[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-92560":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":9,"totalLinesOfCode":9,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":9,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":16,"stars30d":17,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":18,"rankGlobal":9,"rankLanguage":9,"license":9,"archived":19,"fork":19,"defaultBranch":20,"hasWiki":19,"hasPages":19,"topics":21,"createdAt":9,"pushedAt":9,"updatedAt":29,"readmeContent":30,"aiSummary":31,"trendingCount":16,"starSnapshotCount":16,"syncStatus":32,"lastSyncTime":33,"discoverSource":34},92560,"HiGHS","ERGO-Code\u002FHiGHS","ERGO-Code","Linear optimization software",null,"https:\u002F\u002Fgithub.com\u002FERGO-Code\u002FHiGHS","C++",1702,332,31,156,0,1,52.67,false,"main",[22,23,24,25,26,27,28],"parallel","linear-optimization","simplex","high-performance","interior-point-method","mixed-integer-programming","quadratic-programming","2026-07-22 04:02:06","# HiGHS - Linear optimization software\n\n\u003C!-- ![Build Status](https:\u002F\u002Fgithub.com\u002FERGO-Code\u002FHiGHS\u002Factions\u002Fworkflows\u002Fbuild.yml\u002Fbadge.svg) -->\n\n[![Build Status][fast_build_svg]][fast_build_link]\n[![Build Status][linux_build_svg]][linux_build_link]\n[![Build Status][macos_build_svg]][macos_build_link]\n[![Build Status][windows_build_svg]][windows_build_link]\n\\\n[![Conan Center](https:\u002F\u002Fimg.shields.io\u002Fconan\u002Fv\u002Fhighs)](https:\u002F\u002Fconan.io\u002Fcenter\u002Frecipes\u002Fhighs)\n\\\n[![PyPi](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fhighspy.svg)](https:\u002F\u002Fpypi.python.org\u002Fpypi\u002Fhighspy)\n[![PyPi](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fdm\u002Fhighspy.svg)](https:\u002F\u002Fpypi.python.org\u002Fpypi\u002Fhighspy)\n\\\n[![NuGet version](https:\u002F\u002Fimg.shields.io\u002Fnuget\u002Fv\u002FHighs.Native.svg)](https:\u002F\u002Fwww.nuget.org\u002Fpackages\u002FHighs.Native)\n[![NuGet download](https:\u002F\u002Fimg.shields.io\u002Fnuget\u002Fdt\u002FHighs.Native.svg)](https:\u002F\u002Fwww.nuget.org\u002Fpackages\u002FHighs.Native)\n\n[fast_build_svg]: https:\u002F\u002Fgithub.com\u002FERGO-Code\u002FHiGHS\u002Factions\u002Fworkflows\u002Fbuild-fast.yml\u002Fbadge.svg\n[fast_build_link]: https:\u002F\u002Fgithub.com\u002FERGO-Code\u002FHiGHS\u002Factions\u002Fworkflows\u002Fbuild-fast.yml\n[linux_build_svg]: https:\u002F\u002Fgithub.com\u002FERGO-Code\u002FHiGHS\u002Factions\u002Fworkflows\u002Fbuild-linux.yml\u002Fbadge.svg\n[linux_build_link]: https:\u002F\u002Fgithub.com\u002FERGO-Code\u002FHiGHS\u002Factions\u002Fworkflows\u002Fbuild-linux.yml\n[macos_build_svg]: https:\u002F\u002Fgithub.com\u002FERGO-Code\u002FHiGHS\u002Factions\u002Fworkflows\u002Fbuild-macos.yml\u002Fbadge.svg\n[macos_build_link]: https:\u002F\u002Fgithub.com\u002FERGO-Code\u002FHiGHS\u002Factions\u002Fworkflows\u002Fbuild-macos.yml\n[windows_build_svg]: https:\u002F\u002Fgithub.com\u002FERGO-Code\u002FHiGHS\u002Factions\u002Fworkflows\u002Fbuild-windows.yml\u002Fbadge.svg\n[windows_build_link]: https:\u002F\u002Fgithub.com\u002FERGO-Code\u002FHiGHS\u002Factions\u002Fworkflows\u002Fbuild-windows.yml\n\n- [About HiGHS](#about-highs)\n- [Documentation](#documentation)\n- [Installation](#installation)\n  - [Build from source using CMake](#build-from-source-using-cmake)\n  - [Build with Meson*](#build-with-meson)\n  - [Build with Nix*](#build-with-nix)\n  - [Precompiled binaries](#precompiled-binaries)\n- [Running HiGHS](#running-highs)\n- [Interfaces](#interfaces)\n  - [Python](#python)\n  - [C](#c)\n  - [CSharp](#csharp)\n  - [Fortran](#fortran)\n- [Reference](#reference)\n\n\u003Ca id=\"about-highs\">\u003C\u002Fa>\n\n## About HiGHS\n\nHiGHS is a high performance serial and parallel solver for large scale sparse\nlinear optimization problems of the form\n\n$$ \\min \\quad \\dfrac{1}{2}x^TQx + c^Tx \\qquad \\textrm{s.t.}~ \\quad L \\leq Ax \\leq U; \\quad l \\leq x \\leq u $$\n\nwhere $Q$ must be positive semi-definite and, if $Q$ is zero, there may be a requirement that some of the variables take integer values. Thus HiGHS can solve linear programming (LP) problems, convex quadratic programming (QP) problems, and mixed integer programming (MIP) problems. It is mainly written in C++, but also has some C. It has been developed and tested on various Linux, MacOS and Windows installations. No third-party dependencies are required.\n\nHiGHS has primal and dual revised simplex solvers, originally written by Qi Huangfu and further developed by Julian Hall. It also has an interior point solver for LP written by Lukas Schork, an active set solver for QP written by Michael Feldmeier, and a MIP solver written by Leona Gottwald. Other features have been added by Julian Hall and Ivet Galabova, who manages the software engineering of HiGHS and interfaces to C, C#, FORTRAN, Julia and Python.\n\nFind out more about HiGHS at https:\u002F\u002Fwww.highs.dev.\n\nAlthough HiGHS is freely available under the MIT license, we would be pleased to learn about users' experience and give advice via email sent to highsopt@gmail.com.\n\n\u003Ca id=\"documentation\">\u003C\u002Fa>\n\n## Documentation\n\nDocumentation is available at https:\u002F\u002Fergo-code.github.io\u002FHiGHS\u002F.\n\n\u003Ca id=\"installation\">\u003C\u002Fa>\n\n## Installation\n\n\u003Ca id=\"build-from-source-using-cmake\">\u003C\u002Fa>\n\n### Build from source using CMake\n\nHiGHS uses CMake as build system, and requires at least version 3.15. To generate build files in a new subdirectory called 'build', run:\n\n```shell\n    cmake -S . -B build\n    cmake --build build\n```\nThis installs the executable `bin\u002Fhighs` and the library `lib\u002Fhighs`.\n\nTo test whether the compilation was successful, change into the build directory and run\n\n```shell\n    ctest\n```\nMore details on building with CMake can be found in `HiGHS\u002Fcmake\u002FREADME.md`.\n\n\u003Ca id=\"build-with-meson\">\u003C\u002Fa>\n\n#### Build with Meson\n\nAs an alternative, HiGHS can be installed using the `meson` build interface:\n``` sh\nmeson setup bbdir -Dwith_tests=True\nmeson test -C bbdir\n```\n_The meson build files are provided by the community and are not officially supported by the HiGHS development team._ **If you use this method and encounter issues, please consider contributing fixes or updates by checking the [HiGHS Contribution Guide](https:\u002F\u002Fgithub.com\u002FERGO-Code\u002FHiGHS\u002Fblob\u002Fmaster\u002FCONTRIBUTING.md).**\n\n\u003Ca id=\"build-with-nix\">\u003C\u002Fa>\n\n#### Build with Nix\n\nThere is a nix flake that provides the `highs` binary:\n\n```shell\nnix run .\n```\n\nYou can even run [without installing\nanything](https:\u002F\u002Fdeterminate.systems\u002Fposts\u002Fnix-run\u002F), supposing you have\ninstalled [nix](https:\u002F\u002Fnixos.org\u002Fdownload.html):\n\n```shell\nnix run github:ERGO-Code\u002FHiGHS\n```\n\nThe nix flake also provides the python package:\n\n```shell\nnix build .#highspy\ntree result\u002F\n```\n\nAnd a devShell for testing it:\n\n```shell\nnix develop .#highspy\npython\n>>> import highspy\n>>> highspy.Highs()\n```\n\n_The nix build files are provided by the community and are not officially supported by the HiGHS development team._\n\n\u003Ca id=\"precompiled-binaries\">\u003C\u002Fa>\n\n### Precompiled binaries\n\nPrecompiled static binaries are available at https:\u002F\u002Fgithub.com\u002FERGO-Code\u002FHiGHS\u002Freleases.\n\nAdditionally, there is one package containing shared libraries for Windows x64.\n\nThe `*-mit` binary packages contain HiGHS and are MIT-licenced.\nThe `*-apache` binary packages contain HiGHS with HiPO and are Apache-licenced, due to the licensing of the dependencies of HiPO. For more information, see [THIRD_PARTY_NOTICES.md](https:\u002F\u002Fgithub.com\u002FERGO-Code\u002FHiGHS\u002Fblob\u002Fmaster\u002FTHIRD_PARTY_NOTICES.md).\n\nIf you have any questions or requests for more platforms and binaries, please get in touch with us at hello@highs.dev.\n\n\u003Ca id=\"running-highs\">\u003C\u002Fa>\n\n## Running HiGHS\n\nHiGHS can read MPS files and (CPLEX) LP files, and the following command\nsolves the model in `ml.mps`\n\n```shell\n    highs ml.mps\n```\n#### Command line options\n\nWhen HiGHS is run from the command line, some fundamental option values may be\nspecified directly. Many more may be specified via a file. Formally, the usage\nis:\n\n```shell\n$ bin\u002Fhighs --help\nusage:\n      .\u002Fbin\u002Fhighs [options] [file]\n\noptions:\n      --model_file file          File of model to solve.\n      --options_file file        File containing HiGHS options.\n      --read_solution_file file  File of solution to read.\n      --read_basis_file text     File of initial basis to read.\n      --write_model_file text    File for writing out model.\n      --solution_file text       File for writing out solution.\n      --write_basis_file text    File for writing out final basis.\n      --presolve text            Set presolve option to:\n                                   \"choose\" * default\n                                   \"on\"\n                                   \"off\"\n      --solver text              Set solver option to:\n                                   \"choose\" * default\n                                   \"simplex\"\n                                   \"ipm\"\n      --parallel text            Set parallel option to:\n                                   \"choose\" * default\n                                   \"on\"\n                                   \"off\"\n      --threads int              Set maximum number of threads to use:\n                                 0: automatic * default\n      --run_crossover text       Set run_crossover option to:\n                                   \"choose\"\n                                   \"on\" * default\n                                   \"off\"\n      --time_limit float         Run time limit (seconds - double).\n      --random_seed int          Seed to initialize random number\n                                 generation.\n      --ranging text             Compute cost, bound, RHS and basic\n                                 solution ranging:\n                                   \"on\"\n                                   \"off\" * default\n  -v, --version                  Print version.\n  -h, --help                     Print help.\n\n```\nFor a full list of options, see the [options page](https:\u002F\u002Fergo-code.github.io\u002FHiGHS\u002Fstable\u002Foptions\u002Fdefinitions\u002F) of the documentation website.\n\n\u003Ca id=\"interfaces\">\u003C\u002Fa>\n\n## Interfaces\n\nThere are HiGHS interfaces for C, C#, FORTRAN, and Python in `HiGHS\u002Fhighs\u002Finterfaces`, with example driver files in `HiGHS\u002Fexamples\u002F`. More on language and modelling interfaces can be found at https:\u002F\u002Fergo-code.github.io\u002FHiGHS\u002Fstable\u002Finterfaces\u002Fother\u002F.\n\nWe are happy to give a reasonable level of support via email sent to highsopt@gmail.com.\n\n\u003Ca id=\"python\">\u003C\u002Fa>\n\n### Python\n\nThe python package `highspy` is a thin wrapper around HiGHS and is available on [PyPi](https:\u002F\u002Fpypi.org\u002Fproject\u002Fhighspy\u002F). It can be easily installed via `pip` by running\n\n```shell\n$ pip install highspy\n```\n\nAlternatively, `highspy` can be built from source.  Download the HiGHS source code and run\n\n```shell\npip install .\n```\nfrom the root directory.\n\nThe python package `highspy` depends on the `numpy` package and `numpy` will be installed as well, if it is not already present.\n\nThe installation can be tested using the small example `HiGHS\u002Fexamples\u002Fcall_highs_from_python_highspy.py`.\n\nThe [Google Colab Example Notebook](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1JmHF53OYfU-0Sp9bzLw-D2TQyRABSjHb?usp=sharing) also demonstrates how to call `highspy`.\n\nTo use HiPO in Python, the additional `highspy-extras` is required. It contains the HiPO dependencies and is Apache 2.0 licensed. It can be installed with\n\n```shell\n$ pip install highspy[extras]\n```\n\nFor more details, see https:\u002F\u002Fergo-code.github.io\u002FHiGHS\u002F at Interfaces -> Python -> HiPO in Python.\n\n\u003Ca id=\"c\">\u003C\u002Fa>\n\n### C\nThe C API is in `HiGHS\u002Fhighs\u002Finterfaces\u002Fhighs_c_api.h`. It is included in the default build. For more details, check out the documentation website https:\u002F\u002Fergo-code.github.io\u002FHiGHS\u002F.\n\n\u003Ca id=\"csharp\">\u003C\u002Fa>\n\n### CSharp\n\nThe nuget package Highs.Native is on https:\u002F\u002Fwww.nuget.org, at https:\u002F\u002Fwww.nuget.org\u002Fpackages\u002FHighs.Native\u002F.\n\nIt can be added to your C# project with `dotnet`\n\n```shell\ndotnet add package Highs.Native --version 1.15.1\n```\n\nThe nuget package contains runtime libraries for\n\n* `win-x64`\n* `win-x86`\n* `linux-x64`\n* `linux-arm64`\n* `macos-x64`\n* `macos-arm64`\n\nDetails for building locally can be found in `nuget\u002FREADME.md`.\n\n\u003Ca id=\"fortran\">\u003C\u002Fa>\n\n### Fortran\n\nThe Fortran API is in `HiGHS\u002Fhighs\u002Finterfaces\u002Fhighs_fortran_api.f90`. It is *not* included in the default build. For more details, check out the documentation website https:\u002F\u002Fergo-code.github.io\u002FHiGHS\u002F.\n\n\n\u003Ca id=\"reference\">\u003C\u002Fa>\n\n## Reference\n\nIf you use HiGHS in an academic context, please acknowledge this and cite the following article.\n\nParallelizing the dual revised simplex method\nQ. Huangfu and J. A. J. Hall\nMathematical Programming Computation, 10 (1), 119-142, 2018.\nDOI: [10.1007\u002Fs12532-017-0130-5](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12532-017-0130-5)\n","HiGHS 是一个高性能开源线性与混合整数优化求解器，支持线性规划（LP）、凸二次规划（QP）和混合整数规划（MIP）问题求解。其核心采用并行化的单纯形法（ primal\u002Fdual revised simplex）和内点法（interior-point method），具备稀疏矩阵高效处理能力，无需第三方依赖，提供 C++、C、Python、C# 和 Fortran 多语言接口。适用于运筹优化、供应链调度、能源系统建模、金融资产配置等需要大规模数值优化的工业与科研场景。",2,"2026-07-09 02:30:21","trending"]