HiGHS - Linear optimization software

• About HiGHS
• Documentation
• Installation
  • Build from source using CMake
  • Build with Meson*
  • Build with Nix*
  • Precompiled binaries
• Running HiGHS
• Interfaces
  • Python
  • C
  • CSharp
  • Fortran
• Reference

About HiGHS

HiGHS is a high performance serial and parallel solver for large scale sparselinear optimization problems of the form

$$ \min \quad \dfrac{1}{2}x^TQx + c^Tx \qquad \textrm{s.t.}~ \quad L \leq Ax \leq U; \quad l \leq x \leq u $$

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 athttps://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 highsopt@gmail.com.

Documentation

Documentation is available athttps://ergo-code.github.io/HiGHS/.

Installation

Build from source using CMake

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 build

This installs the executablebin/highs and the librarylib/highs.

To test whether the compilation was successful, change into the build directory and run

    ctest

More details on building with CMake can be found inHiGHS/cmake/README.md.

Build with Meson

As an alternative, HiGHS can be installed using themeson build interface:

meson setup bbdir -Dwith_tests=Truemeson test -C bbdir

The meson build files are provided by the community and are not officially supported by the HiGHS development team.

Build with Nix

There is a nix flake that provides thehighs binary:

nix run .

You can even runwithout installinganything, supposing you haveinstallednix:

nix run github:ERGO-Code/HiGHS

The nix flake also provides the python package:

nix build .#highspytree result/

And a devShell for testing it:

nix develop .#highspypython>>> import highspy>>> highspy.Highs()

The nix build files are provided by the community and are not officially supported by the HiGHS development team.

Precompiled binaries

Precompiled static executables are available for a variety of platforms athttps://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.

Seehttps://ergo-code.github.io/HiGHS/stable/installation/#Precompiled-Binaries.

Running HiGHS

HiGHS can read MPS files and (CPLEX) LP files, and the following commandsolves the model inml.mps

    highs ml.mps

Command line options

When HiGHS is run from the command line, some fundamental option values may bespecified directly. Many more may be specified via a file. Formally, the usageis:

$ bin/highs --helpusage:      ./bin/highs [options] [file]options:      --model_file file          File of model to solve.      --options_file file        File containing HiGHS options.      --read_solution_file file  File of solution to read.      --presolve text            Set presolve option to:                                    "choose" * default                                    "on"                                   "off"      --solver text              Set solver option to:                                    "choose" * default                                    "simplex"                                   "ipm"       --parallel text            Set parallel option to:                                    "choose" * default                                    "on"                                    "off"       --run_crossover text       Set run_crossover option to:                                    "choose"                                    "on" * default                                    "off"       --time_limit float         Run time limit (seconds - double).      --solution_file text       File for writing out model solution.      --read_basis_file text     File for initial basis to read.       --write_basis_file text    File for final basis to write.       --write_model_file text    File for writing out model.      --random_seed int          Seed to initialize random number                                  generation.      --ranging text             Compute cost, bound, RHS and basic                                  solution ranging:                                   "on"                                   "off" * default   -v, --version                  Print version.  -h, --help                     Print help.

For a full list of options, see theoptions page of the documentation website.

Interfaces

There are HiGHS interfaces for C, C#, FORTRAN, and Python inHiGHS/src/interfaces, with example driver files inHiGHS/examples/. More on language and modelling interfaces can be found athttps://ergo-code.github.io/HiGHS/stable/interfaces/other/.

We are happy to give a reasonable level of support via email sent to highsopt@gmail.com.

Python

The python packagehighspy is a thin wrapper around HiGHS and is available onPyPi. It can be easily installed viapip by running

$ pip install highspy

Alternatively,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 packagehighspy depends on thenumpy package andnumpy will be installed as well, if it is not already present.

The installation can be tested using the small exampleHiGHS/examples/call_highs_from_python_highspy.py.

TheGoogle Colab Example Notebook also demonstrates how to callhighspy.

C

The C API is inHiGHS/src/interfaces/highs_c_api.h. It is included in the default build. For more details, check out the documentation websitehttps://ergo-code.github.io/HiGHS/.

CSharp

The nuget package Highs.Native is onhttps://www.nuget.org, athttps://www.nuget.org/packages/Highs.Native/.

It can be added to your C# project withdotnet

dotnet add package Highs.Native --version 1.10.0

The nuget package contains runtime libraries for

• win-x64
• win-x32
• linux-x64
• linux-arm64
• macos-x64
• macos-arm64

Details for building locally can be found innuget/README.md.

Fortran

The Fortran API is inHiGHS/src/interfaces/highs_fortran_api.f90. It isnot included in the default build. For more details, check out the documentation websitehttps://ergo-code.github.io/HiGHS/.

Reference

If you use HiGHS in an academic context, please acknowledge this and cite the following article.

Parallelizing the dual revised simplex method

17. Huangfu and J. A. J. HallMathematical Programming Computation, 10 (1), 119-142, 2018.DOI:10.1007/s12532-017-0130-5