Forward–backward–forward algorithms involving two inertial terms for monotone inclusions

We come up with a new type of forward–backward–forward algorithms for monotone inclusion problems based on a self-adaptive technique to avoid the selection of Lipschitz assumption and also double inertial extrapolations to increase the convergence performance of our presented algorithm. We also prov...

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Veröffentlicht in:Computational & applied mathematics Jg. 42; H. 6
Hauptverfasser: Suantai, Suthep, Inkrong, Papatsara, Cholamjiak, Prasit
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.09.2023
Schlagworte:
Applications of Mathematics
Computational Mathematics and Numerical Analysis
Mathematical Applications in Computer Science
Mathematical Applications in the Physical Sciences
Mathematics
Mathematics and Statistics
Weak convergence
65K15
47H10
68W10
47H09
Forward–backward–forward algorithm
Inertial method
Inclusion problem
65Y05
ISSN:2238-3603, 1807-0302
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Zusammenfassung:We come up with a new type of forward–backward–forward algorithms for monotone inclusion problems based on a self-adaptive technique to avoid the selection of Lipschitz assumption and also double inertial extrapolations to increase the convergence performance of our presented algorithm. We also prove its weak convergence theorem under mild hypothesis. Additionally, we provide numerical test in image deblurring and signal recovery as applications. The results show that our algorithm outperforms some known algorithms in the literature.
ISSN:2238-3603
1807-0302
DOI:10.1007/s40314-023-02388-6