Strong convergence of a proximal-based method for convex optimization

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. Copyright Springer-Verlag Berlin Heidelberg 2003

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Year of publication:	2003
Authors:	Azhmyakov, Vadim ; Schmidt, Werner H.
Published in:	Mathematical Methods of Operations Research. - Springer. - Vol. 57.2003, 3, p. 393-407
Publisher:	Springer
Subject:	Convex optimization \| proximal point method \| strong convergence \| nonexpansive mappings

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010950015