A VARIANT OF THE PROXIMAL GRADIENT METHOD FOR CONSTRAINED CONVEX MINIMIZATION PROBLEMS

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dc.contributor.authorSuparat Kesornprom
dc.contributor.authorKunrada Kankam
dc.contributor.authorPapatsara Inkrong
dc.contributor.authorNattawut Pholasa
dc.contributor.authorPrasit Cholamjiak
dc.contributor.correspondenceP. Cholamjiak; School of Science, University of Phayao, Phayao, 56000, Thailand; email: prasit.ch@up.ac.th
dc.date.accessioned2025-03-10T07:34:20Z
dc.date.available2025-03-10T07:34:20Z
dc.date.issued2024
dc.description.abstractThis paper presents a new variant of the proximal gradient algorithm based on double inertial extrapolation to solve a constrained convex minimization problem in real Hilbert spaces. We discuss its weak convergence, including numerical image and signal recovery experiments to support the main results. Some comparisons with other algorithms are also provided. The experiments demonstrate that our method converges better than the other methods in the literature. ©2024 Journal of Nonlinear Functional Analysis.
dc.identifier.citationJournal of Nonlinear Functional Analysis
dc.identifier.doi10.23952/jnfa.2024.14
dc.identifier.issn2052532X
dc.identifier.scopus2-s2.0-85206478750
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4463
dc.languageEnglish
dc.publisherMathematical Research Press
dc.rights.holderScopus
dc.subjectConstrained convex minimization
dc.subjectImage recovery
dc.subjectProjected forward-backward method
dc.subjectSignal recovery
dc.titleA VARIANT OF THE PROXIMAL GRADIENT METHOD FOR CONSTRAINED CONVEX MINIMIZATION PROBLEMS
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85206478750&doi=10.23952%2fjnfa.2024.14&partnerID=40&md5=b7b15bb55eebe5dd8db3fc1c73147da8
oaire.citation.issue1
oaire.citation.volume2024
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