A Primal Heuristic for Nonsmooth Mixed Integer Nonlinear Optimization
Schmidt M, Steinbach MC, Willert BM (2013)
Publication Language: English
Publication Type: Book chapter / Article in edited volumes
Publication year: 2013
Publisher: Springer Berlin Heidelberg
Edited Volumes: Facets of Combinatorial Optimization
Pages Range: 295-320
ISBN: 978-3-642-38188-1
DOI: 10.1007/978-3-642-38189-8_13
Abstract
Complex real-world optimization tasks often lead to mixed-integer nonlinear problems (MINLPs). However, current MINLP algorithms are not always able to solve the resulting large-scale problems. One remedy is to develop problem specific primal heuristics that quickly deliver feasible solutions. This paper presents such a primal heuristic for a certain class of MINLP models. Our approach features a clear distinction between nonsmooth but continuous and genuinely discrete aspects of the model. The former are handled by suitable smoothing techniques; for the latter we employ reformulations using complementarity constraints. The resulting mathematical programs with equilibrium constraints (MPEC) are finally regularized to obtain MINLP-feasible solutions with general purpose NLP solvers.
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Germany (DE)How to cite
APA:
Schmidt, M., Steinbach, M.C., & Willert, B.M. (2013). A Primal Heuristic for Nonsmooth Mixed Integer Nonlinear Optimization. In Jünger M, Reinelt G (Eds.), Facets of Combinatorial Optimization. (pp. 295-320). Springer Berlin Heidelberg.
MLA:
Schmidt, Martin, Marc C. Steinbach, and Bernhard M. Willert. "A Primal Heuristic for Nonsmooth Mixed Integer Nonlinear Optimization." Facets of Combinatorial Optimization. Ed. Jünger M, Reinelt G, Springer Berlin Heidelberg, 2013. 295-320.
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