4.9510002 9.2510004 1.601\n4.9510002 9.2510004 1.651\n4.9510002 9.2510004 1.701\n4.9510002 9.2510004 1.751\n4.9510002 9.2510004 1.801\n4.9510002 9.2510004 1.851\n4.9510002 9.2510004 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0.60100001\n1.951 9.4510002 0.65100002\n1.951 9.4510002 0.70099998\n1.951 9.4510002 0.75099999\n1.951 9.4510002 0.801\n1.951 9.4510002 0.85100001\n1.951 9.4510002 0.90100002\n1.951 9.4510002 0.95099998\n1.951 9.4510002 1.001\n1.951 9.4510002 1.051\n1.951 9.4510002 1.101\n1.951 9.4510002 1.151\n1.951 9.4510002 1.201\n1.951 9.4510002 1.251\n1.951 9.4510002 1.301\n1.951 9.4510002 1.351\n1.951 9.4510002 1.401\n1.951 9.4510002 1.451\n1.951 9.4510002 1.501\n1.951 9.4510002 1.551\n1.951 9.4510002 1.601\n1.951 9.4510002 1.651\n1.951 9.4510002 1.701\n1.951 9.4510002 1.751\n1.951 9.4510002 1.801\n1.951 9.4510002 1.851\n1.951 9.4510002 1.901\n1.951 9.4510002 1.951\n2.0510001 9.151 0.001\n2.0510001 9.151 0.050999999\n2.0510001 9.151 0.101\n2.0510001 9.151 0.15099999\n2.0510001 9.151 0.20100001\n2.0510001 9.151 0.25099999\n2.0510001 9.151 0.301\n2.0510001 9.151 0.35100001\n2.0510001 9.151 0.40099999\n2.0510001 9.151 0.45100001\n2.0510001 9.151 0.50099999\n2.0510001 9.151 0.551\n2.0510001 9.151 0.60100001\n2.0510001 9.151 0.65100002\n2.0510001 9.151 0.70099998\n2.0510001 9.151 0.75099999\n2.0510001 9.151 0.801\n2.0510001 9.151 0.85100001\n2.0510001 9.151 0.90100002\n2.0510001 9.151 0.95099998\n2.0510001 9.151 1.001\n2.0510001 9.151 1.051\n2.0510001 9.151 1.101\n2.0510001 9.151 1.151\n2.0510001 9.151 1.201\n2.0510001 9.151 1.251\n2.0510001 9.151 1.301\n2.0510001 9.151 1.351\n2.0510001 9.151 1.401\n2.0510001 9.151 1.451\n2.0510001 9.151 1.501\n2.0510001 9.151 1.551\n2.0510001 9.151 1.601\n2.0510001 9.151 1.651\n2.0510001 9.151 1.701\n2.0510001 9.151 1.751\n2.0510001 9.151 1.801\n2.0510001 9.151 1.851\n2.0510001 9.151 1.901\n2.0510001 9.151 1.951\n2.0510001 9.151 2.0009999\n2.0510001 9.151 2.0510001\n2.0510001 9.151 2.1010001\n2.0510001 9.151 2.151\n2.0510001 9.151 2.201\n2.0510001 9.151 2.2509999\n2.0510001 9.151 2.3010001\n2.0510001 9.151 2.3510001\n2.0510001 9.151 2.401\n2.0510001 9.151 2.451\n2.0510001 9.2510004 0.001\n2.0510001 9.2510004 0.050999999\n2.0510001 9.2510004 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1.801\n2.0510001 9.2510004 1.851\n2.0510001 9.2510004 1.901\n2.0510001 9.2510004 1.951\n2.0510001 9.2510004 2.0009999\n2.0510001 9.2510004 2.0510001\n2.0510001 9.2510004 2.1010001\n2.0510001 9.2510004 2.151\n2.0510001 9.2510004 2.201\n2.0510001 9.2510004 2.2509999\n2.0510001 9.2510004 2.3010001\n2.0510001 9.2510004 2.3510001\n2.0510001 9.2510004 2.401\n2.0510001 9.2510004 2.451\n2.0510001 9.2510004 2.5009999\n2.0510001 9.2510004 2.5510001\n2.0510001 9.2510004 2.6010001\n2.0510001 9.2510004 2.651\n2.0510001 9.3509998 0.001\n2.0510001 9.3509998 0.050999999\n2.0510001 9.3509998 0.101\n2.0510001 9.3509998 0.15099999\n2.0510001 9.3509998 0.20100001\n2.0510001 9.3509998 0.25099999\n2.0510001 9.3509998 0.301\n2.0510001 9.3509998 0.35100001\n2.0510001 9.3509998 0.40099999\n2.0510001 9.3509998 0.45100001\n2.0510001 9.3509998 0.50099999\n2.0510001 9.3509998 0.551\n2.0510001 9.3509998 0.60100001\n2.0510001 9.3509998 0.65100002\n2.0510001 9.3509998 0.70099998\n2.0510001 9.3509998 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2.901\n2.0510001 9.4510002 2.951\n2.151 9.151 0.001\n2.151 9.151 0.050999999\n2.151 9.151 0.101\n2.151 9.151 0.15099999\n2.151 9.151 0.20100001\n2.151 9.151 0.25099999\n2.151 9.151 0.301\n2.151 9.151 0.35100001\n2.151 9.151 0.40099999\n2.151 9.151 0.45100001\n2.151 9.151 0.50099999\n2.151 9.151 0.551\n2.151 9.151 0.60100001\n2.151 9.151 0.65100002\n2.151 9.151 0.70099998\n2.151 9.151 0.75099999\n2.151 9.151 0.801\n2.151 9.151 0.85100001\n2.151 9.151 0.90100002\n2.151 9.151 0.95099998\n2.151 9.151 1.001\n2.151 9.151 1.051\n2.151 9.151 1.101\n2.151 9.151 1.151\n2.151 9.151 1.201\n2.151 9.151 1.251\n2.151 9.151 1.301\n2.151 9.151 1.351\n2.151 9.151 1.401\n2.151 9.151 1.451\n2.151 9.151 1.501\n2.151 9.151 1.551\n2.151 9.151 1.601\n2.151 9.151 1.651\n2.151 9.151 1.701\n2.151 9.151 1.751\n2.151 9.151 1.801\n2.151 9.151 1.851\n2.151 9.151 1.901\n2.151 9.151 1.951\n2.151 9.151 2.0009999\n2.151 9.151 2.0510001\n2.151 9.2510004 0.001\n2.151 9.2510004 0.050999999\n2.151 9.2510004 0.101\n2.151 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-5.4489999 2.3010001\n-5.7490001 -5.4489999 2.3510001\n-5.7490001 -5.4489999 2.401\n-5.7490001 -5.4489999 2.451\n-5.7490001 -5.349 0.001\n-5.7490001 -5.349 0.050999999\n-5.7490001 -5.349 0.101\n-5.7490001 -5.349 0.15099999\n-5.7490001 -5.349 0.20100001\n-5.7490001 -5.349 0.25099999\n-5.7490001 -5.349 0.301\n-5.7490001 -5.349 0.35100001\n-5.7490001 -5.349 0.40099999\n-5.7490001 -5.349 0.45100001\n-5.7490001 -5.349 0.50099999\n-5.7490001 -5.349 0.551\n-5.7490001 -5.349 0.60100001\n-5.7490001 -5.349 0.65100002\n-5.7490001 -5.349 0.70099998\n-5.7490001 -5.349 0.75099999\n-5.7490001 -5.349 0.801\n-5.7490001 -5.349 0.85100001\n-5.7490001 -5.349 0.90100002\n-5.7490001 -5.349 0.95099998\n-5.7490001 -5.349 1.001\n-5.7490001 -5.349 1.051\n-5.7490001 -5.349 1.101\n-5.7490001 -5.349 1.151\n-5.7490001 -5.349 1.201\n-5.7490001 -5.349 1.251\n-5.7490001 -5.349 1.301\n-5.7490001 -5.349 1.351\n-5.7490001 -5.349 1.401\n-5.7490001 -5.349 1.451\n-5.7490001 -5.349 1.501\n-5.7490001 -5.349 1.551\n-5.7490001 -5.349 1.601\n-5.7490001 -5.349 1.651\n-5.7490001 -5.349 1.701\n-5.7490001 -5.349 1.751\n-5.7490001 -5.349 1.801\n-5.7490001 -5.349 1.851\n-5.7490001 -5.349 1.901\n-5.7490001 -5.349 1.951\n-5.7490001 -5.349 2.0009999\n-5.7490001 -5.349 2.0510001\n-5.7490001 -5.349 2.1010001\n-5.7490001 -5.349 2.151\n-5.7490001 -5.349 2.201\n-5.7490001 -5.349 2.2509999\n-5.7490001 -5.349 2.3010001\n-5.7490001 -5.349 2.3510001\n-5.7490001 -5.349 2.401\n-5.7490001 -5.349 2.451\n-5.7490001 -5.349 2.5009999\n-5.7490001 -5.349 2.5510001\n-5.7490001 -5.349 2.6010001\n-5.7490001 -5.349 2.651\n"
  },
  {
    "path": "src/uav_simulator/map_generator/launch/map_generator.launch",
    "content": "<launch>\n<node pkg =\"map_generator\" name =\"map_generator_node\" type =\"map_generator_node\" output = \"screen\">      \n  <param name=\"sensing/rate\"   value=\"2\"/>\n  <param name=\"map/file_name\"   value=\"map.pcd\"/>\n</node>\n</launch>\n"
  },
  {
    "path": "src/uav_simulator/map_generator/package.xml",
    "content": "<?xml version=\"1.0\"?>\n<package format=\"2\">\n  <name>map_generator</name>\n  <version>0.0.0</version>\n  <description>The map_generator package</description>\n\n  <maintainer email=\"jlji@zju.edu.cn\">soviet</maintainer>\n\n  <license>GPLv3</license>\n\n  <buildtool_depend>catkin</buildtool_depend>\n  <depend>roscpp</depend>\n  <depend>pcl_ros</depend>\n  <depend>pcl_conversions</depend>\n\n  <export>\n  </export>\n</package>\n"
  },
  {
    "path": "src/uav_simulator/map_generator/src/map_generator_node.cpp",
    "content": "#include <iostream>\n#include <pcl/io/pcd_io.h>\n#include <pcl_conversions/pcl_conversions.h>\n#include <pcl/point_cloud.h>\n\n#include <ros/ros.h>\n#include <sensor_msgs/PointCloud2.h>\n#include <ros/package.h>\n\nint main (int argc, char** argv) \n{        \n   ros::init (argc, argv, \"map_generator_node\");\n   ros::NodeHandle n( \"~\" );\n\n   double sense_rate;\n   std::string map_file_name;\n   n.param(\"sensing/rate\", sense_rate, 1.0);\n   n.param(\"map/file_name\", map_file_name, std::string(\"\"));\n   std::string map_path = ros::package::getPath(\"map_generator\")+\"/config/\"+map_file_name.c_str();\n\n   ros::Publisher map_pub = n.advertise<sensor_msgs::PointCloud2>(\"global_map\", 1);\n\n   sensor_msgs::PointCloud2 global_cloud_msg;\n   pcl::PointCloud<pcl::PointXYZ> global_cloud;\n   if (pcl::io::loadPCDFile<pcl::PointXYZ>(map_path, global_cloud) == 0 ){\n      pcl::toROSMsg(global_cloud, global_cloud_msg);\n      global_cloud_msg.header.frame_id = \"world\";\n   }\n\n   ros::Rate loop_rate(sense_rate);\n   while (ros::ok())\n   {\n      map_pub.publish(global_cloud_msg);\n      ros::spinOnce();\n      loop_rate.sleep();\n   }\n}"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/CMakeLists.txt",
    "content": "cmake_minimum_required(VERSION 2.8.3)\nproject(quadrotor_msgs)\n\nset(CMAKE_BUILD_TYPE \"Release\")\nset(CMAKE_CXX_FLAGS_RELEASE \"-O3 -w -g -Wno-deprecated -fPIC\")\nfind_package(catkin REQUIRED COMPONENTS\n  #armadillo\n  roscpp\n  nav_msgs\n  geometry_msgs\n  message_generation\n#  cmake_utils\n)\n\ninclude_directories(\n    ${catkin_INCLUDE_DIRS}\n    include\n    )\n\n\nadd_message_files(\n  FILES\n  AuxCommand.msg\n  Corrections.msg\n  Gains.msg\n  OutputData.msg\n  PositionCommand.msg\n  PPROutputData.msg\n  Serial.msg\n  SO3Command.msg\n  StatusData.msg\n  TRPYCommand.msg\n  Odometry.msg\n  PolynomialTrajectory.msg\n  OptimalTimeAllocator.msg\n  TrajectoryMatrix.msg\n  ReplanCheck.msg\n  SpatialTemporalTrajectory.msg\n  Bspline.msg\n  SwarmCommand.msg\n  Replan.msg\n  SwarmOdometry.msg\n  SwarmInfo.msg\n)\n\ngenerate_messages(\n    DEPENDENCIES\n    geometry_msgs\n    nav_msgs\n)\n\ncatkin_package(\n  #INCLUDE_DIRS include\n  LIBRARIES encode_msgs decode_msgs\n  #CATKIN_DEPENDS geometry_msgs nav_msgs\n  #DEPENDS system_lib\n  CATKIN_DEPENDS message_runtime\n)\n\n# add_executable(test_exe src/test_exe.cpp)\nadd_library(decode_msgs src/decode_msgs.cpp)\nadd_library(encode_msgs src/encode_msgs.cpp)\n\n# add_dependencies(test_exe quadrotor_msgs_generate_messages_cpp)\nadd_dependencies(encode_msgs quadrotor_msgs_generate_messages_cpp)\nadd_dependencies(decode_msgs quadrotor_msgs_generate_messages_cpp)\n\nfind_package(Eigen3 REQUIRED)\ninclude_directories(${EIGEN3_INCLUDE_DIR})\n\n\n# target_link_libraries(test_exe \n#     decode_msgs \n#     encode_msgs\n# )\n\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/include/quadrotor_msgs/comm_types.h",
    "content": "#ifndef __QUADROTOR_MSGS_COMM_TYPES_H__\n#define __QUADROTOR_MSGS_COMM_TYPES_H__\n\n#define TYPE_SO3_CMD 's'\nstruct SO3_CMD_INPUT\n{\n  // Scaling factors when decoding\n  int16_t force[3]; // /500\n  int8_t des_qx, des_qy, des_qz, des_qw; // /125\n  uint8_t kR[3]; // /50\n  uint8_t kOm[3]; // /100\n  int16_t cur_yaw; // /1e4\n  int16_t kf_correction; // /1e11;\n  uint8_t angle_corrections[2]; // roll,pitch /2500\n  uint8_t enable_motors:1;\n  uint8_t use_external_yaw:1;\n  uint8_t seq;\n};\n\n#define TYPE_STATUS_DATA 'c'\nstruct STATUS_DATA\n{\n  uint16_t loop_rate;\n  uint16_t voltage;\n  uint8_t seq;\n};\n\n#define TYPE_OUTPUT_DATA 'd'\nstruct OUTPUT_DATA\n{\n  uint16_t loop_rate;\n  uint16_t voltage;\n  int16_t roll, pitch, yaw;\n  int16_t ang_vel[3];\n  int16_t acc[3];\n  int16_t dheight;\n  int32_t height;\n  int16_t mag[3];\n  uint8_t radio[8];\n  //uint8_t rpm[4];\n  uint8_t seq;\n};\n\n#define TYPE_TRPY_CMD 'p'\nstruct TRPY_CMD\n{\n  int16_t thrust;\n  int16_t roll;\n  int16_t pitch;\n  int16_t yaw;\n  int16_t current_yaw;\n  uint8_t enable_motors:1;\n  uint8_t use_external_yaw:1;\n};\n\n#define TYPE_PPR_OUTPUT_DATA 't'\nstruct PPR_OUTPUT_DATA\n{\n  uint16_t time;\n  int16_t des_thrust;\n  int16_t des_roll;\n  int16_t des_pitch;\n  int16_t des_yaw;\n  int16_t est_roll;\n  int16_t est_pitch;\n  int16_t est_yaw;\n  int16_t est_angvel_x;\n  int16_t est_angvel_y;\n  int16_t est_angvel_z;\n  int16_t est_acc_x;\n  int16_t est_acc_y;\n  int16_t est_acc_z;\n  uint16_t pwm1;\n  uint16_t pwm2;\n  uint16_t pwm3;\n  uint16_t pwm4;\n};\n\n#define TYPE_PPR_GAINS 'g'\nstruct PPR_GAINS\n{\n  int16_t Kp;\n  int16_t Kd;\n  int16_t Kp_yaw;\n  int16_t Kd_yaw;\n};\n#endif\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/include/quadrotor_msgs/decode_msgs.h",
    "content": "#ifndef __QUADROTOR_MSGS_QUADROTOR_MSGS_H__\n#define __QUADROTOR_MSGS_QUADROTOR_MSGS_H__\n\n#include <stdint.h>\n#include <vector>\n#include <quadrotor_msgs/OutputData.h>\n#include <quadrotor_msgs/StatusData.h>\n#include <quadrotor_msgs/PPROutputData.h>\n\nnamespace quadrotor_msgs\n{\n\nbool decodeOutputData(const std::vector<uint8_t> &data,\n                      quadrotor_msgs::OutputData &output);\n\nbool decodeStatusData(const std::vector<uint8_t> &data,\n                      quadrotor_msgs::StatusData &status);\n\nbool decodePPROutputData(const std::vector<uint8_t> &data,\n                         quadrotor_msgs::PPROutputData &output);\n}\n\n#endif\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/include/quadrotor_msgs/encode_msgs.h",
    "content": "#ifndef __QUADROTOR_MSGS_QUADROTOR_MSGS_H__\n#define __QUADROTOR_MSGS_QUADROTOR_MSGS_H__\n\n#include <stdint.h>\n#include <vector>\n#include <quadrotor_msgs/SO3Command.h>\n#include <quadrotor_msgs/TRPYCommand.h>\n#include <quadrotor_msgs/Gains.h>\n\nnamespace quadrotor_msgs\n{\n\nvoid encodeSO3Command(const quadrotor_msgs::SO3Command &so3_command,\n                      std::vector<uint8_t> &output);\nvoid encodeTRPYCommand(const quadrotor_msgs::TRPYCommand &trpy_command,\n                       std::vector<uint8_t> &output);\n\nvoid encodePPRGains(const quadrotor_msgs::Gains &gains,\n                    std::vector<uint8_t> &output);\n}\n\n#endif\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/AuxCommand.msg",
    "content": "float64 current_yaw\nfloat64 kf_correction\nfloat64[2] angle_corrections# Trims for roll, pitch\nbool enable_motors\nbool use_external_yaw\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/Bspline.msg",
    "content": "# the action command for trajectory server.\nuint32 ACTION_ADD                  =   1\nuint32 ACTION_ABORT                =   2\nuint32 ACTION_WARN_START           =   3\nuint32 ACTION_WARN_FINAL           =   4\nuint32 ACTION_WARN_IMPOSSIBLE      =   5\nuint32 action\n\nint32 order\nint64 traj_id\nfloat64[] knots\ngeometry_msgs/Point[] pts\ntime start_time\nfloat64 time_extra\nfloat64 replan_to_global_time"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/Corrections.msg",
    "content": "float64 kf_correction\nfloat64[2] angle_corrections\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/Gains.msg",
    "content": "float64 Kp\nfloat64 Kd\nfloat64 Kp_yaw\nfloat64 Kd_yaw\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/Odometry.msg",
    "content": "uint8 STATUS_ODOM_VALID=0\nuint8 STATUS_ODOM_INVALID=1\nuint8 STATUS_ODOM_LOOPCLOSURE=2\n\nnav_msgs/Odometry curodom\nnav_msgs/Odometry kfodom\nuint32 kfid\nuint8 status\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/OptimalTimeAllocator.msg",
    "content": "Header header\n\ntime start_time\ntime final_time\n\n# the trajectory id, starts from \"1\".\nuint32 trajectory_id\n\n# the action command for trajectory server.\nuint32 ACTION_ADD                  =   1\nuint32 ACTION_ABORT                =   2\nuint32 ACTION_WARN_START           =   3\nuint32 ACTION_WARN_FINAL           =   4\nuint32 ACTION_WARN_IMPOSSIBLE      =   5\nuint32 action\n\n# the vector of all 'K' number of each piece of the time profile.\nint32[] K\nint32   K_max\n\n# the a, b, c, d parameters of the TOPP time profile.\nfloat64[] a\nfloat64[] b\n\n# useful variables for evaluating time\nfloat64[] s\nfloat64[] time\nfloat64[] time_acc\n\n# delta_s in s domain\nfloat64   s_step\n\nstring debug_info"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/OutputData.msg",
    "content": "Header header\nuint16 loop_rate\nfloat64 voltage\ngeometry_msgs/Quaternion orientation\ngeometry_msgs/Vector3 angular_velocity\ngeometry_msgs/Vector3 linear_acceleration\nfloat64 pressure_dheight\nfloat64 pressure_height\ngeometry_msgs/Vector3 magnetic_field\nuint8[8] radio_channel\n#uint8[4] motor_rpm\nuint8 seq\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/PPROutputData.msg",
    "content": "Header header\nuint16 quad_time\nfloat64 des_thrust\nfloat64 des_roll\nfloat64 des_pitch\nfloat64 des_yaw\nfloat64 est_roll\nfloat64 est_pitch\nfloat64 est_yaw\nfloat64 est_angvel_x\nfloat64 est_angvel_y\nfloat64 est_angvel_z\nfloat64 est_acc_x\nfloat64 est_acc_y\nfloat64 est_acc_z\nuint16[4] pwm\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/PolynomialTrajectory.msg",
    "content": "Header header\n\n# the trajectory id, starts from \"1\".\nuint32 trajectory_id\n\n# the action command for trajectory server.\nuint32 ACTION_ADD                  =   1\nuint32 ACTION_ABORT                =   2\nuint32 ACTION_WARN_START           =   3\nuint32 ACTION_WARN_FINAL           =   4\nuint32 ACTION_WARN_IMPOSSIBLE      =   5\nuint32 action\n\n# the order of trajectory.\nuint32 num_order\nuint32 num_segment\n\n# the polynomial coecfficients of the trajectory.\nfloat64 start_yaw\nfloat64 final_yaw\nfloat64[] coef_x\nfloat64[] coef_y\nfloat64[] coef_z\nfloat64[] time\nfloat64   mag_coeff\nuint32[]  order\n\nstring debug_info"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/PositionCommand.msg",
    "content": "Header header\ngeometry_msgs/Point position\ngeometry_msgs/Vector3 velocity\ngeometry_msgs/Vector3 acceleration\nfloat64 yaw\nfloat64 yaw_dot\nfloat64[3] kx\nfloat64[3] kv \n\nuint32 trajectory_id\n\nuint8 TRAJECTORY_STATUS_EMPTY = 0\nuint8 TRAJECTORY_STATUS_READY = 1\nuint8 TRAJECTORY_STATUS_COMPLETED = 3\nuint8 TRAJECTROY_STATUS_ABORT = 4\nuint8 TRAJECTORY_STATUS_ILLEGAL_START = 5\nuint8 TRAJECTORY_STATUS_ILLEGAL_FINAL = 6\nuint8 TRAJECTORY_STATUS_IMPOSSIBLE = 7\n\n# Its ID number will start from 1, allowing you comparing it with 0.\nuint8 trajectory_flag\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/PositionCommand_back.msg",
    "content": "Header header\ngeometry_msgs/Point position\ngeometry_msgs/Vector3 velocity\ngeometry_msgs/Vector3 acceleration\nfloat64 yaw\nfloat64 yaw_dot\nfloat64[3] kx\nfloat64[3] kv \n\nuint32 trajectory_id\n\nuint8 TRAJECTORY_STATUS_EMPTY = 0\nuint8 TRAJECTORY_STATUS_READY = 1\nuint8 TRAJECTORY_STATUS_COMPLETED = 3\nuint8 TRAJECTROY_STATUS_ABORT = 4\nuint8 TRAJECTORY_STATUS_ILLEGAL_START = 5\nuint8 TRAJECTORY_STATUS_ILLEGAL_FINAL = 6\nuint8 TRAJECTORY_STATUS_IMPOSSIBLE = 7\n\n# Its ID number will start from 1, allowing you comparing it with 0.\nuint8 trajectory_flag\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/Replan.msg",
    "content": "#data structure\ngeometry_msgs/Vector3 start_velocity\ngeometry_msgs/Vector3 start_acceleration\nnav_msgs/Path         plan\ngeometry_msgs/Vector3 stop_velocity\ngeometry_msgs/Vector3 stop_acceleration\nfloat64               replan_time\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/ReplanCheck.msg",
    "content": "Header header\ngeometry_msgs/Vector3 start_velocity\ngeometry_msgs/Vector3 start_acceleration\ngeometry_msgs/Point[] plan_points\ngeometry_msgs/Point[] check_points\ngeometry_msgs/Vector3 stop_velocity\ngeometry_msgs/Vector3 stop_acceleration\nfloat64               replan_time_length\nfloat64               check_points_time_interval\nfloat64               plan_points_time_interval\nuint32                trajectory_id\nfloat64               replan_to_global_time\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/SO3Command.msg",
    "content": "Header header\ngeometry_msgs/Vector3 force\ngeometry_msgs/Quaternion orientation\nfloat64[3] kR\nfloat64[3] kOm\nquadrotor_msgs/AuxCommand aux\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/Serial.msg",
    "content": "# Note: These constants need to be kept in sync with the types\n# defined in include/quadrotor_msgs/comm_types.h\nuint8 SO3_CMD = 115 # 's' in base 10\nuint8 TRPY_CMD = 112 # 'p' in base 10\nuint8 STATUS_DATA = 99 # 'c' in base 10\nuint8 OUTPUT_DATA = 100 # 'd' in base 10\nuint8 PPR_OUTPUT_DATA = 116 # 't' in base 10\nuint8 PPR_GAINS = 103 # 'g'\n\nHeader header\nuint8 channel\nuint8 type # One of the types listed above\nuint8[] data\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/SpatialTemporalTrajectory.msg",
    "content": "Header header\n\ntime start_time\ntime final_time\n\n# the trajectory id, starts from \"1\".\nuint32 trajectory_id\n\n# the action command for trajectory server.\nuint32 ACTION_ADD                  =   1\nuint32 ACTION_ABORT                =   2\nuint32 ACTION_WARN_START           =   3\nuint32 ACTION_WARN_FINAL           =   4\nuint32 ACTION_WARN_IMPOSSIBLE      =   5\nuint32 action\n\n# the vector of all 'K' number of each piece of the time profile.\nint32[] K\nint32   K_max\n\n# the a, b, c, d parameters of the TOPP time profile.\nfloat64[] a\nfloat64[] b\n\n# useful variables for evaluating time\nfloat64[] s\nfloat64[] time\nfloat64[] time_acc\n\n# delta_s in s domain\nfloat64   s_step\n\n# the order of trajectory.\nuint32 num_order\nuint32 num_segment\n\n# the polynomial coecfficients of the trajectory.\nfloat64 start_yaw\nfloat64 final_yaw\nfloat64[] coef_x\nfloat64[] coef_y\nfloat64[] coef_z\nfloat64[] range\nfloat64   mag_coeff\nuint32[]  order\n\nstring debug_info"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/StatusData.msg",
    "content": "Header header\nuint16 loop_rate\nfloat64 voltage\nuint8 seq\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/SwarmCommand.msg",
    "content": "#data structure\nHeader        header\nint32[]       selection\nnav_msgs/Path plan\nfloat32[]     formation #todo implement related code\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/SwarmInfo.msg",
    "content": "quadrotor_msgs/TrajectoryMatrix path\ntime start\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/SwarmOdometry.msg",
    "content": "nav_msgs/Odometry odom\nstring name\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/TRPYCommand.msg",
    "content": "Header header\nfloat32 thrust\nfloat32 roll\nfloat32 pitch\nfloat32 yaw\nquadrotor_msgs/AuxCommand aux\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/msg/TrajectoryMatrix.msg",
    "content": "#type\nuint8 TYPE_UNKNOWN = 0\nuint8 TYPE_POLY    = 1\nuint8 TYPE_TIME    = 2\n\n#data structure\nHeader    header\nuint8     type\nuint32    id\nstring    name\nuint32[]  format\nfloat64[] data\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/package.xml",
    "content": "<package>\n  <name>quadrotor_msgs</name>\n  <version>0.0.0</version>\n  <description>quadrotor_msgs</description>\n  <maintainer email=\"todo@todo.todo\">Kartik Mohta</maintainer>\n  <url>http://ros.org/wiki/quadrotor_msgs</url>\n  <license>BSD</license>\n  <buildtool_depend>catkin</buildtool_depend>\n  <build_depend>geometry_msgs       </build_depend>\n  <build_depend>nav_msgs            </build_depend>\n  <build_depend>message_generation  </build_depend>\n  <build_depend>cmake_utils         </build_depend>\n\n  <run_depend>geometry_msgs     </run_depend>\n  <run_depend>nav_msgs          </run_depend>\n  <run_depend>message_runtime   </run_depend>\n  <run_depend>cmake_utils   </run_depend>\n  <export>\n    <cpp cflags=\"-I${prefix}/include\"\n         lflags=\"-Wl,-rpath,${prefix}/lib -L${prefix}/lib\"/>\n  </export>\n\n</package>\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/src/decode_msgs.cpp",
    "content": "#include \"quadrotor_msgs/decode_msgs.h\"\n#include <quadrotor_msgs/comm_types.h>\n#include <Eigen/Geometry>\n\nnamespace quadrotor_msgs\n{\n\nbool decodeOutputData(const std::vector<uint8_t> &data,\n                      quadrotor_msgs::OutputData &output)\n{\n  struct OUTPUT_DATA output_data;\n  if(data.size() != sizeof(output_data))\n    return false;\n\n  memcpy(&output_data, &data[0], sizeof(output_data));\n  output.loop_rate = output_data.loop_rate;\n  output.voltage = output_data.voltage/1e3;\n\n  const double roll = output_data.roll/1e2 * M_PI/180;\n  const double pitch = output_data.pitch/1e2 * M_PI/180;\n  const double yaw = output_data.yaw/1e2 * M_PI/180;\n  // Asctec (2012 firmware) uses  Z-Y-X convention\n  Eigen::Quaterniond q = Eigen::AngleAxisd(yaw, Eigen::Vector3d::UnitZ()) *\n      Eigen::AngleAxisd(pitch, Eigen::Vector3d::UnitY()) *\n      Eigen::AngleAxisd(roll, Eigen::Vector3d::UnitX());\n  output.orientation.w = q.w();\n  output.orientation.x = q.x();\n  output.orientation.y = q.y();\n  output.orientation.z = q.z();\n\n  output.angular_velocity.x = output_data.ang_vel[0]*0.0154*M_PI/180;\n  output.angular_velocity.y = output_data.ang_vel[1]*0.0154*M_PI/180;\n  output.angular_velocity.z = output_data.ang_vel[2]*0.0154*M_PI/180;\n\n  output.linear_acceleration.x = output_data.acc[0]/1e3 * 9.81;\n  output.linear_acceleration.y = output_data.acc[1]/1e3 * 9.81;\n  output.linear_acceleration.z = output_data.acc[2]/1e3 * 9.81;\n\n  output.pressure_dheight = output_data.dheight/1e3;\n  output.pressure_height = output_data.height/1e3;\n\n  output.magnetic_field.x = output_data.mag[0]/2500.0;\n  output.magnetic_field.y = output_data.mag[1]/2500.0;\n  output.magnetic_field.z = output_data.mag[2]/2500.0;\n\n  for(int i = 0; i < 8; i++)\n  {\n    output.radio_channel[i] = output_data.radio[i];\n  }\n  //for(int i = 0; i < 4; i++)\n  //  output.motor_rpm[i] = output_data.rpm[i];\n\n  output.seq = output_data.seq;\n\n  return true;\n}\n\nbool decodeStatusData(const std::vector<uint8_t> &data,\n                      quadrotor_msgs::StatusData &status)\n{\n  struct STATUS_DATA status_data;\n  if(data.size() != sizeof(status_data))\n    return false;\n  memcpy(&status_data, &data[0], sizeof(status_data));\n\n  status.loop_rate = status_data.loop_rate;\n  status.voltage = status_data.voltage/1e3;\n  status.seq = status_data.seq;\n\n  return true;\n}\n\nbool decodePPROutputData(const std::vector<uint8_t> &data,\n                         quadrotor_msgs::PPROutputData &output)\n{\n  struct PPR_OUTPUT_DATA output_data;\n  if(data.size() != sizeof(output_data))\n    return false;\n  memcpy(&output_data, &data[0], sizeof(output_data));\n\n  output.quad_time = output_data.time;\n  output.des_thrust = output_data.des_thrust*1e-4;\n  output.des_roll = output_data.des_roll*1e-4;\n  output.des_pitch = output_data.des_pitch*1e-4;\n  output.des_yaw = output_data.des_yaw*1e-4;\n  output.est_roll = output_data.est_roll*1e-4;\n  output.est_pitch = output_data.est_pitch*1e-4;\n  output.est_yaw = output_data.est_yaw*1e-4;\n  output.est_angvel_x = output_data.est_angvel_x*1e-3;\n  output.est_angvel_y = output_data.est_angvel_y*1e-3;\n  output.est_angvel_z = output_data.est_angvel_z*1e-3;\n  output.est_acc_x = output_data.est_acc_x*1e-4;\n  output.est_acc_y = output_data.est_acc_y*1e-4;\n  output.est_acc_z = output_data.est_acc_z*1e-4;\n  output.pwm[0] = output_data.pwm1;\n  output.pwm[1] = output_data.pwm2;\n  output.pwm[2] = output_data.pwm3;\n  output.pwm[3] = output_data.pwm4;\n\n  return true;\n}\n\n}\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/src/encode_msgs.cpp",
    "content": "#include \"quadrotor_msgs/encode_msgs.h\"\n#include <quadrotor_msgs/comm_types.h>\n\nnamespace quadrotor_msgs\n{\n\nvoid encodeSO3Command(const quadrotor_msgs::SO3Command &so3_command,\n                      std::vector<uint8_t> &output)\n{\n  struct SO3_CMD_INPUT so3_cmd_input;\n\n  so3_cmd_input.force[0] = so3_command.force.x*500;\n  so3_cmd_input.force[1] = so3_command.force.y*500;\n  so3_cmd_input.force[2] = so3_command.force.z*500;\n\n  so3_cmd_input.des_qx = so3_command.orientation.x*125;\n  so3_cmd_input.des_qy = so3_command.orientation.y*125;\n  so3_cmd_input.des_qz = so3_command.orientation.z*125;\n  so3_cmd_input.des_qw = so3_command.orientation.w*125;\n\n  so3_cmd_input.kR[0] = so3_command.kR[0]*50;\n  so3_cmd_input.kR[1] = so3_command.kR[1]*50;\n  so3_cmd_input.kR[2] = so3_command.kR[2]*50;\n\n  so3_cmd_input.kOm[0] = so3_command.kOm[0]*100;\n  so3_cmd_input.kOm[1] = so3_command.kOm[1]*100;\n  so3_cmd_input.kOm[2] = so3_command.kOm[2]*100;\n\n  so3_cmd_input.cur_yaw = so3_command.aux.current_yaw*1e4;\n\n  so3_cmd_input.kf_correction = so3_command.aux.kf_correction*1e11;\n  so3_cmd_input.angle_corrections[0] = so3_command.aux.angle_corrections[0]*2500;\n  so3_cmd_input.angle_corrections[1] = so3_command.aux.angle_corrections[1]*2500;\n\n  so3_cmd_input.enable_motors = so3_command.aux.enable_motors;\n  so3_cmd_input.use_external_yaw = so3_command.aux.use_external_yaw;\n\n  so3_cmd_input.seq = so3_command.header.seq % 255;\n\n  output.resize(sizeof(so3_cmd_input));\n  memcpy(&output[0], &so3_cmd_input, sizeof(so3_cmd_input));\n}\n\nvoid encodeTRPYCommand(const quadrotor_msgs::TRPYCommand &trpy_command,\n                        std::vector<uint8_t> &output)\n{\n  struct TRPY_CMD trpy_cmd_input;\n  trpy_cmd_input.thrust = trpy_command.thrust*1e4;\n  trpy_cmd_input.roll = trpy_command.roll*1e4;\n  trpy_cmd_input.pitch = trpy_command.pitch*1e4;\n  trpy_cmd_input.yaw = trpy_command.yaw*1e4;\n  trpy_cmd_input.current_yaw = trpy_command.aux.current_yaw*1e4;\n  trpy_cmd_input.enable_motors = trpy_command.aux.enable_motors;\n  trpy_cmd_input.use_external_yaw = trpy_command.aux.use_external_yaw;\n\n  output.resize(sizeof(trpy_cmd_input));\n  memcpy(&output[0], &trpy_cmd_input, sizeof(trpy_cmd_input));\n}\n\nvoid encodePPRGains(const quadrotor_msgs::Gains &gains,\n                    std::vector<uint8_t> &output)\n{\n  struct PPR_GAINS ppr_gains;\n  ppr_gains.Kp = gains.Kp;\n  ppr_gains.Kd = gains.Kd;\n  ppr_gains.Kp_yaw = gains.Kp_yaw;\n  ppr_gains.Kd_yaw = gains.Kd_yaw;\n\n  output.resize(sizeof(ppr_gains));\n  memcpy(&output[0], &ppr_gains, sizeof(ppr_gains));\n}\n\n}\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/src/quadrotor_msgs/__init__.py",
    "content": ""
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/src/quadrotor_msgs/msg/_AuxCommand.py",
    "content": "\"\"\"autogenerated by genpy from quadrotor_msgs/AuxCommand.msg. Do not edit.\"\"\"\nimport sys\npython3 = True if sys.hexversion > 0x03000000 else False\nimport genpy\nimport struct\n\n\nclass AuxCommand(genpy.Message):\n  _md5sum = \"94f75840e4b1e03675da764692f2c839\"\n  _type = \"quadrotor_msgs/AuxCommand\"\n  _has_header = False #flag to mark the presence of a Header object\n  _full_text = \"\"\"float64 current_yaw\nfloat64 kf_correction\nfloat64[2] angle_corrections# Trims for roll, pitch\nbool enable_motors\nbool use_external_yaw\n\n\"\"\"\n  __slots__ = ['current_yaw','kf_correction','angle_corrections','enable_motors','use_external_yaw']\n  _slot_types = ['float64','float64','float64[2]','bool','bool']\n\n  def __init__(self, *args, **kwds):\n    \"\"\"\n    Constructor. Any message fields that are implicitly/explicitly\n    set to None will be assigned a default value. The recommend\n    use is keyword arguments as this is more robust to future message\n    changes.  You cannot mix in-order arguments and keyword arguments.\n\n    The available fields are:\n       current_yaw,kf_correction,angle_corrections,enable_motors,use_external_yaw\n\n    :param args: complete set of field values, in .msg order\n    :param kwds: use keyword arguments corresponding to message field names\n    to set specific fields.\n    \"\"\"\n    if args or kwds:\n      super(AuxCommand, self).__init__(*args, **kwds)\n      #message fields cannot be None, assign default values for those that are\n      if self.current_yaw is None:\n        self.current_yaw = 0.\n      if self.kf_correction is None:\n        self.kf_correction = 0.\n      if self.angle_corrections is None:\n        self.angle_corrections = [0.,0.]\n      if self.enable_motors is None:\n        self.enable_motors = False\n      if self.use_external_yaw is None:\n        self.use_external_yaw = False\n    else:\n      self.current_yaw = 0.\n      self.kf_correction = 0.\n      self.angle_corrections = [0.,0.]\n      self.enable_motors = False\n      self.use_external_yaw = False\n\n  def _get_types(self):\n    \"\"\"\n    internal API method\n    \"\"\"\n    return self._slot_types\n\n  def serialize(self, buff):\n    \"\"\"\n    serialize message into buffer\n    :param buff: buffer, ``StringIO``\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_2d.pack(_x.current_yaw, _x.kf_correction))\n      buff.write(_struct_2d.pack(*self.angle_corrections))\n      _x = self\n      buff.write(_struct_2B.pack(_x.enable_motors, _x.use_external_yaw))\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize(self, str):\n    \"\"\"\n    unpack serialized message in str into this message instance\n    :param str: byte array of serialized message, ``str``\n    \"\"\"\n    try:\n      end = 0\n      _x = self\n      start = end\n      end += 16\n      (_x.current_yaw, _x.kf_correction,) = _struct_2d.unpack(str[start:end])\n      start = end\n      end += 16\n      self.angle_corrections = _struct_2d.unpack(str[start:end])\n      _x = self\n      start = end\n      end += 2\n      (_x.enable_motors, _x.use_external_yaw,) = _struct_2B.unpack(str[start:end])\n      self.enable_motors = bool(self.enable_motors)\n      self.use_external_yaw = bool(self.use_external_yaw)\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n\n  def serialize_numpy(self, buff, numpy):\n    \"\"\"\n    serialize message with numpy array types into buffer\n    :param buff: buffer, ``StringIO``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_2d.pack(_x.current_yaw, _x.kf_correction))\n      buff.write(self.angle_corrections.tostring())\n      _x = self\n      buff.write(_struct_2B.pack(_x.enable_motors, _x.use_external_yaw))\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize_numpy(self, str, numpy):\n    \"\"\"\n    unpack serialized message in str into this message instance using numpy for array types\n    :param str: byte array of serialized message, ``str``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      end = 0\n      _x = self\n      start = end\n      end += 16\n      (_x.current_yaw, _x.kf_correction,) = _struct_2d.unpack(str[start:end])\n      start = end\n      end += 16\n      self.angle_corrections = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=2)\n      _x = self\n      start = end\n      end += 2\n      (_x.enable_motors, _x.use_external_yaw,) = _struct_2B.unpack(str[start:end])\n      self.enable_motors = bool(self.enable_motors)\n      self.use_external_yaw = bool(self.use_external_yaw)\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n_struct_I = genpy.struct_I\n_struct_2d = struct.Struct(\"<2d\")\n_struct_2B = struct.Struct(\"<2B\")\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/src/quadrotor_msgs/msg/_Corrections.py",
    "content": "\"\"\"autogenerated by genpy from quadrotor_msgs/Corrections.msg. Do not edit.\"\"\"\nimport sys\npython3 = True if sys.hexversion > 0x03000000 else False\nimport genpy\nimport struct\n\n\nclass Corrections(genpy.Message):\n  _md5sum = \"61e86887a75fe520847d3256306360f5\"\n  _type = \"quadrotor_msgs/Corrections\"\n  _has_header = False #flag to mark the presence of a Header object\n  _full_text = \"\"\"float64 kf_correction\nfloat64[2] angle_corrections\n\n\"\"\"\n  __slots__ = ['kf_correction','angle_corrections']\n  _slot_types = ['float64','float64[2]']\n\n  def __init__(self, *args, **kwds):\n    \"\"\"\n    Constructor. Any message fields that are implicitly/explicitly\n    set to None will be assigned a default value. The recommend\n    use is keyword arguments as this is more robust to future message\n    changes.  You cannot mix in-order arguments and keyword arguments.\n\n    The available fields are:\n       kf_correction,angle_corrections\n\n    :param args: complete set of field values, in .msg order\n    :param kwds: use keyword arguments corresponding to message field names\n    to set specific fields.\n    \"\"\"\n    if args or kwds:\n      super(Corrections, self).__init__(*args, **kwds)\n      #message fields cannot be None, assign default values for those that are\n      if self.kf_correction is None:\n        self.kf_correction = 0.\n      if self.angle_corrections is None:\n        self.angle_corrections = [0.,0.]\n    else:\n      self.kf_correction = 0.\n      self.angle_corrections = [0.,0.]\n\n  def _get_types(self):\n    \"\"\"\n    internal API method\n    \"\"\"\n    return self._slot_types\n\n  def serialize(self, buff):\n    \"\"\"\n    serialize message into buffer\n    :param buff: buffer, ``StringIO``\n    \"\"\"\n    try:\n      buff.write(_struct_d.pack(self.kf_correction))\n      buff.write(_struct_2d.pack(*self.angle_corrections))\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize(self, str):\n    \"\"\"\n    unpack serialized message in str into this message instance\n    :param str: byte array of serialized message, ``str``\n    \"\"\"\n    try:\n      end = 0\n      start = end\n      end += 8\n      (self.kf_correction,) = _struct_d.unpack(str[start:end])\n      start = end\n      end += 16\n      self.angle_corrections = _struct_2d.unpack(str[start:end])\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n\n  def serialize_numpy(self, buff, numpy):\n    \"\"\"\n    serialize message with numpy array types into buffer\n    :param buff: buffer, ``StringIO``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      buff.write(_struct_d.pack(self.kf_correction))\n      buff.write(self.angle_corrections.tostring())\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize_numpy(self, str, numpy):\n    \"\"\"\n    unpack serialized message in str into this message instance using numpy for array types\n    :param str: byte array of serialized message, ``str``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      end = 0\n      start = end\n      end += 8\n      (self.kf_correction,) = _struct_d.unpack(str[start:end])\n      start = end\n      end += 16\n      self.angle_corrections = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=2)\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n_struct_I = genpy.struct_I\n_struct_2d = struct.Struct(\"<2d\")\n_struct_d = struct.Struct(\"<d\")\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/src/quadrotor_msgs/msg/_Gains.py",
    "content": "\"\"\"autogenerated by genpy from quadrotor_msgs/Gains.msg. Do not edit.\"\"\"\nimport sys\npython3 = True if sys.hexversion > 0x03000000 else False\nimport genpy\nimport struct\n\n\nclass Gains(genpy.Message):\n  _md5sum = \"b82763b9f24e5595e2a816aa779c9461\"\n  _type = \"quadrotor_msgs/Gains\"\n  _has_header = False #flag to mark the presence of a Header object\n  _full_text = \"\"\"float64 Kp\nfloat64 Kd\nfloat64 Kp_yaw\nfloat64 Kd_yaw\n\n\"\"\"\n  __slots__ = ['Kp','Kd','Kp_yaw','Kd_yaw']\n  _slot_types = ['float64','float64','float64','float64']\n\n  def __init__(self, *args, **kwds):\n    \"\"\"\n    Constructor. Any message fields that are implicitly/explicitly\n    set to None will be assigned a default value. The recommend\n    use is keyword arguments as this is more robust to future message\n    changes.  You cannot mix in-order arguments and keyword arguments.\n\n    The available fields are:\n       Kp,Kd,Kp_yaw,Kd_yaw\n\n    :param args: complete set of field values, in .msg order\n    :param kwds: use keyword arguments corresponding to message field names\n    to set specific fields.\n    \"\"\"\n    if args or kwds:\n      super(Gains, self).__init__(*args, **kwds)\n      #message fields cannot be None, assign default values for those that are\n      if self.Kp is None:\n        self.Kp = 0.\n      if self.Kd is None:\n        self.Kd = 0.\n      if self.Kp_yaw is None:\n        self.Kp_yaw = 0.\n      if self.Kd_yaw is None:\n        self.Kd_yaw = 0.\n    else:\n      self.Kp = 0.\n      self.Kd = 0.\n      self.Kp_yaw = 0.\n      self.Kd_yaw = 0.\n\n  def _get_types(self):\n    \"\"\"\n    internal API method\n    \"\"\"\n    return self._slot_types\n\n  def serialize(self, buff):\n    \"\"\"\n    serialize message into buffer\n    :param buff: buffer, ``StringIO``\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_4d.pack(_x.Kp, _x.Kd, _x.Kp_yaw, _x.Kd_yaw))\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize(self, str):\n    \"\"\"\n    unpack serialized message in str into this message instance\n    :param str: byte array of serialized message, ``str``\n    \"\"\"\n    try:\n      end = 0\n      _x = self\n      start = end\n      end += 32\n      (_x.Kp, _x.Kd, _x.Kp_yaw, _x.Kd_yaw,) = _struct_4d.unpack(str[start:end])\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n\n  def serialize_numpy(self, buff, numpy):\n    \"\"\"\n    serialize message with numpy array types into buffer\n    :param buff: buffer, ``StringIO``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_4d.pack(_x.Kp, _x.Kd, _x.Kp_yaw, _x.Kd_yaw))\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize_numpy(self, str, numpy):\n    \"\"\"\n    unpack serialized message in str into this message instance using numpy for array types\n    :param str: byte array of serialized message, ``str``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      end = 0\n      _x = self\n      start = end\n      end += 32\n      (_x.Kp, _x.Kd, _x.Kp_yaw, _x.Kd_yaw,) = _struct_4d.unpack(str[start:end])\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n_struct_I = genpy.struct_I\n_struct_4d = struct.Struct(\"<4d\")\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/src/quadrotor_msgs/msg/_OutputData.py",
    "content": "\"\"\"autogenerated by genpy from quadrotor_msgs/OutputData.msg. Do not edit.\"\"\"\nimport sys\npython3 = True if sys.hexversion > 0x03000000 else False\nimport genpy\nimport struct\n\nimport geometry_msgs.msg\nimport std_msgs.msg\n\nclass OutputData(genpy.Message):\n  _md5sum = \"5759ee97fd5c090dcdccf7fc3e50d024\"\n  _type = \"quadrotor_msgs/OutputData\"\n  _has_header = True #flag to mark the presence of a Header object\n  _full_text = \"\"\"Header header\nuint16 loop_rate\nfloat64 voltage\ngeometry_msgs/Quaternion orientation\ngeometry_msgs/Vector3 angular_velocity\ngeometry_msgs/Vector3 linear_acceleration\nfloat64 pressure_dheight\nfloat64 pressure_height\ngeometry_msgs/Vector3 magnetic_field\nuint8[8] radio_channel\n#uint8[4] motor_rpm\nuint8 seq\n\n================================================================================\nMSG: std_msgs/Header\n# Standard metadata for higher-level stamped data types.\n# This is generally used to communicate timestamped data \n# in a particular coordinate frame.\n# \n# sequence ID: consecutively increasing ID \nuint32 seq\n#Two-integer timestamp that is expressed as:\n# * stamp.sec: seconds (stamp_secs) since epoch (in Python the variable is called 'secs')\n# * stamp.nsec: nanoseconds since stamp_secs (in Python the variable is called 'nsecs')\n# time-handling sugar is provided by the client library\ntime stamp\n#Frame this data is associated with\n# 0: no frame\n# 1: global frame\nstring frame_id\n\n================================================================================\nMSG: geometry_msgs/Quaternion\n# This represents an orientation in free space in quaternion form.\n\nfloat64 x\nfloat64 y\nfloat64 z\nfloat64 w\n\n================================================================================\nMSG: geometry_msgs/Vector3\n# This represents a vector in free space. \n\nfloat64 x\nfloat64 y\nfloat64 z\n\"\"\"\n  __slots__ = ['header','loop_rate','voltage','orientation','angular_velocity','linear_acceleration','pressure_dheight','pressure_height','magnetic_field','radio_channel','seq']\n  _slot_types = ['std_msgs/Header','uint16','float64','geometry_msgs/Quaternion','geometry_msgs/Vector3','geometry_msgs/Vector3','float64','float64','geometry_msgs/Vector3','uint8[8]','uint8']\n\n  def __init__(self, *args, **kwds):\n    \"\"\"\n    Constructor. Any message fields that are implicitly/explicitly\n    set to None will be assigned a default value. The recommend\n    use is keyword arguments as this is more robust to future message\n    changes.  You cannot mix in-order arguments and keyword arguments.\n\n    The available fields are:\n       header,loop_rate,voltage,orientation,angular_velocity,linear_acceleration,pressure_dheight,pressure_height,magnetic_field,radio_channel,seq\n\n    :param args: complete set of field values, in .msg order\n    :param kwds: use keyword arguments corresponding to message field names\n    to set specific fields.\n    \"\"\"\n    if args or kwds:\n      super(OutputData, self).__init__(*args, **kwds)\n      #message fields cannot be None, assign default values for those that are\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      if self.loop_rate is None:\n        self.loop_rate = 0\n      if self.voltage is None:\n        self.voltage = 0.\n      if self.orientation is None:\n        self.orientation = geometry_msgs.msg.Quaternion()\n      if self.angular_velocity is None:\n        self.angular_velocity = geometry_msgs.msg.Vector3()\n      if self.linear_acceleration is None:\n        self.linear_acceleration = geometry_msgs.msg.Vector3()\n      if self.pressure_dheight is None:\n        self.pressure_dheight = 0.\n      if self.pressure_height is None:\n        self.pressure_height = 0.\n      if self.magnetic_field is None:\n        self.magnetic_field = geometry_msgs.msg.Vector3()\n      if self.radio_channel is None:\n        self.radio_channel = chr(0)*8\n      if self.seq is None:\n        self.seq = 0\n    else:\n      self.header = std_msgs.msg.Header()\n      self.loop_rate = 0\n      self.voltage = 0.\n      self.orientation = geometry_msgs.msg.Quaternion()\n      self.angular_velocity = geometry_msgs.msg.Vector3()\n      self.linear_acceleration = geometry_msgs.msg.Vector3()\n      self.pressure_dheight = 0.\n      self.pressure_height = 0.\n      self.magnetic_field = geometry_msgs.msg.Vector3()\n      self.radio_channel = chr(0)*8\n      self.seq = 0\n\n  def _get_types(self):\n    \"\"\"\n    internal API method\n    \"\"\"\n    return self._slot_types\n\n  def serialize(self, buff):\n    \"\"\"\n    serialize message into buffer\n    :param buff: buffer, ``StringIO``\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_3I.pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\n      _x = self.header.frame_id\n      length = len(_x)\n      if python3 or type(_x) == unicode:\n        _x = _x.encode('utf-8')\n        length = len(_x)\n      if python3:\n        buff.write(struct.pack('<I%sB'%length, length, *_x))\n      else:\n        buff.write(struct.pack('<I%ss'%length, length, _x))\n      _x = self\n      buff.write(_struct_H16d.pack(_x.loop_rate, _x.voltage, _x.orientation.x, _x.orientation.y, _x.orientation.z, _x.orientation.w, _x.angular_velocity.x, _x.angular_velocity.y, _x.angular_velocity.z, _x.linear_acceleration.x, _x.linear_acceleration.y, _x.linear_acceleration.z, _x.pressure_dheight, _x.pressure_height, _x.magnetic_field.x, _x.magnetic_field.y, _x.magnetic_field.z))\n      _x = self.radio_channel\n      # - if encoded as a list instead, serialize as bytes instead of string\n      if type(_x) in [list, tuple]:\n        buff.write(_struct_8B.pack(*_x))\n      else:\n        buff.write(_struct_8s.pack(_x))\n      buff.write(_struct_B.pack(self.seq))\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize(self, str):\n    \"\"\"\n    unpack serialized message in str into this message instance\n    :param str: byte array of serialized message, ``str``\n    \"\"\"\n    try:\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      if self.orientation is None:\n        self.orientation = geometry_msgs.msg.Quaternion()\n      if self.angular_velocity is None:\n        self.angular_velocity = geometry_msgs.msg.Vector3()\n      if self.linear_acceleration is None:\n        self.linear_acceleration = geometry_msgs.msg.Vector3()\n      if self.magnetic_field is None:\n        self.magnetic_field = geometry_msgs.msg.Vector3()\n      end = 0\n      _x = self\n      start = end\n      end += 12\n      (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\n      start = end\n      end += 4\n      (length,) = _struct_I.unpack(str[start:end])\n      start = end\n      end += length\n      if python3:\n        self.header.frame_id = str[start:end].decode('utf-8')\n      else:\n        self.header.frame_id = str[start:end]\n      _x = self\n      start = end\n      end += 130\n      (_x.loop_rate, _x.voltage, _x.orientation.x, _x.orientation.y, _x.orientation.z, _x.orientation.w, _x.angular_velocity.x, _x.angular_velocity.y, _x.angular_velocity.z, _x.linear_acceleration.x, _x.linear_acceleration.y, _x.linear_acceleration.z, _x.pressure_dheight, _x.pressure_height, _x.magnetic_field.x, _x.magnetic_field.y, _x.magnetic_field.z,) = _struct_H16d.unpack(str[start:end])\n      start = end\n      end += 8\n      self.radio_channel = str[start:end]\n      start = end\n      end += 1\n      (self.seq,) = _struct_B.unpack(str[start:end])\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n\n  def serialize_numpy(self, buff, numpy):\n    \"\"\"\n    serialize message with numpy array types into buffer\n    :param buff: buffer, ``StringIO``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_3I.pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\n      _x = self.header.frame_id\n      length = len(_x)\n      if python3 or type(_x) == unicode:\n        _x = _x.encode('utf-8')\n        length = len(_x)\n      if python3:\n        buff.write(struct.pack('<I%sB'%length, length, *_x))\n      else:\n        buff.write(struct.pack('<I%ss'%length, length, _x))\n      _x = self\n      buff.write(_struct_H16d.pack(_x.loop_rate, _x.voltage, _x.orientation.x, _x.orientation.y, _x.orientation.z, _x.orientation.w, _x.angular_velocity.x, _x.angular_velocity.y, _x.angular_velocity.z, _x.linear_acceleration.x, _x.linear_acceleration.y, _x.linear_acceleration.z, _x.pressure_dheight, _x.pressure_height, _x.magnetic_field.x, _x.magnetic_field.y, _x.magnetic_field.z))\n      _x = self.radio_channel\n      # - if encoded as a list instead, serialize as bytes instead of string\n      if type(_x) in [list, tuple]:\n        buff.write(_struct_8B.pack(*_x))\n      else:\n        buff.write(_struct_8s.pack(_x))\n      buff.write(_struct_B.pack(self.seq))\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize_numpy(self, str, numpy):\n    \"\"\"\n    unpack serialized message in str into this message instance using numpy for array types\n    :param str: byte array of serialized message, ``str``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      if self.orientation is None:\n        self.orientation = geometry_msgs.msg.Quaternion()\n      if self.angular_velocity is None:\n        self.angular_velocity = geometry_msgs.msg.Vector3()\n      if self.linear_acceleration is None:\n        self.linear_acceleration = geometry_msgs.msg.Vector3()\n      if self.magnetic_field is None:\n        self.magnetic_field = geometry_msgs.msg.Vector3()\n      end = 0\n      _x = self\n      start = end\n      end += 12\n      (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\n      start = end\n      end += 4\n      (length,) = _struct_I.unpack(str[start:end])\n      start = end\n      end += length\n      if python3:\n        self.header.frame_id = str[start:end].decode('utf-8')\n      else:\n        self.header.frame_id = str[start:end]\n      _x = self\n      start = end\n      end += 130\n      (_x.loop_rate, _x.voltage, _x.orientation.x, _x.orientation.y, _x.orientation.z, _x.orientation.w, _x.angular_velocity.x, _x.angular_velocity.y, _x.angular_velocity.z, _x.linear_acceleration.x, _x.linear_acceleration.y, _x.linear_acceleration.z, _x.pressure_dheight, _x.pressure_height, _x.magnetic_field.x, _x.magnetic_field.y, _x.magnetic_field.z,) = _struct_H16d.unpack(str[start:end])\n      start = end\n      end += 8\n      self.radio_channel = str[start:end]\n      start = end\n      end += 1\n      (self.seq,) = _struct_B.unpack(str[start:end])\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n_struct_I = genpy.struct_I\n_struct_8B = struct.Struct(\"<8B\")\n_struct_8s = struct.Struct(\"<8s\")\n_struct_3I = struct.Struct(\"<3I\")\n_struct_B = struct.Struct(\"<B\")\n_struct_H16d = struct.Struct(\"<H16d\")\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/src/quadrotor_msgs/msg/_PPROutputData.py",
    "content": "\"\"\"autogenerated by genpy from quadrotor_msgs/PPROutputData.msg. Do not edit.\"\"\"\nimport sys\npython3 = True if sys.hexversion > 0x03000000 else False\nimport genpy\nimport struct\n\nimport std_msgs.msg\n\nclass PPROutputData(genpy.Message):\n  _md5sum = \"732c0e3ca36f241464f8c445e78a0d0a\"\n  _type = \"quadrotor_msgs/PPROutputData\"\n  _has_header = True #flag to mark the presence of a Header object\n  _full_text = \"\"\"Header header\nuint16 quad_time\nfloat64 des_thrust\nfloat64 des_roll\nfloat64 des_pitch\nfloat64 des_yaw\nfloat64 est_roll\nfloat64 est_pitch\nfloat64 est_yaw\nfloat64 est_angvel_x\nfloat64 est_angvel_y\nfloat64 est_angvel_z\nfloat64 est_acc_x\nfloat64 est_acc_y\nfloat64 est_acc_z\nuint16[4] pwm\n\n================================================================================\nMSG: std_msgs/Header\n# Standard metadata for higher-level stamped data types.\n# This is generally used to communicate timestamped data \n# in a particular coordinate frame.\n# \n# sequence ID: consecutively increasing ID \nuint32 seq\n#Two-integer timestamp that is expressed as:\n# * stamp.sec: seconds (stamp_secs) since epoch (in Python the variable is called 'secs')\n# * stamp.nsec: nanoseconds since stamp_secs (in Python the variable is called 'nsecs')\n# time-handling sugar is provided by the client library\ntime stamp\n#Frame this data is associated with\n# 0: no frame\n# 1: global frame\nstring frame_id\n\n\"\"\"\n  __slots__ = ['header','quad_time','des_thrust','des_roll','des_pitch','des_yaw','est_roll','est_pitch','est_yaw','est_angvel_x','est_angvel_y','est_angvel_z','est_acc_x','est_acc_y','est_acc_z','pwm']\n  _slot_types = ['std_msgs/Header','uint16','float64','float64','float64','float64','float64','float64','float64','float64','float64','float64','float64','float64','float64','uint16[4]']\n\n  def __init__(self, *args, **kwds):\n    \"\"\"\n    Constructor. Any message fields that are implicitly/explicitly\n    set to None will be assigned a default value. The recommend\n    use is keyword arguments as this is more robust to future message\n    changes.  You cannot mix in-order arguments and keyword arguments.\n\n    The available fields are:\n       header,quad_time,des_thrust,des_roll,des_pitch,des_yaw,est_roll,est_pitch,est_yaw,est_angvel_x,est_angvel_y,est_angvel_z,est_acc_x,est_acc_y,est_acc_z,pwm\n\n    :param args: complete set of field values, in .msg order\n    :param kwds: use keyword arguments corresponding to message field names\n    to set specific fields.\n    \"\"\"\n    if args or kwds:\n      super(PPROutputData, self).__init__(*args, **kwds)\n      #message fields cannot be None, assign default values for those that are\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      if self.quad_time is None:\n        self.quad_time = 0\n      if self.des_thrust is None:\n        self.des_thrust = 0.\n      if self.des_roll is None:\n        self.des_roll = 0.\n      if self.des_pitch is None:\n        self.des_pitch = 0.\n      if self.des_yaw is None:\n        self.des_yaw = 0.\n      if self.est_roll is None:\n        self.est_roll = 0.\n      if self.est_pitch is None:\n        self.est_pitch = 0.\n      if self.est_yaw is None:\n        self.est_yaw = 0.\n      if self.est_angvel_x is None:\n        self.est_angvel_x = 0.\n      if self.est_angvel_y is None:\n        self.est_angvel_y = 0.\n      if self.est_angvel_z is None:\n        self.est_angvel_z = 0.\n      if self.est_acc_x is None:\n        self.est_acc_x = 0.\n      if self.est_acc_y is None:\n        self.est_acc_y = 0.\n      if self.est_acc_z is None:\n        self.est_acc_z = 0.\n      if self.pwm is None:\n        self.pwm = [0,0,0,0]\n    else:\n      self.header = std_msgs.msg.Header()\n      self.quad_time = 0\n      self.des_thrust = 0.\n      self.des_roll = 0.\n      self.des_pitch = 0.\n      self.des_yaw = 0.\n      self.est_roll = 0.\n      self.est_pitch = 0.\n      self.est_yaw = 0.\n      self.est_angvel_x = 0.\n      self.est_angvel_y = 0.\n      self.est_angvel_z = 0.\n      self.est_acc_x = 0.\n      self.est_acc_y = 0.\n      self.est_acc_z = 0.\n      self.pwm = [0,0,0,0]\n\n  def _get_types(self):\n    \"\"\"\n    internal API method\n    \"\"\"\n    return self._slot_types\n\n  def serialize(self, buff):\n    \"\"\"\n    serialize message into buffer\n    :param buff: buffer, ``StringIO``\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_3I.pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\n      _x = self.header.frame_id\n      length = len(_x)\n      if python3 or type(_x) == unicode:\n        _x = _x.encode('utf-8')\n        length = len(_x)\n      if python3:\n        buff.write(struct.pack('<I%sB'%length, length, *_x))\n      else:\n        buff.write(struct.pack('<I%ss'%length, length, _x))\n      _x = self\n      buff.write(_struct_H13d.pack(_x.quad_time, _x.des_thrust, _x.des_roll, _x.des_pitch, _x.des_yaw, _x.est_roll, _x.est_pitch, _x.est_yaw, _x.est_angvel_x, _x.est_angvel_y, _x.est_angvel_z, _x.est_acc_x, _x.est_acc_y, _x.est_acc_z))\n      buff.write(_struct_4H.pack(*self.pwm))\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize(self, str):\n    \"\"\"\n    unpack serialized message in str into this message instance\n    :param str: byte array of serialized message, ``str``\n    \"\"\"\n    try:\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      end = 0\n      _x = self\n      start = end\n      end += 12\n      (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\n      start = end\n      end += 4\n      (length,) = _struct_I.unpack(str[start:end])\n      start = end\n      end += length\n      if python3:\n        self.header.frame_id = str[start:end].decode('utf-8')\n      else:\n        self.header.frame_id = str[start:end]\n      _x = self\n      start = end\n      end += 106\n      (_x.quad_time, _x.des_thrust, _x.des_roll, _x.des_pitch, _x.des_yaw, _x.est_roll, _x.est_pitch, _x.est_yaw, _x.est_angvel_x, _x.est_angvel_y, _x.est_angvel_z, _x.est_acc_x, _x.est_acc_y, _x.est_acc_z,) = _struct_H13d.unpack(str[start:end])\n      start = end\n      end += 8\n      self.pwm = _struct_4H.unpack(str[start:end])\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n\n  def serialize_numpy(self, buff, numpy):\n    \"\"\"\n    serialize message with numpy array types into buffer\n    :param buff: buffer, ``StringIO``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_3I.pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\n      _x = self.header.frame_id\n      length = len(_x)\n      if python3 or type(_x) == unicode:\n        _x = _x.encode('utf-8')\n        length = len(_x)\n      if python3:\n        buff.write(struct.pack('<I%sB'%length, length, *_x))\n      else:\n        buff.write(struct.pack('<I%ss'%length, length, _x))\n      _x = self\n      buff.write(_struct_H13d.pack(_x.quad_time, _x.des_thrust, _x.des_roll, _x.des_pitch, _x.des_yaw, _x.est_roll, _x.est_pitch, _x.est_yaw, _x.est_angvel_x, _x.est_angvel_y, _x.est_angvel_z, _x.est_acc_x, _x.est_acc_y, _x.est_acc_z))\n      buff.write(self.pwm.tostring())\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize_numpy(self, str, numpy):\n    \"\"\"\n    unpack serialized message in str into this message instance using numpy for array types\n    :param str: byte array of serialized message, ``str``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      end = 0\n      _x = self\n      start = end\n      end += 12\n      (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\n      start = end\n      end += 4\n      (length,) = _struct_I.unpack(str[start:end])\n      start = end\n      end += length\n      if python3:\n        self.header.frame_id = str[start:end].decode('utf-8')\n      else:\n        self.header.frame_id = str[start:end]\n      _x = self\n      start = end\n      end += 106\n      (_x.quad_time, _x.des_thrust, _x.des_roll, _x.des_pitch, _x.des_yaw, _x.est_roll, _x.est_pitch, _x.est_yaw, _x.est_angvel_x, _x.est_angvel_y, _x.est_angvel_z, _x.est_acc_x, _x.est_acc_y, _x.est_acc_z,) = _struct_H13d.unpack(str[start:end])\n      start = end\n      end += 8\n      self.pwm = numpy.frombuffer(str[start:end], dtype=numpy.uint16, count=4)\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n_struct_I = genpy.struct_I\n_struct_H13d = struct.Struct(\"<H13d\")\n_struct_3I = struct.Struct(\"<3I\")\n_struct_4H = struct.Struct(\"<4H\")\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/src/quadrotor_msgs/msg/_PositionCommand.py",
    "content": "\"\"\"autogenerated by genpy from quadrotor_msgs/PositionCommand.msg. Do not edit.\"\"\"\nimport sys\npython3 = True if sys.hexversion > 0x03000000 else False\nimport genpy\nimport struct\n\nimport geometry_msgs.msg\nimport std_msgs.msg\n\nclass PositionCommand(genpy.Message):\n  _md5sum = \"835935bcd6f18632d9e26a3093237902\"\n  _type = \"quadrotor_msgs/PositionCommand\"\n  _has_header = True #flag to mark the presence of a Header object\n  _full_text = \"\"\"Header header\ngeometry_msgs/Point position\ngeometry_msgs/Vector3 velocity\ngeometry_msgs/Vector3 acceleration\nfloat64 yaw\nfloat64 yaw_dot\nfloat64[3] kx\nfloat64[3] kv\n\n================================================================================\nMSG: std_msgs/Header\n# Standard metadata for higher-level stamped data types.\n# This is generally used to communicate timestamped data \n# in a particular coordinate frame.\n# \n# sequence ID: consecutively increasing ID \nuint32 seq\n#Two-integer timestamp that is expressed as:\n# * stamp.sec: seconds (stamp_secs) since epoch (in Python the variable is called 'secs')\n# * stamp.nsec: nanoseconds since stamp_secs (in Python the variable is called 'nsecs')\n# time-handling sugar is provided by the client library\ntime stamp\n#Frame this data is associated with\n# 0: no frame\n# 1: global frame\nstring frame_id\n\n================================================================================\nMSG: geometry_msgs/Point\n# This contains the position of a point in free space\nfloat64 x\nfloat64 y\nfloat64 z\n\n================================================================================\nMSG: geometry_msgs/Vector3\n# This represents a vector in free space. \n\nfloat64 x\nfloat64 y\nfloat64 z\n\"\"\"\n  __slots__ = ['header','position','velocity','acceleration','yaw','yaw_dot','kx','kv']\n  _slot_types = ['std_msgs/Header','geometry_msgs/Point','geometry_msgs/Vector3','geometry_msgs/Vector3','float64','float64','float64[3]','float64[3]']\n\n  def __init__(self, *args, **kwds):\n    \"\"\"\n    Constructor. Any message fields that are implicitly/explicitly\n    set to None will be assigned a default value. The recommend\n    use is keyword arguments as this is more robust to future message\n    changes.  You cannot mix in-order arguments and keyword arguments.\n\n    The available fields are:\n       header,position,velocity,acceleration,yaw,yaw_dot,kx,kv\n\n    :param args: complete set of field values, in .msg order\n    :param kwds: use keyword arguments corresponding to message field names\n    to set specific fields.\n    \"\"\"\n    if args or kwds:\n      super(PositionCommand, self).__init__(*args, **kwds)\n      #message fields cannot be None, assign default values for those that are\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      if self.position is None:\n        self.position = geometry_msgs.msg.Point()\n      if self.velocity is None:\n        self.velocity = geometry_msgs.msg.Vector3()\n      if self.acceleration is None:\n        self.acceleration = geometry_msgs.msg.Vector3()\n      if self.yaw is None:\n        self.yaw = 0.\n      if self.yaw_dot is None:\n        self.yaw_dot = 0.\n      if self.kx is None:\n        self.kx = [0.,0.,0.]\n      if self.kv is None:\n        self.kv = [0.,0.,0.]\n    else:\n      self.header = std_msgs.msg.Header()\n      self.position = geometry_msgs.msg.Point()\n      self.velocity = geometry_msgs.msg.Vector3()\n      self.acceleration = geometry_msgs.msg.Vector3()\n      self.yaw = 0.\n      self.yaw_dot = 0.\n      self.kx = [0.,0.,0.]\n      self.kv = [0.,0.,0.]\n\n  def _get_types(self):\n    \"\"\"\n    internal API method\n    \"\"\"\n    return self._slot_types\n\n  def serialize(self, buff):\n    \"\"\"\n    serialize message into buffer\n    :param buff: buffer, ``StringIO``\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_3I.pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\n      _x = self.header.frame_id\n      length = len(_x)\n      if python3 or type(_x) == unicode:\n        _x = _x.encode('utf-8')\n        length = len(_x)\n      if python3:\n        buff.write(struct.pack('<I%sB'%length, length, *_x))\n      else:\n        buff.write(struct.pack('<I%ss'%length, length, _x))\n      _x = self\n      buff.write(_struct_11d.pack(_x.position.x, _x.position.y, _x.position.z, _x.velocity.x, _x.velocity.y, _x.velocity.z, _x.acceleration.x, _x.acceleration.y, _x.acceleration.z, _x.yaw, _x.yaw_dot))\n      buff.write(_struct_3d.pack(*self.kx))\n      buff.write(_struct_3d.pack(*self.kv))\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize(self, str):\n    \"\"\"\n    unpack serialized message in str into this message instance\n    :param str: byte array of serialized message, ``str``\n    \"\"\"\n    try:\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      if self.position is None:\n        self.position = geometry_msgs.msg.Point()\n      if self.velocity is None:\n        self.velocity = geometry_msgs.msg.Vector3()\n      if self.acceleration is None:\n        self.acceleration = geometry_msgs.msg.Vector3()\n      end = 0\n      _x = self\n      start = end\n      end += 12\n      (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\n      start = end\n      end += 4\n      (length,) = _struct_I.unpack(str[start:end])\n      start = end\n      end += length\n      if python3:\n        self.header.frame_id = str[start:end].decode('utf-8')\n      else:\n        self.header.frame_id = str[start:end]\n      _x = self\n      start = end\n      end += 88\n      (_x.position.x, _x.position.y, _x.position.z, _x.velocity.x, _x.velocity.y, _x.velocity.z, _x.acceleration.x, _x.acceleration.y, _x.acceleration.z, _x.yaw, _x.yaw_dot,) = _struct_11d.unpack(str[start:end])\n      start = end\n      end += 24\n      self.kx = _struct_3d.unpack(str[start:end])\n      start = end\n      end += 24\n      self.kv = _struct_3d.unpack(str[start:end])\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n\n  def serialize_numpy(self, buff, numpy):\n    \"\"\"\n    serialize message with numpy array types into buffer\n    :param buff: buffer, ``StringIO``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_3I.pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\n      _x = self.header.frame_id\n      length = len(_x)\n      if python3 or type(_x) == unicode:\n        _x = _x.encode('utf-8')\n        length = len(_x)\n      if python3:\n        buff.write(struct.pack('<I%sB'%length, length, *_x))\n      else:\n        buff.write(struct.pack('<I%ss'%length, length, _x))\n      _x = self\n      buff.write(_struct_11d.pack(_x.position.x, _x.position.y, _x.position.z, _x.velocity.x, _x.velocity.y, _x.velocity.z, _x.acceleration.x, _x.acceleration.y, _x.acceleration.z, _x.yaw, _x.yaw_dot))\n      buff.write(self.kx.tostring())\n      buff.write(self.kv.tostring())\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize_numpy(self, str, numpy):\n    \"\"\"\n    unpack serialized message in str into this message instance using numpy for array types\n    :param str: byte array of serialized message, ``str``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      if self.position is None:\n        self.position = geometry_msgs.msg.Point()\n      if self.velocity is None:\n        self.velocity = geometry_msgs.msg.Vector3()\n      if self.acceleration is None:\n        self.acceleration = geometry_msgs.msg.Vector3()\n      end = 0\n      _x = self\n      start = end\n      end += 12\n      (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\n      start = end\n      end += 4\n      (length,) = _struct_I.unpack(str[start:end])\n      start = end\n      end += length\n      if python3:\n        self.header.frame_id = str[start:end].decode('utf-8')\n      else:\n        self.header.frame_id = str[start:end]\n      _x = self\n      start = end\n      end += 88\n      (_x.position.x, _x.position.y, _x.position.z, _x.velocity.x, _x.velocity.y, _x.velocity.z, _x.acceleration.x, _x.acceleration.y, _x.acceleration.z, _x.yaw, _x.yaw_dot,) = _struct_11d.unpack(str[start:end])\n      start = end\n      end += 24\n      self.kx = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=3)\n      start = end\n      end += 24\n      self.kv = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=3)\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n_struct_I = genpy.struct_I\n_struct_11d = struct.Struct(\"<11d\")\n_struct_3I = struct.Struct(\"<3I\")\n_struct_3d = struct.Struct(\"<3d\")\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/src/quadrotor_msgs/msg/_SO3Command.py",
    "content": "\"\"\"autogenerated by genpy from quadrotor_msgs/SO3Command.msg. Do not edit.\"\"\"\nimport sys\npython3 = True if sys.hexversion > 0x03000000 else False\nimport genpy\nimport struct\n\nimport geometry_msgs.msg\nimport quadrotor_msgs.msg\nimport std_msgs.msg\n\nclass SO3Command(genpy.Message):\n  _md5sum = \"a466650b2633e768513aa3bf62383c86\"\n  _type = \"quadrotor_msgs/SO3Command\"\n  _has_header = True #flag to mark the presence of a Header object\n  _full_text = \"\"\"Header header\ngeometry_msgs/Vector3 force\ngeometry_msgs/Quaternion orientation\nfloat64[3] kR\nfloat64[3] kOm\nquadrotor_msgs/AuxCommand aux\n\n================================================================================\nMSG: std_msgs/Header\n# Standard metadata for higher-level stamped data types.\n# This is generally used to communicate timestamped data \n# in a particular coordinate frame.\n# \n# sequence ID: consecutively increasing ID \nuint32 seq\n#Two-integer timestamp that is expressed as:\n# * stamp.sec: seconds (stamp_secs) since epoch (in Python the variable is called 'secs')\n# * stamp.nsec: nanoseconds since stamp_secs (in Python the variable is called 'nsecs')\n# time-handling sugar is provided by the client library\ntime stamp\n#Frame this data is associated with\n# 0: no frame\n# 1: global frame\nstring frame_id\n\n================================================================================\nMSG: geometry_msgs/Vector3\n# This represents a vector in free space. \n\nfloat64 x\nfloat64 y\nfloat64 z\n================================================================================\nMSG: geometry_msgs/Quaternion\n# This represents an orientation in free space in quaternion form.\n\nfloat64 x\nfloat64 y\nfloat64 z\nfloat64 w\n\n================================================================================\nMSG: quadrotor_msgs/AuxCommand\nfloat64 current_yaw\nfloat64 kf_correction\nfloat64[2] angle_corrections# Trims for roll, pitch\nbool enable_motors\nbool use_external_yaw\n\n\"\"\"\n  __slots__ = ['header','force','orientation','kR','kOm','aux']\n  _slot_types = ['std_msgs/Header','geometry_msgs/Vector3','geometry_msgs/Quaternion','float64[3]','float64[3]','quadrotor_msgs/AuxCommand']\n\n  def __init__(self, *args, **kwds):\n    \"\"\"\n    Constructor. Any message fields that are implicitly/explicitly\n    set to None will be assigned a default value. The recommend\n    use is keyword arguments as this is more robust to future message\n    changes.  You cannot mix in-order arguments and keyword arguments.\n\n    The available fields are:\n       header,force,orientation,kR,kOm,aux\n\n    :param args: complete set of field values, in .msg order\n    :param kwds: use keyword arguments corresponding to message field names\n    to set specific fields.\n    \"\"\"\n    if args or kwds:\n      super(SO3Command, self).__init__(*args, **kwds)\n      #message fields cannot be None, assign default values for those that are\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      if self.force is None:\n        self.force = geometry_msgs.msg.Vector3()\n      if self.orientation is None:\n        self.orientation = geometry_msgs.msg.Quaternion()\n      if self.kR is None:\n        self.kR = [0.,0.,0.]\n      if self.kOm is None:\n        self.kOm = [0.,0.,0.]\n      if self.aux is None:\n        self.aux = quadrotor_msgs.msg.AuxCommand()\n    else:\n      self.header = std_msgs.msg.Header()\n      self.force = geometry_msgs.msg.Vector3()\n      self.orientation = geometry_msgs.msg.Quaternion()\n      self.kR = [0.,0.,0.]\n      self.kOm = [0.,0.,0.]\n      self.aux = quadrotor_msgs.msg.AuxCommand()\n\n  def _get_types(self):\n    \"\"\"\n    internal API method\n    \"\"\"\n    return self._slot_types\n\n  def serialize(self, buff):\n    \"\"\"\n    serialize message into buffer\n    :param buff: buffer, ``StringIO``\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_3I.pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\n      _x = self.header.frame_id\n      length = len(_x)\n      if python3 or type(_x) == unicode:\n        _x = _x.encode('utf-8')\n        length = len(_x)\n      if python3:\n        buff.write(struct.pack('<I%sB'%length, length, *_x))\n      else:\n        buff.write(struct.pack('<I%ss'%length, length, _x))\n      _x = self\n      buff.write(_struct_7d.pack(_x.force.x, _x.force.y, _x.force.z, _x.orientation.x, _x.orientation.y, _x.orientation.z, _x.orientation.w))\n      buff.write(_struct_3d.pack(*self.kR))\n      buff.write(_struct_3d.pack(*self.kOm))\n      _x = self\n      buff.write(_struct_2d.pack(_x.aux.current_yaw, _x.aux.kf_correction))\n      buff.write(_struct_2d.pack(*self.aux.angle_corrections))\n      _x = self\n      buff.write(_struct_2B.pack(_x.aux.enable_motors, _x.aux.use_external_yaw))\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize(self, str):\n    \"\"\"\n    unpack serialized message in str into this message instance\n    :param str: byte array of serialized message, ``str``\n    \"\"\"\n    try:\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      if self.force is None:\n        self.force = geometry_msgs.msg.Vector3()\n      if self.orientation is None:\n        self.orientation = geometry_msgs.msg.Quaternion()\n      if self.aux is None:\n        self.aux = quadrotor_msgs.msg.AuxCommand()\n      end = 0\n      _x = self\n      start = end\n      end += 12\n      (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\n      start = end\n      end += 4\n      (length,) = _struct_I.unpack(str[start:end])\n      start = end\n      end += length\n      if python3:\n        self.header.frame_id = str[start:end].decode('utf-8')\n      else:\n        self.header.frame_id = str[start:end]\n      _x = self\n      start = end\n      end += 56\n      (_x.force.x, _x.force.y, _x.force.z, _x.orientation.x, _x.orientation.y, _x.orientation.z, _x.orientation.w,) = _struct_7d.unpack(str[start:end])\n      start = end\n      end += 24\n      self.kR = _struct_3d.unpack(str[start:end])\n      start = end\n      end += 24\n      self.kOm = _struct_3d.unpack(str[start:end])\n      _x = self\n      start = end\n      end += 16\n      (_x.aux.current_yaw, _x.aux.kf_correction,) = _struct_2d.unpack(str[start:end])\n      start = end\n      end += 16\n      self.aux.angle_corrections = _struct_2d.unpack(str[start:end])\n      _x = self\n      start = end\n      end += 2\n      (_x.aux.enable_motors, _x.aux.use_external_yaw,) = _struct_2B.unpack(str[start:end])\n      self.aux.enable_motors = bool(self.aux.enable_motors)\n      self.aux.use_external_yaw = bool(self.aux.use_external_yaw)\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n\n  def serialize_numpy(self, buff, numpy):\n    \"\"\"\n    serialize message with numpy array types into buffer\n    :param buff: buffer, ``StringIO``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_3I.pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\n      _x = self.header.frame_id\n      length = len(_x)\n      if python3 or type(_x) == unicode:\n        _x = _x.encode('utf-8')\n        length = len(_x)\n      if python3:\n        buff.write(struct.pack('<I%sB'%length, length, *_x))\n      else:\n        buff.write(struct.pack('<I%ss'%length, length, _x))\n      _x = self\n      buff.write(_struct_7d.pack(_x.force.x, _x.force.y, _x.force.z, _x.orientation.x, _x.orientation.y, _x.orientation.z, _x.orientation.w))\n      buff.write(self.kR.tostring())\n      buff.write(self.kOm.tostring())\n      _x = self\n      buff.write(_struct_2d.pack(_x.aux.current_yaw, _x.aux.kf_correction))\n      buff.write(self.aux.angle_corrections.tostring())\n      _x = self\n      buff.write(_struct_2B.pack(_x.aux.enable_motors, _x.aux.use_external_yaw))\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize_numpy(self, str, numpy):\n    \"\"\"\n    unpack serialized message in str into this message instance using numpy for array types\n    :param str: byte array of serialized message, ``str``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      if self.force is None:\n        self.force = geometry_msgs.msg.Vector3()\n      if self.orientation is None:\n        self.orientation = geometry_msgs.msg.Quaternion()\n      if self.aux is None:\n        self.aux = quadrotor_msgs.msg.AuxCommand()\n      end = 0\n      _x = self\n      start = end\n      end += 12\n      (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\n      start = end\n      end += 4\n      (length,) = _struct_I.unpack(str[start:end])\n      start = end\n      end += length\n      if python3:\n        self.header.frame_id = str[start:end].decode('utf-8')\n      else:\n        self.header.frame_id = str[start:end]\n      _x = self\n      start = end\n      end += 56\n      (_x.force.x, _x.force.y, _x.force.z, _x.orientation.x, _x.orientation.y, _x.orientation.z, _x.orientation.w,) = _struct_7d.unpack(str[start:end])\n      start = end\n      end += 24\n      self.kR = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=3)\n      start = end\n      end += 24\n      self.kOm = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=3)\n      _x = self\n      start = end\n      end += 16\n      (_x.aux.current_yaw, _x.aux.kf_correction,) = _struct_2d.unpack(str[start:end])\n      start = end\n      end += 16\n      self.aux.angle_corrections = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=2)\n      _x = self\n      start = end\n      end += 2\n      (_x.aux.enable_motors, _x.aux.use_external_yaw,) = _struct_2B.unpack(str[start:end])\n      self.aux.enable_motors = bool(self.aux.enable_motors)\n      self.aux.use_external_yaw = bool(self.aux.use_external_yaw)\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n_struct_I = genpy.struct_I\n_struct_2d = struct.Struct(\"<2d\")\n_struct_3I = struct.Struct(\"<3I\")\n_struct_7d = struct.Struct(\"<7d\")\n_struct_2B = struct.Struct(\"<2B\")\n_struct_3d = struct.Struct(\"<3d\")\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/src/quadrotor_msgs/msg/_Serial.py",
    "content": "\"\"\"autogenerated by genpy from quadrotor_msgs/Serial.msg. Do not edit.\"\"\"\nimport sys\npython3 = True if sys.hexversion > 0x03000000 else False\nimport genpy\nimport struct\n\nimport std_msgs.msg\n\nclass Serial(genpy.Message):\n  _md5sum = \"e448fb7595af9a8adfcab5ec241c7d4f\"\n  _type = \"quadrotor_msgs/Serial\"\n  _has_header = True #flag to mark the presence of a Header object\n  _full_text = \"\"\"# Note: These constants need to be kept in sync with the types\n# defined in include/quadrotor_msgs/comm_types.h\nuint8 SO3_CMD = 115 # 's' in base 10\nuint8 TRPY_CMD = 112 # 'p' in base 10\nuint8 STATUS_DATA = 99 # 'c' in base 10\nuint8 OUTPUT_DATA = 100 # 'd' in base 10\nuint8 PPR_OUTPUT_DATA = 116 # 't' in base 10\nuint8 PPR_GAINS = 103 # 'g'\n\nHeader header\nuint8 channel\nuint8 type # One of the types listed above\nuint8[] data\n\n================================================================================\nMSG: std_msgs/Header\n# Standard metadata for higher-level stamped data types.\n# This is generally used to communicate timestamped data \n# in a particular coordinate frame.\n# \n# sequence ID: consecutively increasing ID \nuint32 seq\n#Two-integer timestamp that is expressed as:\n# * stamp.sec: seconds (stamp_secs) since epoch (in Python the variable is called 'secs')\n# * stamp.nsec: nanoseconds since stamp_secs (in Python the variable is called 'nsecs')\n# time-handling sugar is provided by the client library\ntime stamp\n#Frame this data is associated with\n# 0: no frame\n# 1: global frame\nstring frame_id\n\n\"\"\"\n  # Pseudo-constants\n  SO3_CMD = 115\n  TRPY_CMD = 112\n  STATUS_DATA = 99\n  OUTPUT_DATA = 100\n  PPR_OUTPUT_DATA = 116\n  PPR_GAINS = 103\n\n  __slots__ = ['header','channel','type','data']\n  _slot_types = ['std_msgs/Header','uint8','uint8','uint8[]']\n\n  def __init__(self, *args, **kwds):\n    \"\"\"\n    Constructor. Any message fields that are implicitly/explicitly\n    set to None will be assigned a default value. The recommend\n    use is keyword arguments as this is more robust to future message\n    changes.  You cannot mix in-order arguments and keyword arguments.\n\n    The available fields are:\n       header,channel,type,data\n\n    :param args: complete set of field values, in .msg order\n    :param kwds: use keyword arguments corresponding to message field names\n    to set specific fields.\n    \"\"\"\n    if args or kwds:\n      super(Serial, self).__init__(*args, **kwds)\n      #message fields cannot be None, assign default values for those that are\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      if self.channel is None:\n        self.channel = 0\n      if self.type is None:\n        self.type = 0\n      if self.data is None:\n        self.data = ''\n    else:\n      self.header = std_msgs.msg.Header()\n      self.channel = 0\n      self.type = 0\n      self.data = ''\n\n  def _get_types(self):\n    \"\"\"\n    internal API method\n    \"\"\"\n    return self._slot_types\n\n  def serialize(self, buff):\n    \"\"\"\n    serialize message into buffer\n    :param buff: buffer, ``StringIO``\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_3I.pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\n      _x = self.header.frame_id\n      length = len(_x)\n      if python3 or type(_x) == unicode:\n        _x = _x.encode('utf-8')\n        length = len(_x)\n      if python3:\n        buff.write(struct.pack('<I%sB'%length, length, *_x))\n      else:\n        buff.write(struct.pack('<I%ss'%length, length, _x))\n      _x = self\n      buff.write(_struct_2B.pack(_x.channel, _x.type))\n      _x = self.data\n      length = len(_x)\n      # - if encoded as a list instead, serialize as bytes instead of string\n      if type(_x) in [list, tuple]:\n        buff.write(struct.pack('<I%sB'%length, length, *_x))\n      else:\n        buff.write(struct.pack('<I%ss'%length, length, _x))\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize(self, str):\n    \"\"\"\n    unpack serialized message in str into this message instance\n    :param str: byte array of serialized message, ``str``\n    \"\"\"\n    try:\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      end = 0\n      _x = self\n      start = end\n      end += 12\n      (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\n      start = end\n      end += 4\n      (length,) = _struct_I.unpack(str[start:end])\n      start = end\n      end += length\n      if python3:\n        self.header.frame_id = str[start:end].decode('utf-8')\n      else:\n        self.header.frame_id = str[start:end]\n      _x = self\n      start = end\n      end += 2\n      (_x.channel, _x.type,) = _struct_2B.unpack(str[start:end])\n      start = end\n      end += 4\n      (length,) = _struct_I.unpack(str[start:end])\n      start = end\n      end += length\n      self.data = str[start:end]\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n\n  def serialize_numpy(self, buff, numpy):\n    \"\"\"\n    serialize message with numpy array types into buffer\n    :param buff: buffer, ``StringIO``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_3I.pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\n      _x = self.header.frame_id\n      length = len(_x)\n      if python3 or type(_x) == unicode:\n        _x = _x.encode('utf-8')\n        length = len(_x)\n      if python3:\n        buff.write(struct.pack('<I%sB'%length, length, *_x))\n      else:\n        buff.write(struct.pack('<I%ss'%length, length, _x))\n      _x = self\n      buff.write(_struct_2B.pack(_x.channel, _x.type))\n      _x = self.data\n      length = len(_x)\n      # - if encoded as a list instead, serialize as bytes instead of string\n      if type(_x) in [list, tuple]:\n        buff.write(struct.pack('<I%sB'%length, length, *_x))\n      else:\n        buff.write(struct.pack('<I%ss'%length, length, _x))\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize_numpy(self, str, numpy):\n    \"\"\"\n    unpack serialized message in str into this message instance using numpy for array types\n    :param str: byte array of serialized message, ``str``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      end = 0\n      _x = self\n      start = end\n      end += 12\n      (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\n      start = end\n      end += 4\n      (length,) = _struct_I.unpack(str[start:end])\n      start = end\n      end += length\n      if python3:\n        self.header.frame_id = str[start:end].decode('utf-8')\n      else:\n        self.header.frame_id = str[start:end]\n      _x = self\n      start = end\n      end += 2\n      (_x.channel, _x.type,) = _struct_2B.unpack(str[start:end])\n      start = end\n      end += 4\n      (length,) = _struct_I.unpack(str[start:end])\n      start = end\n      end += length\n      self.data = str[start:end]\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n_struct_I = genpy.struct_I\n_struct_3I = struct.Struct(\"<3I\")\n_struct_2B = struct.Struct(\"<2B\")\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/src/quadrotor_msgs/msg/_StatusData.py",
    "content": "\"\"\"autogenerated by genpy from quadrotor_msgs/StatusData.msg. Do not edit.\"\"\"\nimport sys\npython3 = True if sys.hexversion > 0x03000000 else False\nimport genpy\nimport struct\n\nimport std_msgs.msg\n\nclass StatusData(genpy.Message):\n  _md5sum = \"c70a4ec4725273263ae176ad30f89553\"\n  _type = \"quadrotor_msgs/StatusData\"\n  _has_header = True #flag to mark the presence of a Header object\n  _full_text = \"\"\"Header header\nuint16 loop_rate\nfloat64 voltage\nuint8 seq\n\n================================================================================\nMSG: std_msgs/Header\n# Standard metadata for higher-level stamped data types.\n# This is generally used to communicate timestamped data \n# in a particular coordinate frame.\n# \n# sequence ID: consecutively increasing ID \nuint32 seq\n#Two-integer timestamp that is expressed as:\n# * stamp.sec: seconds (stamp_secs) since epoch (in Python the variable is called 'secs')\n# * stamp.nsec: nanoseconds since stamp_secs (in Python the variable is called 'nsecs')\n# time-handling sugar is provided by the client library\ntime stamp\n#Frame this data is associated with\n# 0: no frame\n# 1: global frame\nstring frame_id\n\n\"\"\"\n  __slots__ = ['header','loop_rate','voltage','seq']\n  _slot_types = ['std_msgs/Header','uint16','float64','uint8']\n\n  def __init__(self, *args, **kwds):\n    \"\"\"\n    Constructor. Any message fields that are implicitly/explicitly\n    set to None will be assigned a default value. The recommend\n    use is keyword arguments as this is more robust to future message\n    changes.  You cannot mix in-order arguments and keyword arguments.\n\n    The available fields are:\n       header,loop_rate,voltage,seq\n\n    :param args: complete set of field values, in .msg order\n    :param kwds: use keyword arguments corresponding to message field names\n    to set specific fields.\n    \"\"\"\n    if args or kwds:\n      super(StatusData, self).__init__(*args, **kwds)\n      #message fields cannot be None, assign default values for those that are\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      if self.loop_rate is None:\n        self.loop_rate = 0\n      if self.voltage is None:\n        self.voltage = 0.\n      if self.seq is None:\n        self.seq = 0\n    else:\n      self.header = std_msgs.msg.Header()\n      self.loop_rate = 0\n      self.voltage = 0.\n      self.seq = 0\n\n  def _get_types(self):\n    \"\"\"\n    internal API method\n    \"\"\"\n    return self._slot_types\n\n  def serialize(self, buff):\n    \"\"\"\n    serialize message into buffer\n    :param buff: buffer, ``StringIO``\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_3I.pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\n      _x = self.header.frame_id\n      length = len(_x)\n      if python3 or type(_x) == unicode:\n        _x = _x.encode('utf-8')\n        length = len(_x)\n      if python3:\n        buff.write(struct.pack('<I%sB'%length, length, *_x))\n      else:\n        buff.write(struct.pack('<I%ss'%length, length, _x))\n      _x = self\n      buff.write(_struct_HdB.pack(_x.loop_rate, _x.voltage, _x.seq))\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize(self, str):\n    \"\"\"\n    unpack serialized message in str into this message instance\n    :param str: byte array of serialized message, ``str``\n    \"\"\"\n    try:\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      end = 0\n      _x = self\n      start = end\n      end += 12\n      (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\n      start = end\n      end += 4\n      (length,) = _struct_I.unpack(str[start:end])\n      start = end\n      end += length\n      if python3:\n        self.header.frame_id = str[start:end].decode('utf-8')\n      else:\n        self.header.frame_id = str[start:end]\n      _x = self\n      start = end\n      end += 11\n      (_x.loop_rate, _x.voltage, _x.seq,) = _struct_HdB.unpack(str[start:end])\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n\n  def serialize_numpy(self, buff, numpy):\n    \"\"\"\n    serialize message with numpy array types into buffer\n    :param buff: buffer, ``StringIO``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_3I.pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\n      _x = self.header.frame_id\n      length = len(_x)\n      if python3 or type(_x) == unicode:\n        _x = _x.encode('utf-8')\n        length = len(_x)\n      if python3:\n        buff.write(struct.pack('<I%sB'%length, length, *_x))\n      else:\n        buff.write(struct.pack('<I%ss'%length, length, _x))\n      _x = self\n      buff.write(_struct_HdB.pack(_x.loop_rate, _x.voltage, _x.seq))\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize_numpy(self, str, numpy):\n    \"\"\"\n    unpack serialized message in str into this message instance using numpy for array types\n    :param str: byte array of serialized message, ``str``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      end = 0\n      _x = self\n      start = end\n      end += 12\n      (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\n      start = end\n      end += 4\n      (length,) = _struct_I.unpack(str[start:end])\n      start = end\n      end += length\n      if python3:\n        self.header.frame_id = str[start:end].decode('utf-8')\n      else:\n        self.header.frame_id = str[start:end]\n      _x = self\n      start = end\n      end += 11\n      (_x.loop_rate, _x.voltage, _x.seq,) = _struct_HdB.unpack(str[start:end])\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n_struct_I = genpy.struct_I\n_struct_3I = struct.Struct(\"<3I\")\n_struct_HdB = struct.Struct(\"<HdB\")\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/src/quadrotor_msgs/msg/_TRPYCommand.py",
    "content": "\"\"\"autogenerated by genpy from quadrotor_msgs/TRPYCommand.msg. Do not edit.\"\"\"\nimport sys\npython3 = True if sys.hexversion > 0x03000000 else False\nimport genpy\nimport struct\n\nimport quadrotor_msgs.msg\nimport std_msgs.msg\n\nclass TRPYCommand(genpy.Message):\n  _md5sum = \"6779ee8a86cc757aeea21725df32d00c\"\n  _type = \"quadrotor_msgs/TRPYCommand\"\n  _has_header = True #flag to mark the presence of a Header object\n  _full_text = \"\"\"Header header\nfloat32 thrust\nfloat32 roll\nfloat32 pitch\nfloat32 yaw\nquadrotor_msgs/AuxCommand aux\n\n================================================================================\nMSG: std_msgs/Header\n# Standard metadata for higher-level stamped data types.\n# This is generally used to communicate timestamped data \n# in a particular coordinate frame.\n# \n# sequence ID: consecutively increasing ID \nuint32 seq\n#Two-integer timestamp that is expressed as:\n# * stamp.sec: seconds (stamp_secs) since epoch (in Python the variable is called 'secs')\n# * stamp.nsec: nanoseconds since stamp_secs (in Python the variable is called 'nsecs')\n# time-handling sugar is provided by the client library\ntime stamp\n#Frame this data is associated with\n# 0: no frame\n# 1: global frame\nstring frame_id\n\n================================================================================\nMSG: quadrotor_msgs/AuxCommand\nfloat64 current_yaw\nfloat64 kf_correction\nfloat64[2] angle_corrections# Trims for roll, pitch\nbool enable_motors\nbool use_external_yaw\n\n\"\"\"\n  __slots__ = ['header','thrust','roll','pitch','yaw','aux']\n  _slot_types = ['std_msgs/Header','float32','float32','float32','float32','quadrotor_msgs/AuxCommand']\n\n  def __init__(self, *args, **kwds):\n    \"\"\"\n    Constructor. Any message fields that are implicitly/explicitly\n    set to None will be assigned a default value. The recommend\n    use is keyword arguments as this is more robust to future message\n    changes.  You cannot mix in-order arguments and keyword arguments.\n\n    The available fields are:\n       header,thrust,roll,pitch,yaw,aux\n\n    :param args: complete set of field values, in .msg order\n    :param kwds: use keyword arguments corresponding to message field names\n    to set specific fields.\n    \"\"\"\n    if args or kwds:\n      super(TRPYCommand, self).__init__(*args, **kwds)\n      #message fields cannot be None, assign default values for those that are\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      if self.thrust is None:\n        self.thrust = 0.\n      if self.roll is None:\n        self.roll = 0.\n      if self.pitch is None:\n        self.pitch = 0.\n      if self.yaw is None:\n        self.yaw = 0.\n      if self.aux is None:\n        self.aux = quadrotor_msgs.msg.AuxCommand()\n    else:\n      self.header = std_msgs.msg.Header()\n      self.thrust = 0.\n      self.roll = 0.\n      self.pitch = 0.\n      self.yaw = 0.\n      self.aux = quadrotor_msgs.msg.AuxCommand()\n\n  def _get_types(self):\n    \"\"\"\n    internal API method\n    \"\"\"\n    return self._slot_types\n\n  def serialize(self, buff):\n    \"\"\"\n    serialize message into buffer\n    :param buff: buffer, ``StringIO``\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_3I.pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\n      _x = self.header.frame_id\n      length = len(_x)\n      if python3 or type(_x) == unicode:\n        _x = _x.encode('utf-8')\n        length = len(_x)\n      if python3:\n        buff.write(struct.pack('<I%sB'%length, length, *_x))\n      else:\n        buff.write(struct.pack('<I%ss'%length, length, _x))\n      _x = self\n      buff.write(_struct_4f2d.pack(_x.thrust, _x.roll, _x.pitch, _x.yaw, _x.aux.current_yaw, _x.aux.kf_correction))\n      buff.write(_struct_2d.pack(*self.aux.angle_corrections))\n      _x = self\n      buff.write(_struct_2B.pack(_x.aux.enable_motors, _x.aux.use_external_yaw))\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize(self, str):\n    \"\"\"\n    unpack serialized message in str into this message instance\n    :param str: byte array of serialized message, ``str``\n    \"\"\"\n    try:\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      if self.aux is None:\n        self.aux = quadrotor_msgs.msg.AuxCommand()\n      end = 0\n      _x = self\n      start = end\n      end += 12\n      (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\n      start = end\n      end += 4\n      (length,) = _struct_I.unpack(str[start:end])\n      start = end\n      end += length\n      if python3:\n        self.header.frame_id = str[start:end].decode('utf-8')\n      else:\n        self.header.frame_id = str[start:end]\n      _x = self\n      start = end\n      end += 32\n      (_x.thrust, _x.roll, _x.pitch, _x.yaw, _x.aux.current_yaw, _x.aux.kf_correction,) = _struct_4f2d.unpack(str[start:end])\n      start = end\n      end += 16\n      self.aux.angle_corrections = _struct_2d.unpack(str[start:end])\n      _x = self\n      start = end\n      end += 2\n      (_x.aux.enable_motors, _x.aux.use_external_yaw,) = _struct_2B.unpack(str[start:end])\n      self.aux.enable_motors = bool(self.aux.enable_motors)\n      self.aux.use_external_yaw = bool(self.aux.use_external_yaw)\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n\n  def serialize_numpy(self, buff, numpy):\n    \"\"\"\n    serialize message with numpy array types into buffer\n    :param buff: buffer, ``StringIO``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      _x = self\n      buff.write(_struct_3I.pack(_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs))\n      _x = self.header.frame_id\n      length = len(_x)\n      if python3 or type(_x) == unicode:\n        _x = _x.encode('utf-8')\n        length = len(_x)\n      if python3:\n        buff.write(struct.pack('<I%sB'%length, length, *_x))\n      else:\n        buff.write(struct.pack('<I%ss'%length, length, _x))\n      _x = self\n      buff.write(_struct_4f2d.pack(_x.thrust, _x.roll, _x.pitch, _x.yaw, _x.aux.current_yaw, _x.aux.kf_correction))\n      buff.write(self.aux.angle_corrections.tostring())\n      _x = self\n      buff.write(_struct_2B.pack(_x.aux.enable_motors, _x.aux.use_external_yaw))\n    except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(_x))))\n    except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(_x))))\n\n  def deserialize_numpy(self, str, numpy):\n    \"\"\"\n    unpack serialized message in str into this message instance using numpy for array types\n    :param str: byte array of serialized message, ``str``\n    :param numpy: numpy python module\n    \"\"\"\n    try:\n      if self.header is None:\n        self.header = std_msgs.msg.Header()\n      if self.aux is None:\n        self.aux = quadrotor_msgs.msg.AuxCommand()\n      end = 0\n      _x = self\n      start = end\n      end += 12\n      (_x.header.seq, _x.header.stamp.secs, _x.header.stamp.nsecs,) = _struct_3I.unpack(str[start:end])\n      start = end\n      end += 4\n      (length,) = _struct_I.unpack(str[start:end])\n      start = end\n      end += length\n      if python3:\n        self.header.frame_id = str[start:end].decode('utf-8')\n      else:\n        self.header.frame_id = str[start:end]\n      _x = self\n      start = end\n      end += 32\n      (_x.thrust, _x.roll, _x.pitch, _x.yaw, _x.aux.current_yaw, _x.aux.kf_correction,) = _struct_4f2d.unpack(str[start:end])\n      start = end\n      end += 16\n      self.aux.angle_corrections = numpy.frombuffer(str[start:end], dtype=numpy.float64, count=2)\n      _x = self\n      start = end\n      end += 2\n      (_x.aux.enable_motors, _x.aux.use_external_yaw,) = _struct_2B.unpack(str[start:end])\n      self.aux.enable_motors = bool(self.aux.enable_motors)\n      self.aux.use_external_yaw = bool(self.aux.use_external_yaw)\n      return self\n    except struct.error as e:\n      raise genpy.DeserializationError(e) #most likely buffer underfill\n\n_struct_I = genpy.struct_I\n_struct_2d = struct.Struct(\"<2d\")\n_struct_3I = struct.Struct(\"<3I\")\n_struct_4f2d = struct.Struct(\"<4f2d\")\n_struct_2B = struct.Struct(\"<2B\")\n"
  },
  {
    "path": "src/uav_simulator/quadrotor_msgs/src/quadrotor_msgs/msg/__init__.py",
    "content": "from ._Gains import *\nfrom ._SO3Command import *\nfrom ._TRPYCommand import *\nfrom ._PositionCommand import *\nfrom ._PPROutputData import *\nfrom ._OutputData import *\nfrom ._Corrections import *\nfrom ._Serial import *\nfrom ._AuxCommand import *\nfrom ._StatusData import *\n"
  },
  {
    "path": "src/uav_simulator/so3_controller/CMakeLists.txt",
    "content": "cmake_minimum_required(VERSION 3.0.2)\nproject(so3_controller)\n\nadd_compile_options(-std=c++17)\nset(CMAKE_BUILD_TYPE \"Release\")\nset(CMAKE_CXX_FLAGS_RELEASE \"-O3 -Wall\")\n\nfind_package(Eigen3 REQUIRED)\nfind_package(catkin REQUIRED COMPONENTS\n  quadrotor_msgs\n  roscpp\n  nodelet\n)\n\ncatkin_package(\n  INCLUDE_DIRS include ${EIGEN3_INCLUDE_DIR}\n  CATKIN_DEPENDS quadrotor_msgs roscpp nodelet\n  DEPENDS Eigen3 REQUIRED\n)\n\ninclude_directories(\n  include\n  ${EIGEN3_INCLUDE_DIR}\n  ${catkin_INCLUDE_DIRS}\n)\n\nadd_library(${PROJECT_NAME}_nodelet\n  src/${PROJECT_NAME}_nodelet.cpp\n)\n\ntarget_link_libraries(${PROJECT_NAME}_nodelet \n  ${catkin_LIBRARIES}\n)"
  },
  {
    "path": "src/uav_simulator/so3_controller/include/so3_controller/so3_controller.hpp",
    "content": "#pragma once\n#include <Eigen/Geometry>\n\nnamespace so3_controller {\n\nclass SO3Controller {\n private:\n  // input\n  double          mass_;\n  double          g_;\n  Eigen::Vector3d pos_;\n  Eigen::Vector3d vel_;\n  Eigen::Vector3d acc_;\n  // output\n  Eigen::Vector3d    force_;\n  Eigen::Quaterniond quat_;\n public:\n  SO3Controller(const double& mass, const double& g) : \n    mass_(mass), g_(g) {};\n  ~SO3Controller() {};\n  // update state from sensors\n  inline void setPos(const Eigen::Vector3d& pos) {\n    pos_ = pos;\n  }\n  inline void setVel(const Eigen::Vector3d& vel) {\n    vel_ = vel;\n  }\n  inline void setAcc(const Eigen::Vector3d& acc) {\n    acc_ = acc;\n  }\n  inline Eigen::Vector3d getF() const {\n    return force_;\n  }\n  inline Eigen::Quaterniond getQ() const {\n    return quat_;\n  }\n  // pid\n  inline void calculateControl(const Eigen::Vector3d& des_pos,\n                               const Eigen::Vector3d& des_vel,\n                               const Eigen::Vector3d& des_acc,\n                               const double& des_yaw, const double& des_yaw_dot,\n                               const Eigen::Vector3d& kx,\n                               const Eigen::Vector3d& kv) {\n    Eigen::Vector3d totalError = (des_pos - pos_) + (des_vel - vel_) + (des_acc - acc_);\n    Eigen::Vector3d ka(fabs(totalError[0]) > 3 ? 0 : (fabs(totalError[0]) * 0.2),\n                       fabs(totalError[1]) > 3 ? 0 : (fabs(totalError[1]) * 0.2),\n                       fabs(totalError[2]) > 3 ? 0 : (fabs(totalError[2]) * 0.2));\n    force_.noalias() =\n      kx.asDiagonal() * (des_pos - pos_) + kv.asDiagonal() * (des_vel - vel_) +\n      mass_ * /*(Eigen::Vector3d(1, 1, 1) - ka).asDiagonal() **/ (des_acc) +\n      mass_ * ka.asDiagonal() * (des_acc - acc_) +\n      mass_ * g_ * Eigen::Vector3d(0, 0, 1);\n    // Limit control angle to 45 degree\n    double          theta = M_PI / 2;\n    double          c     = cos(theta);\n    Eigen::Vector3d f;\n    f.noalias() = kx.asDiagonal() * (des_pos - pos_) +\n                kv.asDiagonal() * (des_vel - vel_) + //\n                mass_ * des_acc +                    //\n                mass_ * ka.asDiagonal() * (des_acc - acc_);\n    if (Eigen::Vector3d(0, 0, 1).dot(force_ / force_.norm()) < c)\n    {\n      double nf        = f.norm();\n      double A         = c * c * nf * nf - f(2) * f(2);\n      double B         = 2 * (c * c - 1) * f(2) * mass_ * g_;\n      double C         = (c * c - 1) * mass_ * mass_ * g_ * g_;\n      double s         = (-B + sqrt(B * B - 4 * A * C)) / (2 * A);\n      force_.noalias() = s * f + mass_ * g_ * Eigen::Vector3d(0, 0, 1);\n    }\n    // Limit control angle to 45 degree\n\n    Eigen::Vector3d b1c, b2c, b3c;\n    Eigen::Vector3d b1d(cos(des_yaw), sin(des_yaw), 0);\n\n    if (force_.norm() > 1e-6)\n      b3c.noalias() = force_.normalized();\n    else\n      b3c.noalias() = Eigen::Vector3d(0, 0, 1);\n\n    b2c.noalias() = b3c.cross(b1d).normalized();\n    b1c.noalias() = b2c.cross(b3c).normalized();\n\n    Eigen::Matrix3d R;\n    R << b1c, b2c, b3c;\n\n    quat_ = Eigen::Quaterniond(R);\n  }\n};\n\n} // namespace so3_controller\n"
  },
  {
    "path": "src/uav_simulator/so3_controller/nodelet_plugin.xml",
    "content": "<library path=\"lib/libso3_controller_nodelet\">\n  <class name=\"so3_controller/Nodelet\" type=\"so3_controller::Nodelet\" base_class_type=\"nodelet::Nodelet\">\n    <description></description>\n  </class>\n</library>\n"
  },
  {
    "path": "src/uav_simulator/so3_controller/package.xml",
    "content": "<?xml version=\"1.0\"?>\n<package format=\"2\">\n  <name>so3_controller</name>\n  <version>0.0.0</version>\n  <description>The so3_controller package</description>\n\n  <maintainer email=\"jlji@zju.edu.cn\">ji</maintainer>\n\n  <license>GPLv3</license>\n\n  <buildtool_depend>catkin</buildtool_depend>\n  <depend>quadrotor_msgs</depend>\n  <depend>roscpp</depend>\n  <depend>nodelet</depend>\n\n  <export>\n    <nodelet plugin=\"${prefix}/nodelet_plugin.xml\"/>\n  </export>\n</package>\n"
  },
  {
    "path": "src/uav_simulator/so3_controller/src/so3_controller_nodelet.cpp",
    "content": "#include \"so3_controller/so3_controller.hpp\"\n#include <quadrotor_msgs/PositionCommand.h>\n#include <quadrotor_msgs/SO3Command.h>\n#include <nav_msgs/Odometry.h>\n#include <sensor_msgs/Imu.h>\n#include <tf/transform_datatypes.h>\n#include <nodelet/nodelet.h>\n#include <ros/ros.h>\n\nnamespace so3_controller {\nclass Nodelet : public nodelet::Nodelet{\n private:\n  std::shared_ptr<SO3Controller> so3ControlPtr_;\n  ros::Subscriber position_cmd_sub_, odom_sub_, imu_sub_;\n  ros::Publisher  so3cmd_pub_;\n  ros::Timer state_timer_;\n  quadrotor_msgs::SO3Command so3cmd_;\n\n  bool position_cmd_received_flag_ = false;\n  Eigen::Vector3d des_pos_;\n  double des_yaw_;\n\n  void timer_callback(const ros::TimerEvent& event) {\n    if (position_cmd_received_flag_) {\n      position_cmd_received_flag_ = false;\n    } else {\n      Eigen::Vector3d des_vel(0, 0, 0);\n      Eigen::Vector3d des_acc(0, 0, 0);\n      Eigen::Vector3d kx(5.7, 5.7, 6.2);\n      Eigen::Vector3d kv(3.4, 3.4, 4.0);\n      so3ControlPtr_->calculateControl(des_pos_, des_vel, des_acc, \n                                       des_yaw_, 0, kx, kv);\n      const Eigen::Vector3d& f = so3ControlPtr_->getF();\n      const Eigen::Quaterniond& q = so3ControlPtr_->getQ();\n      so3cmd_.force.x = f(0);\n      so3cmd_.force.y = f(1);\n      so3cmd_.force.z = f(2);\n      so3cmd_.orientation.x = q.x();\n      so3cmd_.orientation.y = q.y();\n      so3cmd_.orientation.z = q.z();\n      so3cmd_.orientation.w = q.w();\n      so3cmd_pub_.publish(so3cmd_);\n    }\n  }\n\n  void position_cmd_callback(const quadrotor_msgs::PositionCommand::ConstPtr& msg) {\n    position_cmd_received_flag_ = true;\n    Eigen::Vector3d des_pos(msg->position.x, msg->position.y, msg->position.z);\n    Eigen::Vector3d des_vel(msg->velocity.x, msg->velocity.y, msg->velocity.z);\n    Eigen::Vector3d des_acc(msg->acceleration.x, msg->acceleration.y, msg->acceleration.z);\n    Eigen::Vector3d kx(msg->kx[0], msg->kx[1], msg->kx[2]);\n    Eigen::Vector3d kv(msg->kv[0], msg->kv[1], msg->kv[2]);\n    if (msg->kx[0] == 0) {\n      kx(0) = 5.7; kx(1) = 5.7; kx(2) = 6.2;\n      kv(0) = 3.4; kv(1) = 3.4; kv(2) = 4.0;\n    }\n    double des_yaw = msg->yaw;\n    double des_yaw_dot = msg->yaw_dot;\n    so3ControlPtr_->calculateControl(des_pos, des_vel, des_acc, \n                                     des_yaw, des_yaw_dot, kx, kv);\n    const Eigen::Vector3d& f = so3ControlPtr_->getF();\n    const Eigen::Quaterniond& q = so3ControlPtr_->getQ();\n    so3cmd_.force.x = f(0);\n    so3cmd_.force.y = f(1);\n    so3cmd_.force.z = f(2);\n    so3cmd_.orientation.x = q.x();\n    so3cmd_.orientation.y = q.y();\n    so3cmd_.orientation.z = q.z();\n    so3cmd_.orientation.w = q.w();\n    so3cmd_pub_.publish(so3cmd_);\n    // store last des_pos and des_yaw\n    des_pos_ = des_pos;\n    des_yaw_ = des_yaw;\n  }\n  void odom_callback(const nav_msgs::Odometry::ConstPtr& msg) {\n    Eigen::Vector3d pos(msg->pose.pose.position.x, \n                        msg->pose.pose.position.y,\n                        msg->pose.pose.position.z);\n    Eigen::Vector3d vel(msg->twist.twist.linear.x,\n                        msg->twist.twist.linear.y,\n                        msg->twist.twist.linear.z);\n    so3ControlPtr_->setPos(pos);\n    so3ControlPtr_->setVel(vel);\n    so3cmd_.aux.current_yaw = tf::getYaw(msg->pose.pose.orientation);\n    des_pos_ = pos;\n    des_yaw_ = tf::getYaw(msg->pose.pose.orientation);\n  }\n  void imu_callback(const sensor_msgs::Imu::ConstPtr& msg) {\n    Eigen::Vector3d acc(msg->linear_acceleration.x,\n                        msg->linear_acceleration.y,\n                        msg->linear_acceleration.z);\n    so3ControlPtr_->setAcc(acc);\n  }\n public:\n  void onInit(void) {\n    ros::NodeHandle nh(getPrivateNodeHandle());\n    // parameters\n    double mass, g;\n    nh.getParam(\"mass\", mass);\n    nh.getParam(\"g\", g);\n    so3ControlPtr_ = std::make_shared<SO3Controller>(mass, g);\n    so3cmd_.header.frame_id = \"world\";\n    nh.getParam(\"gains/rot/x\", so3cmd_.kR[0]);\n    nh.getParam(\"gains/rot/y\", so3cmd_.kR[1]);\n    nh.getParam(\"gains/rot/z\", so3cmd_.kR[2]);\n    nh.getParam(\"gains/ang/x\", so3cmd_.kOm[0]);\n    nh.getParam(\"gains/ang/y\", so3cmd_.kOm[1]);\n    nh.getParam(\"gains/ang/z\", so3cmd_.kOm[2]);\n    nh.getParam(\"corrections/z\", so3cmd_.aux.kf_correction);\n    nh.getParam(\"corrections/r\", so3cmd_.aux.angle_corrections[0]);\n    nh.getParam(\"corrections/p\", so3cmd_.aux.angle_corrections[1]);\n    so3cmd_.aux.enable_motors = true;\n    so3cmd_.aux.use_external_yaw = false;\n    position_cmd_sub_ = nh.subscribe(\"position_cmd\", 10, &Nodelet::position_cmd_callback, \n                                     this, ros::TransportHints().tcpNoDelay());\n    odom_sub_ = nh.subscribe(\"odom\", 10, &Nodelet::odom_callback, this, ros::TransportHints().tcpNoDelay());\n    imu_sub_ = nh.subscribe(\"imu\", 10, &Nodelet::imu_callback, this, ros::TransportHints().tcpNoDelay());\n    so3cmd_pub_ = nh.advertise<quadrotor_msgs::SO3Command>(\"so3cmd\", 10);\n    state_timer_ = nh.createTimer(ros::Duration(0.1), &Nodelet::timer_callback, this);\n  }\n};\n\n} // so3_controller\n\n#include <pluginlib/class_list_macros.h>\nPLUGINLIB_EXPORT_CLASS(so3_controller::Nodelet, nodelet::Nodelet);\n"
  },
  {
    "path": "src/uav_simulator/so3_quadrotor/CMakeLists.txt",
    "content": "cmake_minimum_required(VERSION 3.0.2)\nproject(so3_quadrotor)\n\nadd_compile_options(-std=c++17)\nset(CMAKE_BUILD_TYPE \"Release\")\nset(CMAKE_CXX_FLAGS_RELEASE \"-O3 -Wall\")\n\nfind_package(Eigen3 REQUIRED)\nfind_package(catkin REQUIRED COMPONENTS\n  quadrotor_msgs\n  roscpp\n  nodelet\n  tf\n)\n\ncatkin_package(\n  INCLUDE_DIRS include ${EIGEN3_INCLUDE_DIR}\n  CATKIN_DEPENDS quadrotor_msgs roscpp nodelet tf\n  DEPENDS Eigen3 REQUIRED\n)\n\ninclude_directories(\n  include\n  ${EIGEN3_INCLUDE_DIR}\n  ${catkin_INCLUDE_DIRS}\n)\n\nadd_library(${PROJECT_NAME}_nodelet\n  src/${PROJECT_NAME}_nodelet.cpp\n)\n\ntarget_link_libraries(${PROJECT_NAME}_nodelet\n  ${catkin_LIBRARIES}\n)"
  },
  {
    "path": "src/uav_simulator/so3_quadrotor/include/so3_quadrotor/geometry_utils.hpp",
    "content": "#pragma once\n#include <Eigen/Geometry>\n\nnamespace uav_utils {\n\ntemplate <typename Scalar_t>\nScalar_t toRad(const Scalar_t& x) {\n  return x / 180.0 * M_PI;\n}\n\ntemplate <typename Scalar_t>\nScalar_t toDeg(const Scalar_t& x) {\n  return x * 180.0 / M_PI;\n}\n\ntemplate <typename Scalar_t>\nEigen::Matrix<Scalar_t, 3, 3> rotx(Scalar_t t) {\n  Eigen::Matrix<Scalar_t, 3, 3> R;\n  R(0, 0) = 1.0;\n  R(0, 1) = 0.0;\n  R(0, 2) = 0.0;\n  R(1, 0) = 0.0;\n  R(1, 1) = std::cos(t);\n  R(1, 2) = -std::sin(t);\n  R(2, 0) = 0.0;\n  R(2, 1) = std::sin(t);\n  R(2, 2) = std::cos(t);\n\n  return R;\n}\n\ntemplate <typename Scalar_t>\nEigen::Matrix<Scalar_t, 3, 3> roty(Scalar_t t) {\n  Eigen::Matrix<Scalar_t, 3, 3> R;\n  R(0, 0) = std::cos(t);\n  R(0, 1) = 0.0;\n  R(0, 2) = std::sin(t);\n  R(1, 0) = 0.0;\n  R(1, 1) = 1.0;\n  R(1, 2) = 0;\n  R(2, 0) = -std::sin(t);\n  R(2, 1) = 0.0;\n  R(2, 2) = std::cos(t);\n\n  return R;\n}\n\ntemplate <typename Scalar_t>\nEigen::Matrix<Scalar_t, 3, 3> rotz(Scalar_t t) {\n  Eigen::Matrix<Scalar_t, 3, 3> R;\n  R(0, 0) = std::cos(t);\n  R(0, 1) = -std::sin(t);\n  R(0, 2) = 0.0;\n  R(1, 0) = std::sin(t);\n  R(1, 1) = std::cos(t);\n  R(1, 2) = 0.0;\n  R(2, 0) = 0.0;\n  R(2, 1) = 0.0;\n  R(2, 2) = 1.0;\n\n  return R;\n}\n\ntemplate <typename Derived>\nEigen::Matrix<typename Derived::Scalar, 3, 3> ypr_to_R(const Eigen::DenseBase<Derived>& ypr) {\n  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived);\n  EIGEN_STATIC_ASSERT(Derived::RowsAtCompileTime == 3, THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE);\n  EIGEN_STATIC_ASSERT(Derived::ColsAtCompileTime == 1, THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE);\n\n  typename Derived::Scalar c, s;\n\n  Eigen::Matrix<typename Derived::Scalar, 3, 3> Rz = Eigen::Matrix<typename Derived::Scalar, 3, 3>::Zero();\n  typename Derived::Scalar y = ypr(0);\n  c = cos(y);\n  s = sin(y);\n  Rz(0, 0) = c;\n  Rz(1, 0) = s;\n  Rz(0, 1) = -s;\n  Rz(1, 1) = c;\n  Rz(2, 2) = 1;\n\n  Eigen::Matrix<typename Derived::Scalar, 3, 3> Ry = Eigen::Matrix<typename Derived::Scalar, 3, 3>::Zero();\n  typename Derived::Scalar p = ypr(1);\n  c = cos(p);\n  s = sin(p);\n  Ry(0, 0) = c;\n  Ry(2, 0) = -s;\n  Ry(0, 2) = s;\n  Ry(2, 2) = c;\n  Ry(1, 1) = 1;\n\n  Eigen::Matrix<typename Derived::Scalar, 3, 3> Rx = Eigen::Matrix<typename Derived::Scalar, 3, 3>::Zero();\n  typename Derived::Scalar r = ypr(2);\n  c = cos(r);\n  s = sin(r);\n  Rx(1, 1) = c;\n  Rx(2, 1) = s;\n  Rx(1, 2) = -s;\n  Rx(2, 2) = c;\n  Rx(0, 0) = 1;\n\n  Eigen::Matrix<typename Derived::Scalar, 3, 3> R = Rz * Ry * Rx;\n  return R;\n}\n\ntemplate <typename Derived>\nEigen::Matrix<typename Derived::Scalar, 3, 1> R_to_ypr(const Eigen::DenseBase<Derived>& R) {\n  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived);\n  EIGEN_STATIC_ASSERT(Derived::RowsAtCompileTime == 3, THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE);\n  EIGEN_STATIC_ASSERT(Derived::ColsAtCompileTime == 3, THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE);\n\n  Eigen::Matrix<typename Derived::Scalar, 3, 1> n = R.col(0);\n  Eigen::Matrix<typename Derived::Scalar, 3, 1> o = R.col(1);\n  Eigen::Matrix<typename Derived::Scalar, 3, 1> a = R.col(2);\n\n  Eigen::Matrix<typename Derived::Scalar, 3, 1> ypr(3);\n  typename Derived::Scalar y = atan2(n(1), n(0));\n  typename Derived::Scalar p = atan2(-n(2), n(0) * cos(y) + n(1) * sin(y));\n  typename Derived::Scalar r =\n      atan2(a(0) * sin(y) - a(1) * cos(y), -o(0) * sin(y) + o(1) * cos(y));\n  ypr(0) = y;\n  ypr(1) = p;\n  ypr(2) = r;\n\n  return ypr;\n}\n\ntemplate <typename Derived>\nEigen::Quaternion<typename Derived::Scalar> ypr_to_quaternion(const Eigen::DenseBase<Derived>& ypr) {\n  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived);\n  EIGEN_STATIC_ASSERT(Derived::RowsAtCompileTime == 3, THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE);\n  EIGEN_STATIC_ASSERT(Derived::ColsAtCompileTime == 1, THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE);\n\n  const typename Derived::Scalar cy = cos(ypr(0) / 2.0);\n  const typename Derived::Scalar sy = sin(ypr(0) / 2.0);\n  const typename Derived::Scalar cp = cos(ypr(1) / 2.0);\n  const typename Derived::Scalar sp = sin(ypr(1) / 2.0);\n  const typename Derived::Scalar cr = cos(ypr(2) / 2.0);\n  const typename Derived::Scalar sr = sin(ypr(2) / 2.0);\n\n  Eigen::Quaternion<typename Derived::Scalar> q;\n\n  q.w() = cr * cp * cy + sr * sp * sy;\n  q.x() = sr * cp * cy - cr * sp * sy;\n  q.y() = cr * sp * cy + sr * cp * sy;\n  q.z() = cr * cp * sy - sr * sp * cy;\n\n  return q;\n}\n\ntemplate <typename Scalar_t>\nEigen::Matrix<Scalar_t, 3, 1> quaternion_to_ypr(const Eigen::Quaternion<Scalar_t>& q_) {\n  Eigen::Quaternion<Scalar_t> q = q_.normalized();\n\n  Eigen::Matrix<Scalar_t, 3, 1> ypr;\n  ypr(2) = atan2(2 * (q.w() * q.x() + q.y() * q.z()), 1 - 2 * (q.x() * q.x() + q.y() * q.y()));\n  ypr(1) = asin(2 * (q.w() * q.y() - q.z() * q.x()));\n  ypr(0) = atan2(2 * (q.w() * q.z() + q.x() * q.y()), 1 - 2 * (q.y() * q.y() + q.z() * q.z()));\n\n  return ypr;\n}\n\ntemplate <typename Scalar_t>\nScalar_t get_yaw_from_quaternion(const Eigen::Quaternion<Scalar_t>& q) {\n  return quaternion_to_ypr(q)(0);\n}\n\ntemplate <typename Scalar_t>\nEigen::Quaternion<Scalar_t> yaw_to_quaternion(Scalar_t yaw) {\n  return Eigen::Quaternion<Scalar_t>(rotz(yaw));\n}\n\ntemplate <typename Scalar_t>\nScalar_t normalize_angle(Scalar_t a) {\n  int cnt = 0;\n  while (true) {\n    cnt++;\n\n    if (a < -M_PI) {\n        a += M_PI * 2.0;\n    } else if (a > M_PI) {\n        a -= M_PI * 2.0;\n    }\n\n    if (-M_PI <= a && a <= M_PI) {\n        break;\n    };\n\n    assert(cnt < 10 && \"[uav_utils/geometry_msgs] INVALID INPUT ANGLE\");\n  }\n\n  return a;\n}\n\ntemplate <typename Scalar_t>\nScalar_t angle_add(Scalar_t a, Scalar_t b) {\n  Scalar_t c = a + b;\n  c = normalize_angle(c);\n  assert(-M_PI <= c && c <= M_PI);\n  return c;\n}\n\ntemplate <typename Scalar_t>\nScalar_t yaw_add(Scalar_t a, Scalar_t b) {\n  return angle_add(a, b);\n}\n\ntemplate <typename Derived>\nEigen::Matrix<typename Derived::Scalar, 3, 3> get_skew_symmetric(const Eigen::DenseBase<Derived>& v) {\n  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived);\n  EIGEN_STATIC_ASSERT(Derived::RowsAtCompileTime == 3, THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE);\n  EIGEN_STATIC_ASSERT(Derived::ColsAtCompileTime == 1, THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE);\n\n  Eigen::Matrix<typename Derived::Scalar, 3, 3> M;\n  M.setZero();\n  M(0, 1) = -v(2);\n  M(0, 2) = v(1);\n  M(1, 0) = v(2);\n  M(1, 2) = -v(0);\n  M(2, 0) = -v(1);\n  M(2, 1) = v(0);\n  return M;\n}\n\ntemplate <typename Derived>\nEigen::Matrix<typename Derived::Scalar, 3, 1> from_skew_symmetric(const Eigen::DenseBase<Derived>& M) {\n  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived);\n  EIGEN_STATIC_ASSERT(Derived::RowsAtCompileTime == 3, THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE);\n  EIGEN_STATIC_ASSERT(Derived::ColsAtCompileTime == 3, THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE);\n\n  Eigen::Matrix<typename Derived::Scalar, 3, 1> v;\n  v(0) = M(2, 1);\n  v(1) = -M(2, 0);\n  v(2) = M(1, 0);\n  \n  assert(v.isApprox(Eigen::Matrix<typename Derived::Scalar, 3, 1>(-M(1, 2), M(0, 2), -M(0, 1))));\n  \n  return v;\n}\n\n\n}  // namespace uav_utils\n"
  },
  {
    "path": "src/uav_simulator/so3_quadrotor/include/so3_quadrotor/quadrotor_dynamics.hpp",
    "content": "#pragma once\n#include \"so3_quadrotor/geometry_utils.hpp\"\n\nnamespace so3_quadrotor {\n\n// data types\nstruct Config {\n  double          g;      // gravity\n  double          mass;\n  Eigen::Matrix3d J;      // Inertia\n  double          kf;\n  double          km;\n  double          arm_length;\n  double          motor_time_constant; // unit: sec\n  double          max_rpm;\n  double          min_rpm;\n};\nstruct Control {\n  double rpm[4];\n};\nstruct Cmd {\n  float force[3] = {0,0,0};\n  float qx, qy, qz, qw;\n  float kR[3] = {0.0, 0.0, 0.0};\n  float kOm[3] = {0.0, 0.0, 0.0};\n  float corrections[3] = {0,0,0};\n  float current_yaw = 0;\n  bool  use_external_yaw = false;\n};\n\nclass Quadrotor {\n private:\n  // parameters\n  Config config_;\n  // state\n  struct State {\n    Eigen::Vector3d x = Eigen::Vector3d::Zero();\n    Eigen::Vector3d v = Eigen::Vector3d::Zero();\n    Eigen::Matrix3d R = Eigen::Matrix3d::Identity();\n    Eigen::Vector3d omega = Eigen::Vector3d::Zero();\n    Eigen::Vector4d motor_rpm = Eigen::Vector4d::Zero();\n    EIGEN_MAKE_ALIGNED_OPERATOR_NEW;\n    inline State operator+(const State &t) const {\n      State sum;\n      sum.x = x + t.x;\n      sum.v = v + t.v;\n      sum.R = R + t.R;\n      sum.omega = omega + t.omega;\n      sum.motor_rpm = motor_rpm + t.motor_rpm;\n      return sum;\n    }\n    inline State operator*(const double &t) const {\n      State mul;\n      mul.x = x * t;\n      mul.v = v * t;\n      mul.R = R * t;\n      mul.omega = omega * t;\n      mul.motor_rpm = motor_rpm * t;\n      return mul;\n    }\n    inline State operator/(const double &t) const {\n      State mul;\n      mul.x = x / t;\n      mul.v = v / t;\n      mul.R = R / t;\n      mul.omega = omega / t;\n      mul.motor_rpm = motor_rpm / t;\n      return mul;\n    }\n  } state_;\n  Eigen::Vector4d  input_ = Eigen::Vector4d::Zero();\n public:\n  const Config &config;\n  const State &state;\n  Quadrotor(const Config &conf) : config_(conf), \n    config(config_), state(state_) {};\n  ~Quadrotor() {};\n\n  // Inputs are desired RPM for the motors\n  // Rotor numbering is:\n  //   *1*    Front\n  // 3     4\n  //    2\n  // with 1 and 2 clockwise and 3 and 4 counter-clockwise (looking from top)\n  inline void setInput(double u1, double u2, double u3, double u4) {\n    input_(0) = u1;\n    input_(1) = u2;\n    input_(2) = u3;\n    input_(3) = u4;\n    for (size_t i=0; i<4; ++i) {\n      if (std::isnan(input_(i))) {\n        printf(\"so3_quadrotor: NAN input!\\n\");\n      }\n      input_(i) = input_(i) < config_.max_rpm ? input_(i) : config_.max_rpm;\n      input_(i) = input_(i) > config_.min_rpm ? input_(i) : config_.min_rpm;\n    }\n  }\n\n  // calculate dot of state\n  inline State diff(const State &state) const {\n    State state_dot;\n    // Re-orthonormalize R (polar decomposition)\n    Eigen::LLT<Eigen::Matrix3d> llt(state.R.transpose() * state.R);\n    Eigen::Matrix3d             P = llt.matrixL();\n    Eigen::Matrix3d             R = state.R * P.inverse();\n\n    Eigen::Matrix3d omega_vee(Eigen::Matrix3d::Zero());\n\n    omega_vee(2, 1) = state.omega(0);\n    omega_vee(1, 2) = -state.omega(0);\n    omega_vee(0, 2) = state.omega(1);\n    omega_vee(2, 0) = -state.omega(1);\n    omega_vee(1, 0) = state.omega(2);\n    omega_vee(0, 1) = -state.omega(2);\n\n    Eigen::Vector4d motor_rpm_sq = state.motor_rpm.array().square();\n\n    double thrust = config_.kf * motor_rpm_sq.sum();\n\n    Eigen::Vector3d moments;\n    moments(0) = config_.kf * (motor_rpm_sq(2) - motor_rpm_sq(3)) * config_.arm_length;\n    moments(1) = config_.kf * (motor_rpm_sq(1) - motor_rpm_sq(0)) * config_.arm_length;\n    moments(2) = config_.km * (motor_rpm_sq(0) + motor_rpm_sq(1) - motor_rpm_sq(2) -\n                        motor_rpm_sq(3));\n\n    double resistance = 0.1 *                                        // C\n                        3.14159265 * (config_.arm_length) * (config_.arm_length) * // S\n                        state.v.norm() * state.v.norm();\n    Eigen::Vector3d vnorm = state.v.norm() > 0 ? state.v.normalized() : state.v;\n    state_dot.x = state.v;\n    state_dot.v = -Eigen::Vector3d(0, 0, config_.g) + thrust * R.col(2) / config_.mass - resistance * vnorm / config_.mass;\n    state_dot.R = R * omega_vee;\n    state_dot.omega = config_.J.inverse() * (moments - state.omega.cross(config_.J * state.omega));\n    state_dot.motor_rpm = (input_ - state.motor_rpm) / config_.motor_time_constant;\n    return state_dot;\n  }\n  // Runs the actual dynamics simulation with a time step of dt\n  inline void step(const double &dt) {\n    // Runge–Kutta\n    State k1 = diff(state_);\n    State k2 = diff(state_+k1*dt/2);\n    State k3 = diff(state_+k2*dt/2);\n    State k4 = diff(state_+k3*dt);\n    state_ = state_ + (k1+k2*2+k3*2+k4) * dt/6;\n  }\n  // get control from cmd\n  inline Control getControl(const Cmd& cmd) {\n    double         kf = config_.kf - cmd.corrections[0];\n    double         km = config_.km / kf * kf;\n    double          d = config_.arm_length;\n    Eigen::Matrix3f J = config_.J.cast<float>();\n    float     I[3][3] = { { J(0, 0), J(0, 1), J(0, 2) },\n                          { J(1, 0), J(1, 1), J(1, 2) },\n                          { J(2, 0), J(2, 1), J(2, 2) } };\n    // rotation, may use external yaw\n    Eigen::Vector3d ypr = uav_utils::R_to_ypr(state_.R);\n    if (cmd.use_external_yaw) {\n      ypr[0] = cmd.current_yaw;\n    }\n    Eigen::Matrix3d R; \n    R = Eigen::AngleAxisd(ypr[0], Eigen::Vector3d::UnitZ()) *\n        Eigen::AngleAxisd(ypr[1], Eigen::Vector3d::UnitY()) *\n        Eigen::AngleAxisd(ypr[2], Eigen::Vector3d::UnitX());\n    float R11 = R(0, 0);\n    float R12 = R(0, 1);\n    float R13 = R(0, 2);\n    float R21 = R(1, 0);\n    float R22 = R(1, 1);\n    float R23 = R(1, 2);\n    float R31 = R(2, 0);\n    float R32 = R(2, 1);\n    float R33 = R(2, 2);\n    float Om1 = state_.omega(0);\n    float Om2 = state_.omega(1);\n    float Om3 = state_.omega(2);\n    float Rd11 =\n      cmd.qw * cmd.qw + cmd.qx * cmd.qx - cmd.qy * cmd.qy - cmd.qz * cmd.qz;\n    float Rd12 = 2 * (cmd.qx * cmd.qy - cmd.qw * cmd.qz);\n    float Rd13 = 2 * (cmd.qx * cmd.qz + cmd.qw * cmd.qy);\n    float Rd21 = 2 * (cmd.qx * cmd.qy + cmd.qw * cmd.qz);\n    float Rd22 =\n      cmd.qw * cmd.qw - cmd.qx * cmd.qx + cmd.qy * cmd.qy - cmd.qz * cmd.qz;\n    float Rd23 = 2 * (cmd.qy * cmd.qz - cmd.qw * cmd.qx);\n    float Rd31 = 2 * (cmd.qx * cmd.qz - cmd.qw * cmd.qy);\n    float Rd32 = 2 * (cmd.qy * cmd.qz + cmd.qw * cmd.qx);\n    float Rd33 =\n      cmd.qw * cmd.qw - cmd.qx * cmd.qx - cmd.qy * cmd.qy + cmd.qz * cmd.qz;\n\n    float Psi = 0.5f * (3.0f - (Rd11 * R11 + Rd21 * R21 + Rd31 * R31 +\n                                Rd12 * R12 + Rd22 * R22 + Rd32 * R32 +\n                                Rd13 * R13 + Rd23 * R23 + Rd33 * R33));\n    float force = 0;\n    if (Psi < 1.0f) // Position control stability guaranteed only when Psi < 1\n      force = cmd.force[0] * R13 + cmd.force[1] * R23 + cmd.force[2] * R33;\n\n    float eR1 = 0.5f * (R12 * Rd13 - R13 * Rd12 + R22 * Rd23 - R23 * Rd22 +\n                        R32 * Rd33 - R33 * Rd32);\n    float eR2 = 0.5f * (R13 * Rd11 - R11 * Rd13 - R21 * Rd23 + R23 * Rd21 -\n                        R31 * Rd33 + R33 * Rd31);\n    float eR3 = 0.5f * (R11 * Rd12 - R12 * Rd11 + R21 * Rd22 - R22 * Rd21 +\n                        R31 * Rd32 - R32 * Rd31);\n\n    float eOm1 = Om1;\n    float eOm2 = Om2;\n    float eOm3 = Om3;\n\n    float in1 = Om2 * (I[2][0] * Om1 + I[2][1] * Om2 + I[2][2] * Om3) -\n                Om3 * (I[1][0] * Om1 + I[1][1] * Om2 + I[1][2] * Om3);\n    float in2 = Om3 * (I[0][0] * Om1 + I[0][1] * Om2 + I[0][2] * Om3) -\n                Om1 * (I[2][0] * Om1 + I[2][1] * Om2 + I[2][2] * Om3);\n    float in3 = Om1 * (I[1][0] * Om1 + I[1][1] * Om2 + I[1][2] * Om3) -\n                Om2 * (I[0][0] * Om1 + I[0][1] * Om2 + I[0][2] * Om3);\n    float M1 = -cmd.kR[0] * eR1 - cmd.kOm[0] * eOm1 + in1;\n    float M2 = -cmd.kR[1] * eR2 - cmd.kOm[1] * eOm2 + in2;\n    float M3 = -cmd.kR[2] * eR3 - cmd.kOm[2] * eOm3 + in3;\n\n    float w_sq[4];\n    w_sq[0] = force / (4 * kf) - M2 / (2 * d * kf) + M3 / (4 * km);\n    w_sq[1] = force / (4 * kf) + M2 / (2 * d * kf) + M3 / (4 * km);\n    w_sq[2] = force / (4 * kf) + M1 / (2 * d * kf) - M3 / (4 * km);\n    w_sq[3] = force / (4 * kf) - M1 / (2 * d * kf) - M3 / (4 * km);\n\n    Control control;\n    for (int i = 0; i < 4; i++) {\n      if (w_sq[i] < 0) w_sq[i] = 0;\n      control.rpm[i] = sqrtf(w_sq[i]);\n    }\n    return control;\n  }\n\n  // set initial state\n  inline void setPos(const Eigen::Vector3d &pos) {\n    state_.x = pos;\n  }\n  inline void setYpr(const Eigen::Vector3d &ypr) {\n    state_.R = uav_utils::ypr_to_R(ypr);\n  }\n  inline void setRpm(const Eigen::Vector4d &rpm) {\n    state_.motor_rpm = rpm;\n  }\n  // get values of state\n  inline double getGrav() const {\n    return config_.g * config_.mass;\n  }\n  inline Eigen::Vector3d getPos() const {\n    return state_.x;\n  }\n  inline Eigen::Vector3d getVel() const {\n    return state_.v;\n  }\n  inline Eigen::Vector3d getAcc() const {\n    Eigen::LLT<Eigen::Matrix3d> llt(state_.R.transpose() * state_.R);\n    Eigen::Matrix3d             P = llt.matrixL();\n    Eigen::Matrix3d             R = state_.R * P.inverse();\n    double resistance = 0.1 *                                        // C\n                        3.14159265 * (config_.arm_length) * (config_.arm_length) * // S\n                        state_.v.norm() * state_.v.norm();\n    Eigen::Vector3d vnorm = state_.v.normalized();\n    Eigen::Vector4d motor_rpm_sq = state_.motor_rpm.array().square();\n    double thrust = config_.kf * motor_rpm_sq.sum();\n    return -Eigen::Vector3d(0, 0, config_.g) + thrust * R.col(2) / config_.mass - resistance * vnorm / config_.mass;\n  }\n  inline Eigen::Quaterniond getQuat() const {\n    return Eigen::Quaterniond(state_.R);\n  }\n  inline Eigen::Vector3d getOmega() const {\n    return state_.omega;\n  }\n\n};\n\n} // namespace so3_quadrotor\n"
  },
  {
    "path": "src/uav_simulator/so3_quadrotor/nodelet_plugin.xml",
    "content": "<library path=\"lib/libso3_quadrotor_nodelet\">\n  <class name=\"so3_quadrotor/Nodelet\" type=\"so3_quadrotor::Nodelet\" base_class_type=\"nodelet::Nodelet\">\n    <description></description>\n  </class>\n</library>\n"
  },
  {
    "path": "src/uav_simulator/so3_quadrotor/package.xml",
    "content": "<?xml version=\"1.0\"?>\n<package format=\"2\">\n  <name>so3_quadrotor</name>\n  <version>0.0.0</version>\n  <description>The so3_quadrotor package</description>\n\n  <maintainer email=\"jlji@zju.edu.cn\">ji</maintainer>\n\n  <license>GPLv3</license>\n\n  <buildtool_depend>catkin</buildtool_depend>\n  <depend>quadrotor_msgs</depend>\n  <depend>roscpp</depend>\n  <depend>nodelet</depend>\n  <depend>tf</depend>\n\n  <export>\n    <nodelet plugin=\"${prefix}/nodelet_plugin.xml\"/>\n  </export>\n</package>\n"
  },
  {
    "path": "src/uav_simulator/so3_quadrotor/src/so3_quadrotor_nodelet.cpp",
    "content": "#include \"so3_quadrotor/quadrotor_dynamics.hpp\"\n#include <quadrotor_msgs/SO3Command.h>\n#include <nodelet/nodelet.h>\n#include <ros/ros.h>\n#include <nav_msgs/Odometry.h>\n#include <sensor_msgs/Imu.h>\n#include <visualization_msgs/MarkerArray.h>\n#include <tf/transform_broadcaster.h>\n\nnamespace so3_quadrotor {\nclass Nodelet : public nodelet::Nodelet {\n private:\n  std::shared_ptr<Quadrotor> quadrotorPtr_;\n  Control control_;\n  Cmd cmd_;\n  ros::Publisher odom_pub_, imu_pub_, vis_pub_;\n  ros::Subscriber cmd_sub_;\n  ros::Timer simulation_timer;\n  tf::TransformBroadcaster tf_br_;\n\n  // parameters\n  int simulation_rate_ = 1e3;\n  int odom_rate_ = 400;\n  // msgs\n  nav_msgs::Odometry odom_msg_;\n  sensor_msgs::Imu imu_msg_;\n  visualization_msgs::MarkerArray vis_msg_;\n  double motor_yaw_[4] = {0,0,0,0};\n\n\n  void cmd_callback(const quadrotor_msgs::SO3Command::ConstPtr& cmd_msg) {\n    cmd_.force[0]         = cmd_msg->force.x;\n    cmd_.force[1]         = cmd_msg->force.y;\n    cmd_.force[2]         = cmd_msg->force.z;\n    cmd_.qx               = cmd_msg->orientation.x;\n    cmd_.qy               = cmd_msg->orientation.y;\n    cmd_.qz               = cmd_msg->orientation.z;\n    cmd_.qw               = cmd_msg->orientation.w;\n    cmd_.kR[0]            = cmd_msg->kR[0];\n    cmd_.kR[1]            = cmd_msg->kR[1];\n    cmd_.kR[2]            = cmd_msg->kR[2];\n    cmd_.kOm[0]           = cmd_msg->kOm[0];\n    cmd_.kOm[1]           = cmd_msg->kOm[1];\n    cmd_.kOm[2]           = cmd_msg->kOm[2];\n    cmd_.corrections[0]   = cmd_msg->aux.kf_correction;\n    cmd_.corrections[1]   = cmd_msg->aux.angle_corrections[0];\n    cmd_.corrections[2]   = cmd_msg->aux.angle_corrections[1];\n    cmd_.current_yaw      = cmd_msg->aux.current_yaw;\n    cmd_.use_external_yaw = cmd_msg->aux.use_external_yaw;\n  }\n  void timer_callback(const ros::TimerEvent& event) {\n    auto last_control = control_;\n    control_ = quadrotorPtr_->getControl(cmd_);\n    for (size_t i = 0; i < 4; ++i) {\n      if (std::isnan(control_.rpm[i]))\n        control_.rpm[i] = last_control.rpm[i];\n    }\n    quadrotorPtr_->setInput(control_.rpm[0], control_.rpm[1], control_.rpm[2], control_.rpm[3]);\n    quadrotorPtr_->step(1.0/simulation_rate_);\n    static ros::Time next_odom_pub_time = ros::Time::now();\n    ros::Time tnow = ros::Time::now();\n    if (tnow >= next_odom_pub_time) {\n      next_odom_pub_time += ros::Duration(1.0/odom_rate_);\n      const Eigen::Vector3d&     pos = quadrotorPtr_->getPos();\n      const Eigen::Vector3d&     vel = quadrotorPtr_->getVel();\n      const Eigen::Vector3d&     acc = quadrotorPtr_->getAcc();\n      const Eigen::Quaterniond& quat = quadrotorPtr_->getQuat();\n      const Eigen::Vector3d&   omega = quadrotorPtr_->getOmega();\n      // tf\n      tf::Transform transform;\n      transform.setOrigin( tf::Vector3(pos.x(), pos.y(), pos.z()) );\n      transform.setRotation( tf::Quaternion(quat.x(), quat.y(), quat.z(), quat.w()) );\n      tf_br_.sendTransform(tf::StampedTransform(transform, ros::Time::now(), \"world\", \"body\"));\n      // odom\n      odom_msg_.header.stamp = tnow;\n      odom_msg_.pose.pose.position.x = pos(0);\n      odom_msg_.pose.pose.position.y = pos(1);\n      odom_msg_.pose.pose.position.z = pos(2);\n      odom_msg_.pose.pose.orientation.x = quat.x();\n      odom_msg_.pose.pose.orientation.y = quat.y();\n      odom_msg_.pose.pose.orientation.z = quat.z();\n      odom_msg_.pose.pose.orientation.w = quat.w();\n\n      odom_msg_.twist.twist.linear.x = vel(0);\n      odom_msg_.twist.twist.linear.y = vel(1);\n      odom_msg_.twist.twist.linear.z = vel(2);\n\n      odom_msg_.twist.twist.angular.x = omega(0);\n      odom_msg_.twist.twist.angular.y = omega(1);\n      odom_msg_.twist.twist.angular.z = omega(2);\n      odom_pub_.publish(odom_msg_);\n      // imu\n      imu_msg_.header.stamp = tnow;\n      imu_msg_.orientation.x = quat.x();\n      imu_msg_.orientation.y = quat.y();\n      imu_msg_.orientation.z = quat.z();\n      imu_msg_.orientation.w = quat.w();\n\n      imu_msg_.angular_velocity.x = omega(0);\n      imu_msg_.angular_velocity.y = omega(1);\n      imu_msg_.angular_velocity.z = omega(2);\n\n      imu_msg_.linear_acceleration.x = acc[0];\n      imu_msg_.linear_acceleration.y = acc[1];\n      imu_msg_.linear_acceleration.z = acc[2];\n      imu_pub_.publish(imu_msg_);\n      // drone visualization\n      std::vector<Eigen::Vector3d> propellers;\n      propellers.emplace_back(0, +quadrotorPtr_->config.arm_length/2, 0.02);\n      propellers.emplace_back(0, -quadrotorPtr_->config.arm_length/2, 0.02);\n      propellers.emplace_back(-quadrotorPtr_->config.arm_length/2, 0, 0.02);\n      propellers.emplace_back(+quadrotorPtr_->config.arm_length/2, 0, 0.02);\n      for (size_t i=0; i<4; ++i) {\n        double rpm = quadrotorPtr_->state.motor_rpm.coeff(i);\n        if (i/2) {\n          motor_yaw_[i] += rpm*60 / odom_rate_;\n        } else {\n          motor_yaw_[i] -= rpm*60 / odom_rate_;\n        }\n        motor_yaw_[i] = std::fmod(motor_yaw_[i], 2*M_PI);\n        Eigen::Quaterniond quat_propeller = quat * uav_utils::ypr_to_quaternion(\n          Eigen::Vector3d(motor_yaw_[i], 0, 0));\n        Eigen::Vector3d pos_propeller = pos + quat.toRotationMatrix() * propellers[i];\n\n        vis_msg_.markers[i].pose.position.x = pos_propeller(0);\n        vis_msg_.markers[i].pose.position.y = pos_propeller(1);\n        vis_msg_.markers[i].pose.position.z = pos_propeller(2);\n        vis_msg_.markers[i].pose.orientation.x = quat_propeller.x();\n        vis_msg_.markers[i].pose.orientation.y = quat_propeller.y();\n        vis_msg_.markers[i].pose.orientation.z = quat_propeller.z();\n        vis_msg_.markers[i].pose.orientation.w = quat_propeller.w();\n      }\n      for (size_t i=4; i<vis_msg_.markers.size(); ++i) {\n        vis_msg_.markers[i].pose = odom_msg_.pose.pose;\n        vis_msg_.markers[i].id = i;\n      }\n      vis_pub_.publish(vis_msg_);\n    }\n  }\n public:\n  void onInit(void) {\n    ros::NodeHandle nh(getPrivateNodeHandle());\n    // parameters\n    double init_x, init_y, init_z, init_yaw;\n    nh.getParam(\"init_x\", init_x);\n    nh.getParam(\"init_y\", init_y);\n    nh.getParam(\"init_z\", init_z);\n    nh.getParam(\"init_yaw\", init_yaw);\n    // config of quadrotor\n    Config config;\n    nh.getParam(\"g\",       config.g);\n    nh.getParam(\"mass\", config.mass);\n    double Ixx, Iyy, Izz;\n    nh.getParam(\"Ixx\", Ixx);\n    nh.getParam(\"Iyy\", Iyy);\n    nh.getParam(\"Izz\", Izz);\n    config.J = Eigen::Vector3d(Ixx, Iyy, Izz).asDiagonal();\n    nh.getParam(\"kf\", config.kf);\n    double prop_radius;\n    nh.getParam(\"prop_radius\", prop_radius);\n    config.km = 0.07 * (3 * prop_radius) * config.kf;\n    nh.getParam(\"arm_length\", config.arm_length);\n    nh.getParam(\"motor_time_constant\", config.motor_time_constant);\n    nh.getParam(\"max_rpm\", config.max_rpm);\n    nh.getParam(\"min_rpm\", config.min_rpm);\n    nh.getParam(\"simulation_rate\", simulation_rate_);\n    nh.getParam(\"odom_rate\", odom_rate_);\n\n    quadrotorPtr_ = std::make_shared<Quadrotor>(config);\n    quadrotorPtr_->setPos(Eigen::Vector3d(init_x, init_y, init_z));\n    quadrotorPtr_->setYpr(Eigen::Vector3d(init_yaw, 0, 0));\n    double rpm = sqrt(config.mass * config.g / 4 / config.kf);\n    quadrotorPtr_->setRpm(Eigen::Vector4d(rpm, rpm, rpm, rpm));\n    \n    Eigen::Quaterniond quat = uav_utils::ypr_to_quaternion(Eigen::Vector3d(init_yaw, 0, 0));\n    cmd_.force[2] =  config.mass * config.g;\n    cmd_.qw = quat.w();\n    cmd_.qx = quat.x();\n    cmd_.qy = quat.y();\n    cmd_.qz = quat.z();\n\n    odom_pub_ = nh.advertise<nav_msgs::Odometry>(\"odom\", 100);\n    imu_pub_  = nh.advertise<sensor_msgs::Imu>(\"imu\", 10);\n    vis_pub_= nh.advertise<visualization_msgs::MarkerArray>(\"vis\", 10);\n    cmd_sub_  = nh.subscribe<quadrotor_msgs::SO3Command>(\"so3cmd\", 10, &Nodelet::cmd_callback, this, ros::TransportHints().tcpNoDelay());\n    simulation_timer = nh.createTimer(ros::Duration(1.0/simulation_rate_), &Nodelet::timer_callback, this);\n\n    odom_msg_.header.frame_id = \"world\";\n    imu_msg_ .header.frame_id = \"world\";\n    vis_msg_.markers.resize(4);\n    visualization_msgs::Marker propeller;\n    propeller.header.frame_id = \"world\";\n    propeller.type = visualization_msgs::Marker::CYLINDER;\n    propeller.action = visualization_msgs::Marker::MODIFY;\n    propeller.scale.x = 2 * prop_radius;\n    propeller.scale.y = 0.2 * prop_radius;\n    propeller.scale.z = 0.1 * prop_radius;\n    propeller.color.a = 1;\n    propeller.color.r = 0;\n    propeller.color.g = 0;\n    propeller.color.b = 0;\n    for (size_t i=0; i<4; ++i) {\n      vis_msg_.markers[i] = propeller;\n      vis_msg_.markers[i].id = i;\n    }\n    visualization_msgs::Marker drone_body = propeller;\n    drone_body.type = visualization_msgs::Marker::SPHERE;\n    drone_body.scale.x = config.arm_length;\n    drone_body.scale.y = 0.2 * config.arm_length;\n    drone_body.scale.z = 0.1 * config.arm_length;\n    vis_msg_.markers.push_back(drone_body);\n    drone_body.scale.y = config.arm_length;\n    drone_body.scale.x = 0.2 * config.arm_length;\n    vis_msg_.markers.push_back(drone_body);\n  };\n};\n\n} // so3_quadrotor\n\n#include <pluginlib/class_list_macros.h>\nPLUGINLIB_EXPORT_CLASS(so3_quadrotor::Nodelet, nodelet::Nodelet);\n"
  },
  {
    "path": "src/uav_simulator/uav_simulator/CMakeLists.txt",
    "content": "cmake_minimum_required(VERSION 3.0.2)\nproject(uav_simulator)\n\nadd_compile_options(-std=c++17)\nset(CMAKE_BUILD_TYPE \"Release\")\nset(CMAKE_CXX_FLAGS_RELEASE \"-O3 -Wall\")\n\nfind_package(catkin REQUIRED COMPONENTS)\n\ncatkin_package()\n\ninclude_directories(\n  ${catkin_INCLUDE_DIRS}\n)\n"
  },
  {
    "path": "src/uav_simulator/uav_simulator/config/rviz_config.rviz",
    "content": "Panels:\n  - Class: rviz/Displays\n    Help Height: 0\n    Name: Displays\n    Property Tree Widget:\n      Expanded:\n        - /Global Options1\n        - /GlobalMap1/GlobalMap1\n        - /Planning1/SpatialCurve1\n        - /Planning1/SpatialCurve1/Namespaces1\n        - /Planning1/SlidingWindow1\n        - /Planning1/many ReplanTraj1\n        - /Planning1/initialReplanPts1\n        - /Sensors1/SensedPoints_gpu1\n        - /DronePose1/Status1\n      Splitter Ratio: 0.5777778029441833\n    Tree Height: 876\n  - Class: rviz/Selection\n    Name: Selection\n  - Class: rviz/Tool Properties\n    Expanded:\n      - /Publish Point1\n    Name: Tool Properties\n    Splitter Ratio: 0.5886790156364441\n  - Class: rviz/Views\n    Expanded:\n      - /Current View1\n    Name: Views\n    Splitter Ratio: 0.5\n  - Class: rviz/Time\n    Experimental: false\n    Name: Time\n    SyncMode: 0\n    SyncSource: GlobalMap\n  - Class: rviz/Displays\n    Help Height: 78\n    Name: Displays\n    Property Tree Widget:\n      Expanded:\n        - /Global Options1\n        - /Status1\n      Splitter Ratio: 0.5\n    Tree Height: 341\n  - Class: rviz/Tool Properties\n    Expanded: ~\n    Name: Tool Properties\n    Splitter Ratio: 0.6555560231208801\n  - Class: rviz/Views\n    Expanded:\n      - /Current View1\n    Name: Views\n    Splitter Ratio: 0.5\n  - Class: rviz/Tool Properties\n    Expanded: ~\n    Name: Tool Properties\n    Splitter Ratio: 0.5\n  - Class: rviz/Views\n    Expanded:\n      - /Current View1\n      - /Current View1/Focal Point1\n    Name: Views\n    Splitter Ratio: 0.5\nPreferences:\n  PromptSaveOnExit: true\nToolbars:\n  toolButtonStyle: 2\nVisualization Manager:\n  Class: \"\"\n  Displays:\n    - Alpha: 1\n      Cell Size: 1\n      Class: rviz/Grid\n      Color: 152; 152; 152\n      Enabled: true\n      Line Style:\n        Line Width: 0.029999999329447746\n        Value: Lines\n      Name: Grid\n      Normal Cell Count: 0\n      Offset:\n        X: 0\n        Y: 0\n        Z: 0\n      Plane: XY\n      Plane Cell Count: 20\n      Reference Frame: world\n      Value: true\n    - Alpha: 1\n      Class: rviz/Axes\n      Enabled: true\n      Length: 2\n      Name: Axes\n      Radius: 0.10000000149011612\n      Reference Frame: world\n      Value: true\n    - Class: rviz/Group\n      Displays:\n        - Alpha: 1\n          Autocompute Intensity Bounds: true\n          Autocompute Value Bounds:\n            Max Value: 5.046831130981445\n            Min Value: 0.0010000000474974513\n            Value: true\n          Axis: Z\n          Channel Name: intensity\n          Class: rviz/PointCloud2\n          Color: 186; 186; 186\n          Color Transformer: AxisColor\n          Decay Time: 0\n          Enabled: true\n          Invert Rainbow: false\n          Max Color: 255; 255; 255\n          Max Intensity: 4096\n          Min Color: 0; 0; 0\n          Min Intensity: 0\n          Name: GlobalMap\n          Position Transformer: XYZ\n          Queue Size: 10\n          Selectable: true\n          Size (Pixels): 3\n          Size (m): 0.15000000596046448\n          Style: Boxes\n          Topic: /map_generator_node/global_map\n          Unreliable: false\n          Use Fixed Frame: true\n          Use rainbow: true\n          Value: true\n      Enabled: true\n      Name: GlobalMap\n    - Class: rviz/Group\n      Displays:\n        - Class: rviz/Marker\n          Enabled: true\n          Marker Topic: /tr_node/trajectory_vis\n          Name: SpatialCurve\n          Namespaces:\n            {}\n          Queue Size: 100\n          Value: true\n        - Class: rviz/Marker\n          Enabled: true\n          Marker Topic: /local_replanner/check_trajectory_vis\n          Name: SlidingWindow\n          Namespaces:\n            {}\n          Queue Size: 100\n          Value: true\n        - Class: rviz/Marker\n          Enabled: true\n          Marker Topic: /local_replanner/replaned_b_spline_vis\n          Name: many ReplanTraj\n          Namespaces:\n            {}\n          Queue Size: 100\n          Value: true\n        - Class: rviz/Marker\n          Enabled: true\n          Marker Topic: /local_replanner/initial_b_spline_vis\n          Name: initialReplanPts\n          Namespaces:\n            {}\n          Queue Size: 100\n          Value: true\n      Enabled: true\n      Name: Planning\n    - Class: rviz/Group\n      Displays:\n        - Alpha: 1\n          Autocompute Intensity Bounds: true\n          Autocompute Value Bounds:\n            Max Value: 10\n            Min Value: -10\n            Value: true\n          Axis: Z\n          Channel Name: intensity\n          Class: rviz/PointCloud2\n          Color: 157; 157; 157\n          Color Transformer: FlatColor\n          Decay Time: 0\n          Enabled: false\n          Invert Rainbow: false\n          Max Color: 255; 255; 255\n          Max Intensity: 4096\n          Min Color: 0; 0; 0\n          Min Intensity: 0\n          Name: SensedPoints_cpu\n          Position Transformer: XYZ\n          Queue Size: 10\n          Selectable: true\n          Size (Pixels): 3\n          Size (m): 0.25\n          Style: Flat Squares\n          Topic: /pcl_render_node/local_pointcloud\n          Unreliable: false\n          Use Fixed Frame: true\n          Use rainbow: true\n          Value: false\n        - Alpha: 1\n          Autocompute Intensity Bounds: true\n          Autocompute Value Bounds:\n            Max Value: 3.0736145973205566\n            Min Value: -0.0973629504442215\n            Value: true\n          Axis: Z\n          Channel Name: intensity\n          Class: rviz/PointCloud2\n          Color: 255; 255; 255\n          Color Transformer: FlatColor\n          Decay Time: 0\n          Enabled: false\n          Invert Rainbow: false\n          Max Color: 255; 255; 255\n          Max Intensity: 4096\n          Min Color: 0; 0; 0\n          Min Intensity: 0\n          Name: SensedPoints_gpu\n          Position Transformer: XYZ\n          Queue Size: 10\n          Selectable: true\n          Size (Pixels): 3\n          Size (m): 0.20000000298023224\n          Style: Flat Squares\n          Topic: /pcl_render_node/rendered_pcl\n          Unreliable: false\n          Use Fixed Frame: true\n          Use rainbow: true\n          Value: false\n        - Class: rviz/Image\n          Enabled: false\n          Image Topic: /pcl_render_node/colordepth\n          Max Value: 1\n          Median window: 5\n          Min Value: 0\n          Name: DepthImage\n          Normalize Range: true\n          Queue Size: 2\n          Transport Hint: raw\n          Unreliable: false\n          Value: false\n      Enabled: true\n      Name: Sensors\n    - Alpha: 0.10000000149011612\n      BoundColor: 255; 0; 0\n      Class: decomp_rviz_plugins/PolyhedronArray\n      Enabled: true\n      MeshColor: 0; 170; 255\n      Name: Corridor\n      Queue Size: 10\n      Scale: 0.10000000149011612\n      State: Mesh\n      Topic: /drone0/polyhedron_corridor_mesh\n      Unreliable: false\n      Value: true\n      VsColor: 0; 255; 0\n      VsScale: 1\n    - Alpha: 1\n      Buffer Length: 5\n      Class: rviz/Path\n      Color: 85; 0; 255\n      Enabled: true\n      Head Diameter: 0.30000001192092896\n      Head Length: 0.20000000298023224\n      Length: 0.30000001192092896\n      Line Style: Billboards\n      Line Width: 0.05000000074505806\n      Name: Reference_traj\n      Offset:\n        X: 0\n        Y: 0\n        Z: 0\n      Pose Color: 255; 85; 255\n      Pose Style: None\n      Queue Size: 10\n      Radius: 0.029999999329447746\n      Shaft Diameter: 0.10000000149011612\n      Shaft Length: 0.10000000149011612\n      Topic: /drone0/refer_path\n      Unreliable: false\n      Value: true\n    - Alpha: 0.10000000149011612\n      BoundColor: 255; 0; 230\n      Class: decomp_rviz_plugins/PolyhedronArray\n      Enabled: true\n      MeshColor: 85; 255; 255\n      Name: Tubes\n      Queue Size: 10\n      Scale: 0.009999999776482582\n      State: Both\n      Topic: /drone0/flight_tunnel\n      Unreliable: false\n      Value: true\n      VsColor: 0; 255; 0\n      VsScale: 1\n    - Alpha: 1\n      Buffer Length: 1\n      Class: rviz/Path\n      Color: 255; 0; 0\n      Enabled: true\n      Head Diameter: 0.30000001192092896\n      Head Length: 0.20000000298023224\n      Length: 0.30000001192092896\n      Line Style: Billboards\n      Line Width: 0.10000000149011612\n      Name: Predictive_traj\n      Offset:\n        X: 0\n        Y: 0\n        Z: 0\n      Pose Color: 255; 85; 255\n      Pose Style: None\n      Queue Size: 10\n      Radius: 0.029999999329447746\n      Shaft Diameter: 0.10000000149011612\n      Shaft Length: 0.10000000149011612\n      Topic: /drone0/predict_path\n      Unreliable: false\n      Value: true\n    - Class: rviz/Marker\n      Enabled: true\n      Marker Topic: /drone0/drone_pose\n      Name: DronePose\n      Namespaces:\n        \"\": true\n      Queue Size: 100\n      Value: true\n    - Class: rviz/Marker\n      Enabled: true\n      Marker Topic: /drone0/global_pose\n      Name: GlobalPose\n      Namespaces:\n        \"\": true\n      Queue Size: 100\n      Value: true\n    - Class: rviz/MarkerArray\n      Enabled: true\n      Marker Topic: /drone0/vis\n      Name: Drone\n      Namespaces:\n        \"\": true\n      Queue Size: 100\n      Value: true\n  Enabled: true\n  Global Options:\n    Background Color: 255; 255; 255\n    Default Light: true\n    Fixed Frame: world\n    Frame Rate: 30\n  Name: root\n  Tools:\n    - Class: rviz/Interact\n      Hide Inactive Objects: true\n    - Class: rviz/MoveCamera\n    - Class: rviz/Select\n    - Class: rviz/FocusCamera\n    - Class: rviz/Measure\n    - Class: rviz/PublishPoint\n      Single click: true\n      Topic: /clicked_point\n  Value: true\n  Views:\n    Current:\n      Class: rviz/XYOrbit\n      Distance: 29.572378158569336\n      Enable Stereo Rendering:\n        Stereo Eye Separation: 0.05999999865889549\n        Stereo Focal Distance: 1\n        Swap Stereo Eyes: false\n        Value: false\n      Field of View: 0.7853981852531433\n      Focal Point:\n        X: 0.29717594385147095\n        Y: -6.039834976196289\n        Z: 7.161857865867205e-06\n      Focal Shape Fixed Size: true\n      Focal Shape Size: 0.05000000074505806\n      Invert Z Axis: false\n      Name: Current View\n      Near Clip Distance: 0.009999999776482582\n      Pitch: 1.5697963237762451\n      Target Frame: world\n      Yaw: 4.698230743408203\n    Saved:\n      - Class: rviz/ThirdPersonFollower\n        Distance: 4.048277378082275\n        Enable Stereo Rendering:\n          Stereo Eye Separation: 0.05999999865889549\n          Stereo Focal Distance: 1\n          Swap Stereo Eyes: false\n          Value: false\n        Field of View: 0.7853981852531433\n        Focal Point:\n          X: 3.297011613845825\n          Y: -0.13499683141708374\n          Z: -1.0989770889282227\n        Focal Shape Fixed Size: true\n        Focal Shape Size: 0.05000000074505806\n        Invert Z Axis: false\n        Name: ThirdPersonFollower\n        Near Clip Distance: 0.009999999776482582\n        Pitch: 0.15354639291763306\n        Target Frame: drone1\n        Yaw: 3.1068692207336426\n      - Class: rviz/Orbit\n        Distance: 14.031325340270996\n        Enable Stereo Rendering:\n          Stereo Eye Separation: 0.05999999865889549\n          Stereo Focal Distance: 1\n          Swap Stereo Eyes: false\n          Value: false\n        Field of View: 0.7853981852531433\n        Focal Point:\n          X: -7.736832141876221\n          Y: -7.348899841308594\n          Z: 0.8977007865905762\n        Focal Shape Fixed Size: false\n        Focal Shape Size: 0.05000000074505806\n        Invert Z Axis: false\n        Name: Orbit\n        Near Clip Distance: 0.009999999776482582\n        Pitch: 1.0697968006134033\n        Target Frame: world\n        Yaw: 4.538923740386963\n      - Class: rviz/Orbit\n        Distance: 13.27771282196045\n        Enable Stereo Rendering:\n          Stereo Eye Separation: 0.05999999865889549\n          Stereo Focal Distance: 1\n          Swap Stereo Eyes: false\n          Value: false\n        Field of View: 0.7853981852531433\n        Focal Point:\n          X: -9.282596588134766\n          Y: -8.473143577575684\n          Z: 1.7744790315628052\n        Focal Shape Fixed Size: true\n        Focal Shape Size: 0.05000000074505806\n        Invert Z Axis: false\n        Name: Orbit\n        Near Clip Distance: 0.009999999776482582\n        Pitch: 1.1547964811325073\n        Target Frame: world\n        Yaw: 6.054520606994629\n      - Class: rviz/ThirdPersonFollower\n        Distance: 10.391033172607422\n        Enable Stereo Rendering:\n          Stereo Eye Separation: 0.05999999865889549\n          Stereo Focal Distance: 1\n          Swap Stereo Eyes: false\n          Value: false\n        Field of View: 0.7853981852531433\n        Focal Point:\n          X: 1.0356316566467285\n          Y: -0.4885826110839844\n          Z: -0.00026488304138183594\n        Focal Shape Fixed Size: true\n        Focal Shape Size: 0.05000000074505806\n        Invert Z Axis: false\n        Name: ThirdPersonFollower\n        Near Clip Distance: 0.009999999776482582\n        Pitch: 0.6947967410087585\n        Target Frame: drone1\n        Yaw: 2.7186548709869385\n      - Class: rviz/ThirdPersonFollower\n        Distance: 22.49183464050293\n        Enable Stereo Rendering:\n          Stereo Eye Separation: 0.05999999865889549\n          Stereo Focal Distance: 1\n          Swap Stereo Eyes: false\n          Value: false\n        Field of View: 0.7853981852531433\n        Focal Point:\n          X: 0.4420492649078369\n          Y: 1.1208577156066895\n          Z: 1.5976747818058357e-06\n        Focal Shape Fixed Size: true\n        Focal Shape Size: 0.05000000074505806\n        Invert Z Axis: false\n        Name: ThirdPersonFollower\n        Near Clip Distance: 0.009999999776482582\n        Pitch: 1.0247968435287476\n        Target Frame: drone1\n        Yaw: 3.1827449798583984\n      - Class: rviz/ThirdPersonFollower\n        Distance: 22.4423770904541\n        Enable Stereo Rendering:\n          Stereo Eye Separation: 0.05999999865889549\n          Stereo Focal Distance: 1\n          Swap Stereo Eyes: false\n          Value: false\n        Field of View: 0.7853981852531433\n        Focal Point:\n          X: 3.6197400093078613\n          Y: 0.5623701810836792\n          Z: -3.2186508178710938e-06\n        Focal Shape Fixed Size: true\n        Focal Shape Size: 0.05000000074505806\n        Invert Z Axis: false\n        Name: ThirdPersonFollower\n        Near Clip Distance: 0.009999999776482582\n        Pitch: 0.8597970008850098\n        Target Frame: drone1\n        Yaw: 3.265840530395508\n      - Class: rviz/ThirdPersonFollower\n        Distance: 14.360918045043945\n        Enable Stereo Rendering:\n          Stereo Eye Separation: 0.05999999865889549\n          Stereo Focal Distance: 1\n          Swap Stereo Eyes: false\n          Value: false\n        Field of View: 0.7853981852531433\n        Focal Point:\n          X: -6.281792640686035\n          Y: -5.607008934020996\n          Z: 0.9089744687080383\n        Focal Shape Fixed Size: false\n        Focal Shape Size: 0.05000000074505806\n        Invert Z Axis: false\n        Name: ThirdPersonFollower\n        Near Clip Distance: 0.009999999776482582\n        Pitch: 0.949797511100769\n        Target Frame: world\n        Yaw: 4.0072102546691895\nWindow Geometry:\n  DepthImage:\n    collapsed: false\n  Displays:\n    collapsed: false\n  Height: 1027\n  Hide Left Dock: false\n  Hide Right Dock: false\n  QMainWindow State: 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\n  Selection:\n    collapsed: false\n  Time:\n    collapsed: false\n  Tool Properties:\n    collapsed: false\n  Views:\n    collapsed: false\n  Width: 1860\n  X: 60\n  Y: 154\n"
  },
  {
    "path": "src/uav_simulator/uav_simulator/config/so3_controller.yaml",
    "content": "g: 9.81\nmass: 0.98\ngains/rot/x  : 1.5\ngains/rot/y  : 1.5\ngains/rot/z  : 1.0\ngains/ang/x  : 0.13\ngains/ang/y  : 0.13\ngains/ang/z  : 0.1\ncorrections/z: 0.0\ncorrections/r: 0.0\ncorrections/p: 0.0"
  },
  {
    "path": "src/uav_simulator/uav_simulator/config/so3_quadrotor.yaml",
    "content": "init_x: -4.0\ninit_y: -8.0\ninit_z: 1.0\ninit_yaw: 0.0\nsimulation_rate: 1000\nodom_rate: 400\ng: 9.81\nmass: 0.98\nIxx: 2.64e-3\nIyy: 2.64e-3\nIzz: 4.96e-3\nkf: 8.98132e-9\nprop_radius: 0.062\narm_length: 0.26\nmotor_time_constant: 0.03333\nmax_rpm: 35000.0\nmin_rpm: 1200.0"
  },
  {
    "path": "src/uav_simulator/uav_simulator/launch/uav_simulator.launch",
    "content": "<launch>\n\n<node pkg=\"rviz\" name=\"rviz\" type=\"rviz\" output=\"log\"\n  args=\"-d $(find uav_simulator)/config/rviz_config.rviz\">\n</node>\n\n<include file=\"$(find map_generator)/launch/map_generator.launch\">\n\n</include>\n\n<group ns=\"drone0\">\n\n  <node pkg=\"nodelet\" type=\"nodelet\" name=\"manager\" args=\"manager\" output=\"screen\"/>\n\n  <node pkg=\"nodelet\" type=\"nodelet\" name=\"so3_quadrotor\"\n    args=\"load so3_quadrotor/Nodelet manager\" output=\"screen\">\n  <!-- <node pkg=\"nodelet\" type=\"nodelet\" name=\"so3_quadrotor\"\n    args=\"standalone so3_quadrotor/Nodelet\" output=\"screen\"> -->\n    <rosparam file=\"$(find uav_simulator)/config/so3_quadrotor.yaml\"/>\n    <remap from=\"~odom\" to=\"odom\"/>\n    <remap from=\"~imu\"  to=\"imu\" />\n    <remap from=\"~so3cmd\" to=\"so3cmd\"/>\n    <remap from=\"~vis\" to=\"vis\"/>\n  </node>\n  <node pkg=\"nodelet\" type=\"nodelet\" name=\"so3_controller\"\n    args=\"load so3_controller/Nodelet manager\" output=\"screen\">\n  <!-- <node pkg=\"nodelet\" type=\"nodelet\" name=\"so3_controller\"\n    args=\"standalone so3_controller/Nodelet\" output=\"screen\"> -->\n    <rosparam file=\"$(find uav_simulator)/config/so3_controller.yaml\"/>\n    <remap from=\"~odom\" to=\"odom\"/>\n    <remap from=\"~imu\"  to=\"imu\" />\n    <remap from=\"~so3cmd\" to=\"so3cmd\"/>\n    <remap from=\"~position_cmd\" to=\"position_cmd\"/>\n  </node>\n\n  <node pkg=\"cmpcc\" name=\"simulation_node\" type=\"simulation_node\" output=\"screen\">\n    <remap from=\"~odom\" to=\"odom\"/>\n    <remap from=\"~position_cmd\" to=\"position_cmd\"/>\n  </node>\n\n</group>\n\n</launch>"
  },
  {
    "path": "src/uav_simulator/uav_simulator/package.xml",
    "content": "<?xml version=\"1.0\"?>\n<package format=\"2\">\n  <name>uav_simulator</name>\n  <version>0.0.0</version>\n  <description>The uav_simulator package</description>\n\n  <maintainer email=\"jlji@zju.edu.cn\">ji</maintainer>\n\n  <license>GPLv3</license>\n\n  <buildtool_depend>catkin</buildtool_depend>\n\n</package>\n"
  }
]