Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Facets of Combinatorial Optimization |
| Untertitel | Festschrift for Martin Grötschel |
| Seiten | 295-320 |
| Seitenumfang | 26 |
| Band | 9783642381898 |
| ISBN (elektronisch) | 9783642381898 |
| Publikationsstatus | Veröffentlicht - 1 Jan. 2013 |
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.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Allgemeine Mathematik
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Facets of Combinatorial Optimization: Festschrift for Martin Grötschel. Band 9783642381898 2013. S. 295-320.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review
}
TY - CHAP
T1 - A primal heuristic for nonsmooth mixed integer nonlinear optimization
AU - Schmidt, Martin
AU - Steinbach, Marc C.
AU - Willert, Bernhard M.
PY - 2013/1/1
Y1 - 2013/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84929901862&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-38189-8_13
DO - 10.1007/978-3-642-38189-8_13
M3 - Contribution to book/anthology
AN - SCOPUS:84929901862
SN - 364238188X
SN - 9783642381881
VL - 9783642381898
SP - 295
EP - 320
BT - Facets of Combinatorial Optimization
ER -