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New proximal type algorithms for convex minimization and its application to image deblurring

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dc.contributor.authorKesornprom, Suparat-
dc.contributor.authorCholamjiak, Prasit-
dc.contributor.authorPark, Choonkil-
dc.date.accessioned2022-12-20T06:18:43Z-
dc.date.available2022-12-20T06:18:43Z-
dc.date.issued2022-10-
dc.identifier.issn0101-8205-
dc.identifier.issn2238-3603-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/173015-
dc.description.abstractIn this work, we are interested in solving a convex minimization problem in real Hilbert spaces. We propose a new modified proximal algorithm using the inertial extrapolation and the linesearch technique. Its weak convergence theorems are established under mild conditions. Numerical experiments are presented to illustrate the performance of the proposed algorithm in image deblurring.-
dc.language영어-
dc.language.isoENG-
dc.publisherSPRINGER HEIDELBERG-
dc.titleNew proximal type algorithms for convex minimization and its application to image deblurring-
dc.typeArticle-
dc.publisher.location독일-
dc.identifier.doi10.1007/s40314-022-02042-7-
dc.identifier.scopusid2-s2.0-85139187766-
dc.identifier.wosid000862417700002-
dc.identifier.bibliographicCitationCOMPUTATIONAL & APPLIED MATHEMATICS, v.41, no.7-
dc.citation.titleCOMPUTATIONAL & APPLIED MATHEMATICS-
dc.citation.volume41-
dc.citation.number7-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaMathematics-
dc.relation.journalWebOfScienceCategoryMathematics, Applied-
dc.subject.keywordPlusSPLIT FEASIBILITY-
dc.subject.keywordPlusCONVERGENCE-
dc.subject.keywordPlusSHRINKAGE-
dc.subject.keywordAuthorConvex minimization problem-
dc.subject.keywordAuthorForward-backward method-
dc.subject.keywordAuthorLinesearch rule-
dc.subject.keywordAuthorInertial method-
dc.subject.keywordAuthorWeak convergence-
dc.identifier.urlhttps://link.springer.com/article/10.1007/s40314-022-02042-7-
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