HiGHS - high performance
software
for linear optimization
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 [email protected] 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 .
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
Support us
We greatly appreciate the financial help from our users, allowing us to improve and enhance our solvers! Donations are welcome via GitHub Sponsors at the HiGHS Sponsors page.
Tax-deductible donations are also welcome through the Linux Foundation at the HiGHS donation page.
Our interior point solver for LP has been championed as a game changer by the open-source energy systems planning community, and this has led to a "common good" funding campaign for its enhancement and development.
Sponsors
Contact
Your comments or specific questions on HiGHS would be greatly appreciated, so please send an email to [email protected] to get in touch with the team.