HiGHS is a high performance serial and parallel solver for large scale sparse linear optimization problems of the form
where 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.
HiGHS 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.
Find out more about HiGHS at https://www.highs.dev.
Although HiGHS is freely available under the MIT license, we would be pleased to learn about users' experience and give advice via email sent to [email protected].
Documentation is available at https://ergo-code.github.io/HiGHS/.
HiGHS uses CMake as build system, and requires at least version 3.15. To generate build files in a new subdirectory called 'build', run:
cmake -S . -B build
cmake --build buildThis installs the executable bin/highs and the library lib/highs.
To test whether the compilation was successful, change into the build directory and run
ctestMore details on building with CMake can be found in HiGHS/cmake/README.md.
As an alternative, HiGHS can be installed using the meson build interface:
meson setup bbdir -Dwith_tests=True
meson test -C bbdirThe meson build files are provided by the community and are not officially supported by the HiGHS development team.
There is a nix flake that provides the highs binary:
nix run .You can even run without installing anything, supposing you have installed nix:
nix run github:ERGO-Code/HiGHSThe nix flake also provides the python package:
nix build .#highspy
tree result/And a devShell for testing it:
nix develop .#highspy
python
>>> import highspy
>>> highspy.Highs()The nix build files are provided by the community and are not officially supported by the HiGHS development team.
Precompiled static executables are available for a variety of platforms at https://github.com/JuliaBinaryWrappers/HiGHSstatic_jll.jl/releases
These binaries are provided by the Julia community and are not officially supported by the HiGHS development team. If you have trouble using these libraries, please open a GitHub issue and tag @odow in your question.
See https://ergo-code.github.io/HiGHS/stable/installation/#Precompiled-Binaries.
HiGHS can read MPS files and (CPLEX) LP files, and the following command
solves the model in ml.mps
highs ml.mpsWhen HiGHS is run from the command line, some fundamental option values may be specified directly. Many more may be specified via a file. Formally, the usage is:
$ bin/highs --help
HiGHS options
Usage:
bin/highs [OPTION...] [file]
--model_file arg File of model to solve.
--read_solution_file arg File of solution to read.
--options_file arg File containing HiGHS options.
--presolve arg Presolve: "choose" by default - "on"/"off"
are alternatives.
--solver arg Solver: "choose" by default - "simplex"/"ipm"
are alternatives.
--parallel arg Parallel solve: "choose" by default -
"on"/"off" are alternatives.
--run_crossover arg Run crossover: "on" by default -
"choose"/"off" are alternatives.
--time_limit arg Run time limit (seconds - double).
--solution_file arg File for writing out model solution.
--write_model_file arg File for writing out model.
--random_seed arg Seed to initialize random number generation.
--ranging arg Compute cost, bound, RHS and basic solution
ranging.
--version Print version.
-h, --help Print help.
For a full list of options, see the options page of the documentation website.
There are HiGHS interfaces for C, C#, FORTRAN, and Python in HiGHS/src/interfaces, with example driver files in HiGHS/examples/. More on language and modelling interfaces can be found at https://ergo-code.github.io/HiGHS/stable/interfaces/other/.
We are happy to give a reasonable level of support via email sent to [email protected].
The python package highspy is a thin wrapper around HiGHS and is available on PyPi. It can be easily installed via pip by running
$ pip install highspyAlternatively, highspy can be built from source. Download the HiGHS source code and run
pip install .from the root directory.
The HiGHS C++ library no longer needs to be separately installed. The python package highspy depends on the numpy package and numpy will be installed as well, if it is not already present.
The installation can be tested using the small example HiGHS/examples/call_highs_from_python_highspy.py.
The Google Colab Example Notebook also demonstrates how to call highspy.
The C API is in HiGHS/src/interfaces/highs_c_api.h. It is included in the default build. For more details, check out the documentation website https://ergo-code.github.io/HiGHS/.
The nuget package Highs.Native is on https://www.nuget.org, at https://www.nuget.org/packages/Highs.Native/.
It can be added to your C# project with dotnet
dotnet add package Highs.Native --version 1.8.1The nuget package contains runtime libraries for
win-x64win-x32linux-x64linux-arm64macos-x64macos-arm64
Details for building locally can be found in nuget/README.md.
The Fortran API is in HiGHS/src/interfaces/highs_fortran_api.f90. It is not included in the default build. For more details, check out the documentation website https://ergo-code.github.io/HiGHS/.
If you use HiGHS in an academic context, please acknowledge this and cite the following article.
Parallelizing the dual revised simplex method Q. Huangfu and J. A. J. Hall Mathematical Programming Computation, 10 (1), 119-142, 2018. DOI: 10.1007/s12532-017-0130-5
