Abstract
Nonconvex mixedbinary 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 mixedbinary 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 instances in at most 10s.
Original language  English 

Pages (fromto)  95118 
Number of pages  24 
Journal  Mathematical Programming Computation 
Volume  11 
Issue number  1 
Early online date  23 Aug 2018 
DOIs  
Publication status  Published  14 Mar 2019 
Keywords
 Complementarity constraints
 MINLP
 Mixedinteger nonlinear optimization
 MPEC
 Primal heuristic
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Lars Schewe
 School of Mathematics  Lecturer in Operational Research
Person: Academic: Research Active (Teaching)