Variant Gradient Projection Methods for the Minimization Problems

被引：1
|
作者：
Yao, Yonghong [2 ]
Liou, Yeong-Cheng [3 ]
Wen, Ching-Feng [1 ]
机构：
[1] Kaohsiung Med Univ, Ctr Gen Educ, Kaohsiung 807, Taiwan
[2] Tianjin Polytech Univ, Dept Math, Tianjin 300387, Peoples R China
[3] Cheng Shiu Univ, Dept Informat Management, Kaohsiung 833, Taiwan
来源：
ABSTRACT AND APPLIED ANALYSIS | 2012年
关键词：
VARIATIONAL INEQUALITY PROBLEMS; SPLIT FEASIBILITY PROBLEM; NONEXPANSIVE-MAPPINGS; CONSTRAINED OPTIMIZATION; ITERATIVE ALGORITHMS; CONVEX-OPTIMIZATION; STRONG-CONVERGENCE; INVERSE PROBLEMS; EFFICIENCY;
D O I：
10.1155/2012/792078
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
The gradient projection algorithm plays an important role in solving constrained convex minimization problems. In general, the gradient projection algorithm has only weak convergence in infinite-dimensional Hilbert spaces. Recently, H.K. Xu (2011) provided two modified gradient projection algorithms which have strong convergence. Motivated by Xu's work, in the present paper, we suggest three more simpler variant gradient projection methods so that strong convergence is guaranteed.
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页数：16
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