On solving nonconvex MINLP problems with SHOT

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The Supporting Hyperplane Optimization Toolkit (SHOT) solver was originally developed for solving convex MINLP problems, for which it has proven to be very efficient. In this paper, we describe some techniques and strategies implemented in SHOT for improving its performance on nonconvex problems. These include utilizing an objective cut to force an update of the best known solution and strategies for handling infeasibilities resulting from supporting hyperplanes and cutting planes generated from nonconvex constraint functions. For convex problems, SHOT gives a guarantee to find the global optimality, but for general nonconvex problems it will only be a heuristic. However, utilizing some automated transformations it is actually possible in some cases to reformulate all nonconvexities into linear form, ensuring that the obtained solution is globally optimal. Finally, SHOT is compared to other MINLP solvers on a few nontrivial test problems to illustrate its performance.

Titel på gästpublikationOptimization of complex systems: Theory, models, algorithms and applications
RedaktörerHoai An Le Thi, Hoai Minh Le, Tao Pham Dinh
ISBN (elektroniskt)978-3-030-21803-4
ISBN (tryckt)978-3-030-21802-7
StatusPublicerad - 2020
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangWorld Congress on Global Optimization (WCGO) - 6th World Congress on Global Optimization, WCGO 2019
Varaktighet: 8 jul 201910 jul 2019


Namn Advances in Intelligent Systems and Computing
ISSN (tryckt)2194-5357
ISSN (elektroniskt)2194-5365


KonferensWorld Congress on Global Optimization (WCGO)


  • Feasibility relaxation
  • Nonconvex MINLP
  • Reformulation techniques
  • Supporting Hyperplane Optimization Toolkit (SHOT)

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