S-Subgradient Projection Methods with S-Subdifferential Functions for Nonconvex Split Feasibility Problems
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School of Mathematics and Statistics, Lingnan Normal University, Zhanjiang 524048, China
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Center for General Education, China Medical University, Taichung 40402, Taiwan
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Department of Mathematics and Informatics, University “Politehnica” of Bucharest, 060042 Bucharest, Romania
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Romanian Academy, Gh. Mihoc-C. Iacob Institute of Mathematical Statistics and Applied Mathematics, 050711 Bucharest, Romania
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School of Mathematical Sciences, Tianjin Polytechnic University, Tianjin 300387, China
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The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province, North Minzu University, Yinchuan 750021, China
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(12), 1517; https://doi.org/10.3390/sym11121517
Received: 24 November 2019 / Revised: 11 December 2019 / Accepted: 11 December 2019 / Published: 14 December 2019
(This article belongs to the Special Issue Advance in Nonlinear Analysis and Optimization)
In this paper, the original algorithm, the relaxed algorithm, the gradient projection method ( ) algorithm, and the subgradient projection method ( ) algorithm for the convex split feasibility problem are reviewed, and a renewed algorithm with S-subdifferential functions to solve nonconvex split feasibility problems in finite dimensional spaces is suggested. The weak convergence theorem is established.
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Keywords:
S-subgradient projection method; nonconvex; S-subdifferentiable; split feasibility problem
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MDPI and ACS Style
Chen, J.; Postolache, M.; Yao, Y. S-Subgradient Projection Methods with S-Subdifferential Functions for Nonconvex Split Feasibility Problems. Symmetry 2019, 11, 1517.
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