Computing Feasible Points for Binary MINLPs with MPECs

Beitrag in einer Fachzeitschrift

Details zur Publikation

Autor(en): Schewe L, Schmidt M
Zeitschrift: Mathematical Programming Computation
Jahr der Veröffentlichung: 2018
ISSN: 1867-2949
eISSN: 1867-2957
Sprache: Englisch


Nonconvex mixed-binary nonlinear optimization problems frequently appear in practice and are typically extremely hard to solve. In this paper we discuss a class of primal heuristics that are based on a reformulation of the problem as a mathematical program with equilibrium constraints. We then use different regularization schemes for this class of problems and use an iterative solution procedure for solving series of regularized problems. In the case of success, these procedures result in a feasible solution of the original mixed-binary nonlinear problem. Since we rely on local nonlinear programming solvers the resulting method is fast and we further improve its reliability by additional algorithmic techniques. We show the strength of our method by an extensive computational study on 662 MINLPLib2 instances, where our methods are able to produce feasible solutions for 60% of all instances in at most 10s.

FAU-Autoren / FAU-Herausgeber

Schewe, Lars PD Dr.
Lehrstuhl für Wirtschaftsmathematik
Schmidt, Martin Prof. Dr.
Juniorprofessur für Optimierung von Energiesystemen


Schewe, L., & Schmidt, M. (2018). Computing Feasible Points for Binary MINLPs with MPECs. Mathematical Programming Computation.

Schewe, Lars, and Martin Schmidt. "Computing Feasible Points for Binary MINLPs with MPECs." Mathematical Programming Computation (2018).


Zuletzt aktualisiert 2019-02-01 um 15:10