Title

HiGHS: Turning Gradware into Software and Impact

Abstract

HiGHS is open-source optimization software for linear programming (LP), mixed-integer programming (MIP) and quadratic programming (QP). This talk will give an insight into the state-of-the-art techniques underlying its solvers, most of which were originally written as “gradware” by PhD students. Independent benchmark results will be given to justify the claim that HiGHS is the world’s best open-source linear optimization software, in particular when solving LPs by interior point. However, the interior point solver can still be uncompetitive with commercial solvers. This has been noticed particularly in the context of energy systems, and led to major funding for the development of a new interior point solver. This talk will discuss our work in this area, as well as providing an update on more general advances in HiGHS. The team developing HiGHS was responsible for a 2021 REF Impact Case Study, and has the potential to generate further Impact for the next REF, so observations on the creation of Impact via software development will be given.

Bio

Julian Hall obtained his PhD from the University of Dundee under the supervision of Roger Fletcher and, since 1990, has been employed as a lecturer in the School of Mathematics at the University of Edinburgh. His main research interest has been developing algorithmic and computational techniques for solving large scale linear programming (LP) problems using the revised simplex method on both serial and parallel computers. In collaboration with Ivet Galabova, and based on solvers written by Leona Gottwald and former PhD students Michael Feldmeier and Qi Huangfu, he is managing the development of the world’s best open-source linear optimization software HiGHS.