Strong convergence of a proximal-based method for convex optimization

被引：0
|
作者：
Vadim Azhmyakov
Werner H. Schmidt
机构：
[1] Institute of Mathematics and Computer Sciences,
[2] EMA University of Greifswald,undefined
[3] Jahnstr. 15a,undefined
[4] D-17487 Greifswald,undefined
[5] Germany (e-mail: azmjakov@uni-greifswald.de; e-mail: wschmidt@uni-greifswald.de),undefined
来源：
Mathematical Methods of Operations Research | 2003年 / 57卷
关键词：
Key words: Convex optimization, proximal point method, strong convergence, nonexpansive mappings;
D O I：
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学科分类号：
摘要：
In this work we study a proximal-like method for the problem of convex minimization in Hilbert spaces. Using the classical proximal mapping, we construct a new stable iterative procedure. The strong convergence of obtained sequences to the normal solution of the optimization problem is proved. Some results of this paper are extended for uniformly convex Banach spaces.
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页码：393 / 407
页数：14
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