A STRONGLY CONVERGENT METHOD FOR NONSMOOTH CONVEX MINIMIZATION IN HILBERT SPACES

被引：19
|
作者：
Bello Cruz, J. Y. [1 ]
Iusem, A. N. [2 ]
机构：
[1] Univ Fed Goias, Inst Matemat & Estat, BR-74001970 Goiania, Go, Brazil
[2] Jardim Bot, Inst Matemat Pura & Aplicada, Rio De Janeiro, Brazil
来源：
NUMERICAL FUNCTIONAL ANALYSIS AND OPTIMIZATION | 2011年 / 32卷 / 10期
关键词：
Convex minimization; Nonsmooth optimization; Projected subgradient algorithm; Projection method; Strong convergence; MESH-INDEPENDENCE PRINCIPLE; IMAGE-RECONSTRUCTION; OPTIMIZATION;
D O I：
10.1080/01630563.2011.590914
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this article, we propose a strongly convergent variant on the projected subgradient method for constrained convex minimization problems in Hilbert spaces. The advantage of the proposed method is that it converges strongly when the problem has solutions, without additional assumptions. The method also has the following desirable property: the sequence converges to the solution of the problem which lies closest to the initial iterate.
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页码：1009 / 1018
页数：10
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