TY - GEN
T1 - On solving nonconvex MINLP problems with SHOT
AU - Lundell, Andreas
AU - Kronqvist, Jan
N1 - ast.
Includes the proceedings of the WCGO 2019—6th World Congress on Global Optimization, held on July 8–10 2019 in Metz, France
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Embargo: 12 months (tryckta utkom ren 19.6.2019, trots att det står 2020 i boken, så sätt 1.1.2021 som datum)
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Har kontaktat andreas.lundell@abo.fi den 26.2.2020/LN
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PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Feasibility relaxation
KW - Nonconvex MINLP
KW - Reformulation techniques
KW - Supporting Hyperplane Optimization Toolkit (SHOT)
KW - Feasibility relaxation
KW - Nonconvex MINLP
KW - Reformulation techniques
KW - Supporting Hyperplane Optimization Toolkit (SHOT)
KW - Feasibility relaxation
KW - Nonconvex MINLP
KW - Reformulation techniques
KW - Supporting Hyperplane Optimization Toolkit (SHOT)
U2 - 10.1007/978-3-030-21803-4_45
DO - 10.1007/978-3-030-21803-4_45
M3 - Conference contribution
SN - 978-3-030-21802-7
T3 - Advances in Intelligent Systems and Computing
SP - 448
EP - 457
BT - Optimization of complex systems: Theory, models, algorithms and applications
A2 - An Le Thi, Hoai
A2 - Minh Le, Hoai
A2 - Pham Dinh, Tao
PB - Springer
T2 - World Congress on Global Optimization (WCGO)
Y2 - 8 July 2019 through 10 July 2019
ER -