• search hit 20 of 84
Back to Result List

Computing Feasible Points for Binary MINLPs with MPECs

  • 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.

Download full text files

Export metadata

Metadaten
Author:Lars Schewe, Martin Schmidt
Parent Title (German):Mathematical Programming Computation
Document Type:Article
Language:English
Date of Publication (online):2016/12/21
Date of first Publication:2016/12/21
Release Date:2016/12/21
Tag:Complementarity Constraints; MINLP; MPEC; Mixed-Integer Nonlinear Optimization; Primal Heuristic
Issue:11(1)
Page Number:24
First Page:95
Last Page:118
Institutes:Friedrich-Alexander-Universität Erlangen-Nürnberg
Subprojects:B07
B08