Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/60051
Type: Artigo de periódico
Title: INCREMENTAL SUBGRADIENTS FOR CONSTRAINED CONVEX OPTIMIZATION: A UNIFIED FRAMEWORK AND NEW METHODS
Author: Neto, ESH
De Pierro, AR
Abstract: We present a unifying framework for nonsmooth convex minimization bringing together is an element of-subgradient algorithms and methods for the convex feasibility problem. This development is a natural step for is an element of-subgradient methods in the direction of constrained optimization since the Euclidean projection frequently required in such methods is replaced by an approximate projection, which is often easier to compute. The developments are applied to incremental subgradient methods, resulting in new algorithms suitable to large-scale optimization problems, such as those arising in tomographic imaging.
Subject: convex optimization
projection methods
subgradient methods
incremental subgradient
Country: EUA
Editor: Siam Publications
Rights: aberto
Identifier DOI: 10.1137/070711712
Date Issue: 2009
Appears in Collections:Unicamp - Artigos e Outros Documentos

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