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Article: Solving large-scale least squares semidefinite programming by alternating direction methods

TitleSolving large-scale least squares semidefinite programming by alternating direction methods
Authors
KeywordsAlternating direction method
Least squares semidefinite matrix
Variational inequality
Large-scale
Issue Date2011
PublisherSociety for Industrial and Applied Mathematics. The Journal's web site is located at http://www.siam.org/journals/simax.php
Citation
SIAM Journal on Matrix Analysis and Applications, 2011, v. 32, n. 1, p. 136-152 How to Cite?
Abstract© 2011 Society for Industrial and Applied Mathematics. The well-known least squares semidefinite programming (LSSDP) problem seeks the nearest adjustment of a given symmetric matrix in the intersection of the cone of positive semidefinite matrices and a set of linear constraints, and it captures many applications in diversing fields. The task of solving large-scale LSSDP with many linear constraints, however, is numerically challenging. This paper mainly shows the applicability of the classical alternating direction method (ADM) for solving LSSDP and convinces the efficiency of the ADM approach. We compare the ADM approach with some other existing approaches numerically, and we show the superiority of ADM for solving large-scale LSSDP.
Persistent Identifierhttp://hdl.handle.net/10722/251141
ISSN
2020 Impact Factor: 1.944
2020 SCImago Journal Rankings: 1.268
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHe, Bingsheng-
dc.contributor.authorXu, Minghua-
dc.contributor.authorYuan, Xiaoming-
dc.date.accessioned2018-02-01T01:54:43Z-
dc.date.available2018-02-01T01:54:43Z-
dc.date.issued2011-
dc.identifier.citationSIAM Journal on Matrix Analysis and Applications, 2011, v. 32, n. 1, p. 136-152-
dc.identifier.issn0895-4798-
dc.identifier.urihttp://hdl.handle.net/10722/251141-
dc.description.abstract© 2011 Society for Industrial and Applied Mathematics. The well-known least squares semidefinite programming (LSSDP) problem seeks the nearest adjustment of a given symmetric matrix in the intersection of the cone of positive semidefinite matrices and a set of linear constraints, and it captures many applications in diversing fields. The task of solving large-scale LSSDP with many linear constraints, however, is numerically challenging. This paper mainly shows the applicability of the classical alternating direction method (ADM) for solving LSSDP and convinces the efficiency of the ADM approach. We compare the ADM approach with some other existing approaches numerically, and we show the superiority of ADM for solving large-scale LSSDP.-
dc.languageeng-
dc.publisherSociety for Industrial and Applied Mathematics. The Journal's web site is located at http://www.siam.org/journals/simax.php-
dc.relation.ispartofSIAM Journal on Matrix Analysis and Applications-
dc.subjectAlternating direction method-
dc.subjectLeast squares semidefinite matrix-
dc.subjectVariational inequality-
dc.subjectLarge-scale-
dc.titleSolving large-scale least squares semidefinite programming by alternating direction methods-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1137/090768813-
dc.identifier.scopuseid_2-s2.0-79952421740-
dc.identifier.volume32-
dc.identifier.issue1-
dc.identifier.spage136-
dc.identifier.epage152-
dc.identifier.eissn1095-7162-
dc.identifier.isiWOS:000292816300007-
dc.identifier.issnl0895-4798-

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