General iterative methods for equilibrium and constrained convex minimization problem

被引：12

作者：

Tian, Ming ^{[1
,2
]}

Liu, Lei ^{[1
]}

机构：

[1] Civil Aviat Univ China, Coll Sci, Tianjin 300300, Peoples R China

[2] Tianjin Key Lab Adv Signal Proc, Civil Aviat Univ China, Tianjin 300300, Peoples R China

来源：

OPTIMIZATION | 2014年 / 63卷 / 09期

关键词：

iterative algorithm; equilibrium problem; constrained convex minimization; variational inequality; FIXED-POINT PROBLEMS; VISCOSITY APPROXIMATION METHODS; NONEXPANSIVE-MAPPINGS; STRONG-CONVERGENCE; ALGORITHMS;

D O I：

10.1080/02331934.2012.713361

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

The gradient-projection algorithm (GPA) plays an important role in solving constrained convex minimization problems. Based on Marino and Xu's method [G. Marino and H.-K. Xu, A general method for nonexpansive mappings in Hilbert space, J. Math. Anal. Appl. 318 (2006), pp. 43-52], we combine GPA and averaged mapping approach to propose implicit and explicit composite iterative algorithms for finding a common solution of an equilibrium and a constrained convex minimization problem for the first time in this article. Under suitable conditions, strong convergence theorems are obtained.

引用

页码：1367 / 1385

页数：19

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