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Article: A customized proximal point algorithm for convex minimization with linear constraints

TitleA customized proximal point algorithm for convex minimization with linear constraints
Authors
KeywordsResolvent operator
Augmented Lagrangian method
Convex minimization
Proximal point algorithm
Issue Date2013
Citation
Computational Optimization and Applications, 2013, v. 56, n. 3, p. 559-572 How to Cite?
AbstractThis paper demonstrates a customized application of the classical proximal point algorithm (PPA) to the convex minimization problem with linear constraints. We show that if the proximal parameter in metric form is chosen appropriately, the application of PPA could be effective to exploit the simplicity of the objective function. The resulting subproblems could be easier than those of the augmented Lagrangian method (ALM), a benchmark method for the model under our consideration. The efficiency of the customized application of PPA is demonstrated by some image processing problems. © 2013 Springer Science+Business Media New York.
Persistent Identifierhttp://hdl.handle.net/10722/251261
ISSN
2020 Impact Factor: 2.167
2020 SCImago Journal Rankings: 1.028
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHe, Bingsheng-
dc.contributor.authorYuan, Xiaoming-
dc.contributor.authorZhang, Wenxing-
dc.date.accessioned2018-02-01T01:55:03Z-
dc.date.available2018-02-01T01:55:03Z-
dc.date.issued2013-
dc.identifier.citationComputational Optimization and Applications, 2013, v. 56, n. 3, p. 559-572-
dc.identifier.issn0926-6003-
dc.identifier.urihttp://hdl.handle.net/10722/251261-
dc.description.abstractThis paper demonstrates a customized application of the classical proximal point algorithm (PPA) to the convex minimization problem with linear constraints. We show that if the proximal parameter in metric form is chosen appropriately, the application of PPA could be effective to exploit the simplicity of the objective function. The resulting subproblems could be easier than those of the augmented Lagrangian method (ALM), a benchmark method for the model under our consideration. The efficiency of the customized application of PPA is demonstrated by some image processing problems. © 2013 Springer Science+Business Media New York.-
dc.languageeng-
dc.relation.ispartofComputational Optimization and Applications-
dc.subjectResolvent operator-
dc.subjectAugmented Lagrangian method-
dc.subjectConvex minimization-
dc.subjectProximal point algorithm-
dc.titleA customized proximal point algorithm for convex minimization with linear constraints-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s10589-013-9564-5-
dc.identifier.scopuseid_2-s2.0-84890193998-
dc.identifier.volume56-
dc.identifier.issue3-
dc.identifier.spage559-
dc.identifier.epage572-
dc.identifier.eissn1573-2894-
dc.identifier.isiWOS:000327240600004-
dc.identifier.issnl0926-6003-

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