New inertial forward–backward algorithm for convex minimization with applications
Abstract: In this work, we present a new proximal gradient algorithm based on Tseng’s extragradient method and an inertial technique to solve the convex minimization problem in real Hilbert spaces. Using the stepsize rules, the selection of the Lipschitz constant of the gradient of functions is avoided. We then prove the weak convergence theorem and present the numerical experiments for image recovery. The comparative results show that the proposed algorithm has better efficiency than other methods.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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New inertial forward–backward algorithm for convex minimization with applications ; volume:56 ; number:1 ; year:2023 ; extent:13
Demonstratio mathematica ; 56, Heft 1 (2023) (gesamt 13)
- Urheber
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Kankam, Kunrada
Cholamjiak, Watcharaporn
Cholamjiak, Prasit
- DOI
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10.1515/dema-2022-0188
- URN
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urn:nbn:de:101:1-2023021613042549225395
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:47 MESZ
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Beteiligte
- Kankam, Kunrada
- Cholamjiak, Watcharaporn
- Cholamjiak, Prasit
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