Integrality gap minimization heuristics for binary mixed integer nonlinear programming

We present two feasibility heuristics for binary mixed integer nonlinear programming. Called integrality gap minimization algorithm (IGMA)—versions 1 and 2, our heuristics are based on the solution of integrality gap minimization problems with a space partitioning scheme defined over the integer var...

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Veröffentlicht in:Journal of global optimization Jg. 71; H. 3; S. 593 - 612
Hauptverfasser: Melo, Wendel, Fampa, Marcia, Raupp, Fernanda
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.07.2018
Springer
Springer Nature B.V
Schlagworte:
Algorithms
Computer Science
Heuristic
Mathematics
Mathematics and Statistics
Mixed integer
Nonlinear programming
Operations Research/Decision Theory
Optimization
Real Functions
Integrality gap minimization
Solver
Binary mixed integer nonlinear programming
Heuristics
ISSN:0925-5001, 1573-2916
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Zusammenfassung:We present two feasibility heuristics for binary mixed integer nonlinear programming. Called integrality gap minimization algorithm (IGMA)—versions 1 and 2, our heuristics are based on the solution of integrality gap minimization problems with a space partitioning scheme defined over the integer variables of the problem addressed. Computational results on a set of benchmark instances show that the proposed approaches present satisfactory results.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-018-0623-4