HiGHS - Linear optimization software

Table of Contents

• About HiGHS
• Documentation
• Precompiled binaries
• Compilation
• Interfaces
• Python
• Example
• 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 [email protected].

Documentation

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

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/binaries.html.

Compilation

HiGHS uses CMake as build system, and requires at least version 3.15. First setup a build folder and call CMake as follows

mkdir build
cd build
cmake ..

Then compile the code using

cmake --build .

This installs the executable bin/highs.

As an alternative it is also possible to let cmake create the build folder and thus build everything from the HiGHS directory, as follows

cmake -S . -B build
cmake --build build

To test whether the compilation was successful, 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 .

with the optional setting of --prefix <prefix> = The installation prefix CMAKE_INSTALL_PREFIX if it is to be installed anywhere other than the default location.

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/interfaces.html.

We are happy to give a reasonable level of support via email sent to [email protected].

Python

There are two ways to build the Python interface to HiGHS.

From PyPi

HiGHS has been added to PyPi, so should be installable using the command

pip install highspy

The installation can be tested using the example minimal.py, yielding the output

Running HiGHS 1.5.0 [date: 2023-02-22, git hash: d041b3da0]
Copyright (c) 2023 HiGHS under MIT licence terms
Presolving model
2 rows, 2 cols, 4 nonzeros
0 rows, 0 cols, 0 nonzeros
0 rows, 0 cols, 0 nonzeros
Presolve : Reductions: rows 0(-2); columns 0(-2); elements 0(-4) - Reduced to empty
Solving the original LP from the solution after postsolve
Model   status      : Optimal
Objective value     :  1.0000000000e+00
HiGHS run time      :          0.00

or the more didactic call_highs_from_python.py.

Directly

In order to build the Python interface, build and install the HiGHS library as described above, ensure the shared library is in the LD_LIBRARY_PATH environment variable, and then run

pip install ./

from the HiGHS directory.

You may also require

• pip install pybind11
• pip install pyomo

The Python interface can then be tested as above.

Google Colab Example

The Google Colab Example Notebook demonstrates how to call HiGHS via the Python interface highspy.

Reference

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