On solving nonconvex MINLP problems with SHOT

A4 Konferenspublikationer

Interna författare/redaktörer

Publikationens författare: Andreas Lundell, Jan Kronqvist
Redaktörer: Hoai An Le Thi, Hoai Minh Le, Tao Pham Dinh
Publiceringsår: 2020
Förläggare: Springer
Moderpublikationens namn: Optimization of complex systems: Theory, models, algorithms and applications
Seriens namn: Advances in intelligent systems and computing
Volym: 991
Artikelns första sida, sidnummer: 448
Artikelns sista sida, sidnummer: 457
ISBN: 978-3-030-21802-7
eISBN: 978-3-030-21803-4
ISSN: 2194-5357


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.


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

Senast uppdaterad 2020-30-03 vid 08:54