Viscosity modification with parallel inertial two steps forward-backward splitting methods for inclusion problems applied to signal recovery

•We investigate the common variational inclusion problem (CVIP) and offer a new parallel technique that combines viscosity modification with parallel inertial two-step forward-backward splitting approaches.•We prove the strongly convergent theorems in Hilbert spaces under some reasonable conditions....

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Veröffentlicht in:Chaos, solitons and fractals Jg. 157; S. 111858
Hauptverfasser: Cholamjiak, Watcharaporn, Dutta, Hemen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.04.2022
Schlagworte:
Forward-backward algorithm
Inclusion problem
Inertial method
Parallel algorithm
Signal recovery
Viscosity method
Parallel algorithm
Viscosity method
68W10
Inertial method
Signal recovery
49M37
Inclusion problem
47H05
47J25
65Y05
Forward-backward algorithm
ISSN:0960-0779, 1873-2887
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Beschreibung
Zusammenfassung:•We investigate the common variational inclusion problem (CVIP) and offer a new parallel technique that combines viscosity modification with parallel inertial two-step forward-backward splitting approaches.•We prove the strongly convergent theorems in Hilbert spaces under some reasonable conditions.•For signal recovery problems requiring several filters, an extensive numerical examination is presented, with comparisons to the inertial parallel monotone hybrid method.•All of the numerical results show that the proposed algorithm solves the signal recovery problem and improves the quality of the observed signal significantly. In this paper, we introduce a new parallel algorithm by combining viscosity modification with parallel inertial two steps forward-backward splitting methods for approximating a solution of common inclusion problems. The strongly convergent theorems are established under some suitable conditions in Hilbert spaces. The applicability and advantages of the new parallel algorithm are presented by using to solve signal recovering problem in compressed sensing. The efficiency of the algorithm is shown by comparing it with some previous parallel algorithms.
ISSN:0960-0779
1873-2887
DOI:10.1016/j.chaos.2022.111858