HiGHS - high performance software
for linear optimization

Open source serial and parallel solvers for large-scale
sparse linear programming (LP),
mixed-integer programming (MIP), and quadratic programming (QP) models

Get started

HiGHS is high performance serial and parallel software for solving large-scale sparse linear programming (LP), mixed-integer programming (MIP) and quadratic programming (QP) models, developed in C++11, with interfaces to C, C#, FORTRAN, Julia and Python.

HiGHS is freely available under the MIT licence, and is downloaded from GitHub. Installing HiGHS from source code requires CMake minimum version 3.15, but no other third-party utilities. HiGHS can be used as a stand-alone executable on Windows, Linux and MacOS. There is a C++11 library which can be used within a C++ project or, via one of the interfaces, to a project written in other languages.

Your comments or specific questions on HiGHS would be greatly appreciated, so please send an email to highsopt@gmail.com to get in touch with the team.

Documentation

Information on how to set up and use HiGHS is given in the HiGHS Documentation page.

HiGHS Workshop 2024

The first HiGHS workshop will take place in Edinburgh on 26-28th June 2024: the end of the week before the EURO Operational Research conference in Copenhagen. With major industrial and academic users of HiGHS already committed to attending, this will be an opportunity to make connections with other HiGHS users and help shape the project's future. For more details, please refer to the 2024 HiGHS Workshop Website .

Welcome to the HiGHS Newsletter!



HiGHS_Newsletter_24_0.pdf
HiGHS_Newsletter_24_1.pdf

Background

Authorship

HiGHS is based on the high performance dual revised simplex solver for LP developed by Qi Huangfu, the novel interior point solver for LP developed by Lukas Schork, the active set QP solver written by Michael Feldmeier, and the branch-and-cut MIP solver written by Leona Gottwald. The project is managed by Julian Hall, and Ivet Galabova continues to develop and maintain the underlying software engineering.

Citation

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

The Team

Julian Hall
Julian Hall
Ivet Galabova
Ivet Galabova
Filippo Zanetti
Filippo Zanetti

Sponsors

Contact

Your comments or specific questions on HiGHS would be greatly appreciated, so please send an email to highsopt@gmail.com to get in touch with the team.

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