Highs.Native 1.7.0

HiGHS - Linear optimization software

• HiGHS - Linear optimization software
  • About HiGHS
  • Documentation
  • Installation
    • Build from source using CMake
    • Precompiled binaries
  • Interfaces
    • Python
    • CSharp
  • Reference

About HiGHS

HiGHS is a high performance serial and parallel solver for large scale sparse linear 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 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 highsopt@gmail.com.

Documentation

Documentation is available at https://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 executable bin/highs and the library lib/highs.

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

    ctest

HiGHS can read MPS files and (CPLEX) LP files, and the following command solves the model in ml.mps

    highs ml.mps

HiGHS is installed using the command

    cmake --install build

with the optional setting of --prefix <prefix>, or the cmake option CMAKE_INSTALL_PREFIX if it is to be installed anywhere other than the default location.

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

meson setup bbdir -Dwith_tests=True
meson test -C bbdir

The meson 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 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.

Build with Nix

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/HiGHS

The 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.

Interfaces

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 highsopt@gmail.com.

Python

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 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 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 call_highs_from_python_highspy.py.

The Google Colab Example Notebook also demonstrates how to call highspy.

CSharp

The nuget package Highs.Native is on https://www.nuget.org, at https://www.nuget.org/packages/HiGHS/.

It can be added to your C# project with dotnet

dotnet add package Highs.Native --version 1.7.0

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 in nuget/README.md.

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. Hall Mathematical Programming Computation, 10 (1), 119-142, 2018. DOI: 10.1007/s12532-017-0130-5