On the solution of nonconvex cardinality Boolean quadratic programming problems: a computational study

Handle URI:
http://hdl.handle.net/10754/621502
Title:
On the solution of nonconvex cardinality Boolean quadratic programming problems: a computational study
Authors:
Lima, Ricardo ( 0000-0002-5735-6089 ) ; Grossmann, Ignacio E.
Abstract:
This paper addresses the solution of a cardinality Boolean quadratic programming problem using three different approaches. The first transforms the original problem into six mixed-integer linear programming (MILP) formulations. The second approach takes one of the MILP formulations and relies on the specific features of an MILP solver, namely using starting incumbents, polishing, and callbacks. The last involves the direct solution of the original problem by solvers that can accomodate the nonlinear combinatorial problem. Particular emphasis is placed on the definition of the MILP reformulations and their comparison with the other approaches. The results indicate that the data of the problem has a strong influence on the performance of the different approaches, and that there are clear-cut approaches that are better for some instances of the data. A detailed analysis of the results is made to identify the most effective approaches for specific instances of the data. © 2016 Springer Science+Business Media New York
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Lima RM, Grossmann IE (2016) On the solution of nonconvex cardinality Boolean quadratic programming problems: a computational study. Comput Optim Appl. Available: http://dx.doi.org/10.1007/s10589-016-9856-7.
Publisher:
Springer Nature
Journal:
Computational Optimization and Applications
Issue Date:
16-Jun-2016
DOI:
10.1007/s10589-016-9856-7
Type:
Article
ISSN:
0926-6003; 1573-2894
Sponsors:
Fundação para a Ciência e a Tecnologia[DFRH/WIIA/67/2011]; European Union Seventh Framework Programme[PCOFUND-GA-2009-246542]
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorLima, Ricardoen
dc.contributor.authorGrossmann, Ignacio E.en
dc.date.accessioned2016-11-03T08:30:54Z-
dc.date.available2016-11-03T08:30:54Z-
dc.date.issued2016-06-16en
dc.identifier.citationLima RM, Grossmann IE (2016) On the solution of nonconvex cardinality Boolean quadratic programming problems: a computational study. Comput Optim Appl. Available: http://dx.doi.org/10.1007/s10589-016-9856-7.en
dc.identifier.issn0926-6003en
dc.identifier.issn1573-2894en
dc.identifier.doi10.1007/s10589-016-9856-7en
dc.identifier.urihttp://hdl.handle.net/10754/621502-
dc.description.abstractThis paper addresses the solution of a cardinality Boolean quadratic programming problem using three different approaches. The first transforms the original problem into six mixed-integer linear programming (MILP) formulations. The second approach takes one of the MILP formulations and relies on the specific features of an MILP solver, namely using starting incumbents, polishing, and callbacks. The last involves the direct solution of the original problem by solvers that can accomodate the nonlinear combinatorial problem. Particular emphasis is placed on the definition of the MILP reformulations and their comparison with the other approaches. The results indicate that the data of the problem has a strong influence on the performance of the different approaches, and that there are clear-cut approaches that are better for some instances of the data. A detailed analysis of the results is made to identify the most effective approaches for specific instances of the data. © 2016 Springer Science+Business Media New Yorken
dc.description.sponsorshipFundação para a Ciência e a Tecnologia[DFRH/WIIA/67/2011]en
dc.description.sponsorshipEuropean Union Seventh Framework Programme[PCOFUND-GA-2009-246542]en
dc.publisherSpringer Natureen
dc.subjectComputing scienceen
dc.titleOn the solution of nonconvex cardinality Boolean quadratic programming problems: a computational studyen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalComputational Optimization and Applicationsen
dc.contributor.institutionDepartment of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, United Statesen
kaust.authorLima, Ricardoen
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