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Strong Convergence of Modified Algorithms Based on the Regularization for the Constrained Convex Minimization Problem
Joint Authors
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-10-27
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
As is known, the regularization method plays an important role in solving constrained convex minimization problems.
Based on the idea of regularization, implicit and explicit iterative algorithms are proposed in this paper and the sequences generated by the algorithms can converge strongly to a solution of the constrained convex minimization problem, which also solves a certain variational inequality.
As an application, we also apply the algorithm to solve the split feasibility problem.
American Psychological Association (APA)
Tian, Ming& Gong, Jun-Ying. 2014. Strong Convergence of Modified Algorithms Based on the Regularization for the Constrained Convex Minimization Problem. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1034061
Modern Language Association (MLA)
Tian, Ming& Gong, Jun-Ying. Strong Convergence of Modified Algorithms Based on the Regularization for the Constrained Convex Minimization Problem. Abstract and Applied Analysis No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1034061
American Medical Association (AMA)
Tian, Ming& Gong, Jun-Ying. Strong Convergence of Modified Algorithms Based on the Regularization for the Constrained Convex Minimization Problem. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1034061
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1034061