Proximal type algorithms involving linesearch and inertial technique for split variational inclusion problem in hilbert spaces with applications

In convex optimization, numerous problems in applied sciences can be modelled as the split variational inclusion problem (SVIP). In this connection, we aim to design new and efficient proximal type algorithms which are based on the inertial technique and the linesearches terminology. We then discuss...

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Bibliographic Details
Published inOptimization Vol. 68; no. 12; pp. 2369 - 2395
Main Authors Kesornprom, Suparat, Cholamjiak, Prasit
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 02.12.2019
Taylor & Francis LLC
Subjects
Algorithms
Computational geometry
Convexity
Feasibility
Hilbert space
hilbert spaces
inertial technique
Inverse problems
linesearch
Mathematical analysis
Optimization
proximal algorithm
Split variational inclusion problem
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Summary:In convex optimization, numerous problems in applied sciences can be modelled as the split variational inclusion problem (SVIP). In this connection, we aim to design new and efficient proximal type algorithms which are based on the inertial technique and the linesearches terminology. We then discuss its convergence under some suitable conditions without the assumption on the operator norm. We also apply our main result to the split minimization problem, the split feasibility problem, the relaxed split feasibility problem and the linear inverse problem. Finally, we provide some numerical experiments and comparisons to these problems. The obtained result mainly improves the recent results investigated by Chuang.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:0233-1934
1029-4945
DOI:10.1080/02331934.2019.1638389