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Article: An alternating direction-based contraction method for linearly constrained separable convex programming problems

TitleAn alternating direction-based contraction method for linearly constrained separable convex programming problems
Authors
Keywordsconvex programming
separable structure
linear constraint
contraction method
alternating direction method
Issue Date2013
Citation
Optimization, 2013, v. 62, n. 4, p. 573-596 How to Cite?
AbstractThe classical alternating direction method (ADM) has been well studied in the context of linearly constrained convex programming and variational inequalities where the involved operator is formed as the sum of two individual functions without crossed variables. Recently, ADM has found many novel applications in diversified areas, such as image processing and statistics. However, it is still not clear whether ADM can be extended to the case where the operator is the sum of more than two individual functions. In this article, we extend the spirit of ADM to solve the general case of the linearly constrained separable convex programming problems whose involved operator is separable into finitely many individual functions. As a result, an alternating direction-based contraction-type method is developed. The realization of tackling this class of problems broadens the applicable scope of ADM substantially. © 2013 Copyright Taylor and Francis Group, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/251034
ISSN
2020 Impact Factor: 2.36
2020 SCImago Journal Rankings: 0.906
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHe, Bingsheng-
dc.contributor.authorTao, Min-
dc.contributor.authorXu, Minghua-
dc.contributor.authorYuan, Xiaoming-
dc.date.accessioned2018-02-01T01:54:23Z-
dc.date.available2018-02-01T01:54:23Z-
dc.date.issued2013-
dc.identifier.citationOptimization, 2013, v. 62, n. 4, p. 573-596-
dc.identifier.issn0233-1934-
dc.identifier.urihttp://hdl.handle.net/10722/251034-
dc.description.abstractThe classical alternating direction method (ADM) has been well studied in the context of linearly constrained convex programming and variational inequalities where the involved operator is formed as the sum of two individual functions without crossed variables. Recently, ADM has found many novel applications in diversified areas, such as image processing and statistics. However, it is still not clear whether ADM can be extended to the case where the operator is the sum of more than two individual functions. In this article, we extend the spirit of ADM to solve the general case of the linearly constrained separable convex programming problems whose involved operator is separable into finitely many individual functions. As a result, an alternating direction-based contraction-type method is developed. The realization of tackling this class of problems broadens the applicable scope of ADM substantially. © 2013 Copyright Taylor and Francis Group, LLC.-
dc.languageeng-
dc.relation.ispartofOptimization-
dc.subjectconvex programming-
dc.subjectseparable structure-
dc.subjectlinear constraint-
dc.subjectcontraction method-
dc.subjectalternating direction method-
dc.titleAn alternating direction-based contraction method for linearly constrained separable convex programming problems-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/02331934.2011.611885-
dc.identifier.scopuseid_2-s2.0-84877306694-
dc.identifier.volume62-
dc.identifier.issue4-
dc.identifier.spage573-
dc.identifier.epage596-
dc.identifier.eissn1029-4945-
dc.identifier.isiWOS:000318152100011-
dc.identifier.issnl0233-1934-

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