SHOT Webinar

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Published: 15 Apr, 2021

SHOT (Supporting Hyperplane Optimization Toolkit) is a deterministic solver for mixed-integer nonlinear programming problems (MINLPs).

Originally, SHOT was intended for convex MINLP problems only, but now also has functionality to solve nonconvex MINLP problems as a heuristic method without providing guarantees of global optimality. However, SHOT can solve certain nonconvex problem types to global optimality as well. For convex MINLP problems, SHOT is among the most efficient solvers (see ) and is guaranteed to find the global optimal solution. SHOT can be run as fully open source with CBC and IPOPT as subsolvers, but the performance is significantly improved by using either CPLEX or GUROBI as a subsolver.

SHOT is mainly developed by Andreas Lundell (Åbo Akademi University, Finland) and Jan Kronqvist (Imperial College London, UK).

In this webinar, the two developers explain the basics of their algorithm, and how to utilise SHOT from GAMS.

This webinar has been recorded in November 2020.


Andreas Lundell

Department of Information Technologies
Department of Mathematics
Åbo Akademi University, Finland

Andreas is currently a researcher at the Department of Information Technologies at Åbo Akademi University (ÅAU) in Finland. His research is mainly focused on global optimization and mixed-integer nonlinear programming (MINLP).

Andreas received his PhD in applied mathematics from ÅAU in 2009, and has since then been involved in several optimization-related research projects. One of these is the development of the SHOT solver, for which he is currently the project manager. Since 2013 he is an adjunct professor at ÅAU.

Jan Kronqvist

Faculty of Engineering
Department of Computing
Imperial College, London, UK

Jan has just finished a 2-year postdoc at Imperial College London, and will start as Assistant Professor in Optimization and Systems Theory at KTH Royal Institute of Technology in Sweden in May 2021. His research is focused on mixed-integer optimization, specifically in theory and algorithms for mixed-integer nonlinear programming (MINLP) and applications of mixed-integer optimization in machine learning and artificial intelligence.

Jan graduated in 2018 with honors from Åbo Akademi University in Finland, and was awarded best PhD thesis at the Faculty of Science and Engineering. After his PhD, he was awarded a Newton International Fellowship by the Royal Society in 2018, and a grant by the Foundations Post Doc Pool (given by the Swedish Cultural Foundation in Finland) to support his postdoc research. From 2019 to 2021, Jan worked as a postdoc at Imperial College London (Royal Society- Newton International Fellow).