2022-07-27 –, JuMP
This talk will describe how the JuMP and HiGHS teams have worked together to deliver the best open-source linear optimization solvers to the Julia community, and present some high-profile use cases.
Almost from the moment that the development of HiGHS was proposed in 2018, the prospect of it offering top-class open-source linear optimization solvers with a well designed and fully supported API was attractive to JuMP. Since then, as HiGHS has developed from outstanding "gradware" to the world's best open-source linear optimization software, there have been invaluable contributions from the JuMP team. This great example of community cooperation now means that HiGHS is the default MILP solver in JuMP's documentation, and Julia users have a slick interface to HiGHS. One particular area of activity that is exploiting this is the rapidly-growing world of open-source energy systems planning, where the high license fees for commercial optimization solvers mean that open-source alternatives are critically important for small scale commercial enterprises, NGOs, and organisations in developing countries. Some high-profile use cases of the JuMP-HiGHS interface in this field will be presented.
I have been developing software for linear optimization since my time as a PhD student with Roger Fletcher in the late 1980's. A lecturer at the University of Edinburgh since 1990, my research has focused on serial and parallel computing techniques for implementing the simplex algorithm for linear programming. This has resulted in the development of HiGHS, the world's best open-source software for linear optimization.