A relaxed version of Bregman's method for convex programming
type: Item
instance of: scholarly article
title: A relaxed version of Bregman's method for convex programming
zbMATH Open document ID: 0581.90069
DOI: 10.1007/BF00940283
published in: Journal of Optimization Theory and Applications
publication date: 1986-01-01T00:00:00Z
review text: A new type of relaxation for Bregman's method, an iterative primal-dual algorithm for linearly constrained convex programming, is presented. It is shown that the new relaxation procedure generalizes the usual concept of relaxation and preserves the convergence properties of Bregman's algorithm for a suitable choice of the relaxation parameters. For convergence, Bregman's method requires that the objective function satisfy certain conditions. A sufficient and easily checkable condition for these requirements to hold is also given.
Mathematics Subject Classification ID: 90C25
Mathematics Subject Classification ID: 90C55
Mathematics Subject Classification ID: 65K05
zbMATH DE Number: 3930732
zbMATH Keywords: relaxation
zbMATH Keywords: Bregman's method
zbMATH Keywords: iterative primal-dual algorithm
zbMATH Keywords: linearly constrained convex programming
zbMATH Keywords: entropy optimization
zbMATH Keywords: large and sparse matrices
zbMATH Keywords: nonorthogonal projections
author: Álvaro Rodolfo De Pierro
author: Alfredo Iusem
MaRDI profile type: Publication
cites work: Q4171875
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full work available at URL: https://doi.org/10.1007/bf00940283
OpenAlex ID: W2016611950
description: scientific article; zbMATH DE number 3930732