Computing Feasible Points for Binary MINLPs with MPECs

Journal article


Publication Details

Author(s): Schewe L, Schmidt M
Journal: Mathematical Programming Computation
Publication year: 2018
ISSN: 1867-2949
eISSN: 1867-2957
Language: English


Abstract

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 Authors / FAU Editors

Schewe, Lars PD Dr.
Economics - Discrete Optimization - Mathematics (EDOM)
Schmidt, Martin Prof. Dr.
Juniorprofessur für Optimierung von Energiesystemen


How to cite

APA:
Schewe, L., & Schmidt, M. (2018). Computing Feasible Points for Binary MINLPs with MPECs. Mathematical Programming Computation. https://dx.doi.org/10.1007/s12532-018-0141-x

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

BibTeX: 

Last updated on 2018-06-08 at 12:38