On semidefinite bounds for maximization of a non-convex quadratic objective over the ℓ1 unit ball

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dc.citation.epage265en_US
dc.citation.issueNumber3en_US
dc.citation.spage253en_US
dc.citation.volumeNumber40en_US
dc.contributor.authorPınar, M. Ç.en_US
dc.contributor.authorTeboulle, M.en_US
dc.date.accessioned2016-02-08T10:18:57Z
dc.date.available2016-02-08T10:18:57Z
dc.date.issued2006en_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.description.abstractWe consider the non-convex quadratic maximization problem subject to the ℓ1 unit ball constraint. The nature of the l1 norm structure makes this problem extremely hard to analyze, and as a consequence, the same difficulties are encountered when trying to build suitable approximations for this problem by some tractable convex counterpart formulations. We explore some properties of this problem, derive SDP-like relaxations and raise open questions.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T10:18:57Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2006en
dc.identifier.doi10.1051/ro:2006023en_US
dc.identifier.eissn1290-3868
dc.identifier.issn0399-0559
dc.identifier.urihttp://hdl.handle.net/11693/23775
dc.language.isoEnglishen_US
dc.publisherE D P Sciencesen_US
dc.relation.isversionofhttp://dx.doi.org/10.1051/ro:2006023en_US
dc.source.titleRAIRO - Operations Researchen_US
dc.subjectNon-convex quadratic optimizationen_US
dc.subjectL1-norm constrainten_US
dc.subjectSemidefinite programming relaxationen_US
dc.subjectDualityen_US
dc.titleOn semidefinite bounds for maximization of a non-convex quadratic objective over the ℓ1 unit ballen_US
dc.typeArticleen_US

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