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 MINLPLib2instances, where our methods are able to produce feasible solutions for 60% of all instanc

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Metadaten
Author:Lars ScheweORCiD, Martin SchmidtORCiD
DOI:https://doi.org/10.1007/s12532-018-0141-x
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2018
Tag:Software; Theoretical Computer Science
Volume:11
Issue:1
Page Number:24
First Page:95
Last Page:118
Mathematical Programming Computation :MPC 2019 - Issue 1
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