S-Subgradient Projection Methods with S-Subdifferential Functions for Nonconvex Split Feasibility Problems
Jinzuo Chen, Mihai Postolache, Yonghong Yao
Abstract
Open-access reader
Jinzuo Chen, Mihai Postolache, Yonghong Yao
Abstract
Open-access reader
In this paper, the original C Q algorithm, the relaxed C Q algorithm, the gradient projection method ( G P M ) algorithm, and the subgradient projection method ( S P M ) algorithm for the convex split feasibility problem are reviewed, and a renewed S P M 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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In this paper, the original C Q algorithm, the relaxed C Q algorithm, the gradient projection method ( G P M ) algorithm, and the subgradient projection method ( S P M ) algorithm for the convex split feasibility problem are reviewed, and a renewed S P M algorithm with S-subdifferential functions to solve nonconvex split feasibility problems in finite dimensional spaces is suggested. The weak convergence theorem is established.
Key concepts: Subgradient method, Subderivative, Mathematics, Projection (relational algebra), Convergence (economics), Regular polygon, Mathematical optimization, Convex function