Software

HiGHS is open-source software for linear optimization that has been developed by Julian Hall and Ivet Galabova using solvers written by them, PhD students and a contractor. It has simplex and interior point solvers for linear programming (LP), mixed-integer programming (MIP), and quadratic programming (QP). Its overall performance in the industry standard benchmarks exceeds that of any other open-source linear optimization software in the world. HiGHS provides the default LP and MIP solvers in MATLAB and the SciPy system, the MIP solver in the NAG library, and is the documented solver in the popular modern Julia-based modelling and optimization system JuMP. Written in C++, there are APIs to allow HiGHS to be called from C, C#, FORTRAN, Javascript, Julia and Rust. HiGHS is available as a solver for the modelling languages AMPL, GAMS, Pyomo and PuLP. HiGHS is callable from R. The highspy Python interface and modelling language is available from PyPI. Future plans will see HiGHS available in Google's OR-Tools. HiGHS is available from Github under the MIT license. 

Contact HighsOpt@gmail.com, follow HiGHS on Twitter and LinkeIn, and read our permanent record on Wikipedia.