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Article: An augmented lagrangian based parallel splitting method for separable convex minimization with applications to image processing

TitleAn augmented lagrangian based parallel splitting method for separable convex minimization with applications to image processing
Authors
Issue Date2014
Citation
Mathematics of Computation, 2014, v. 83, n. 289, p. 2263-2291 How to Cite?
Abstract© 2014 American Mathematical Society. This paper considers the convex minimization problem with linear constraints and a separable objective function which is the sum of many individual functions without coupled variables. An algorithm is developed by splitting the augmented Lagrangian function in a parallel way. The new algorithm differs substantially from existing splitting methods in alternating style which require solving the decomposed subproblems sequentially, while it remains the main superiority of existing splitting methods in that the resulting subproblems could be simple enough to have closed-form solutions for such an application whose functions in the objective are simple. We show applicability and encouraging efficiency of the new algorithm by some applications in image processing.
Persistent Identifierhttp://hdl.handle.net/10722/250877
ISSN
2023 Impact Factor: 2.2
2023 SCImago Journal Rankings: 1.460
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHan, Deren-
dc.contributor.authorYuan, Xiaoming-
dc.contributor.authorZhang, Wenxing-
dc.date.accessioned2018-02-01T01:53:58Z-
dc.date.available2018-02-01T01:53:58Z-
dc.date.issued2014-
dc.identifier.citationMathematics of Computation, 2014, v. 83, n. 289, p. 2263-2291-
dc.identifier.issn0025-5718-
dc.identifier.urihttp://hdl.handle.net/10722/250877-
dc.description.abstract© 2014 American Mathematical Society. This paper considers the convex minimization problem with linear constraints and a separable objective function which is the sum of many individual functions without coupled variables. An algorithm is developed by splitting the augmented Lagrangian function in a parallel way. The new algorithm differs substantially from existing splitting methods in alternating style which require solving the decomposed subproblems sequentially, while it remains the main superiority of existing splitting methods in that the resulting subproblems could be simple enough to have closed-form solutions for such an application whose functions in the objective are simple. We show applicability and encouraging efficiency of the new algorithm by some applications in image processing.-
dc.languageeng-
dc.relation.ispartofMathematics of Computation-
dc.titleAn augmented lagrangian based parallel splitting method for separable convex minimization with applications to image processing-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1090/S0025-5718-2014-02829-9-
dc.identifier.scopuseid_2-s2.0-84910018768-
dc.identifier.volume83-
dc.identifier.issue289-
dc.identifier.spage2263-
dc.identifier.epage2291-
dc.identifier.isiWOS:000351438000009-
dc.identifier.issnl0025-5718-

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