A general self-adaptive relaxed-PPA method for convex programming with linear constraints
type: Item
instance of: scholarly article
title: A general self-adaptive relaxed-PPA method for convex programming with linear constraints
zbMATH Open document ID: 1291.90170
DOI: 10.1155/2013/492305
author: Xiaoling Fu
published in: Abstract and Applied Analysis
publication date: 2014-06-23T00:00:00Z
review text: Summary: We present an efficient method for solving linearly constrained convex programming. Our algorithmic framework employs an implementable proximal step by a slight relaxation to the subproblem of proximal point algorithm (PPA). In particular, the stepsize choice condition of our algorithm is weaker than some elegant PPA-type methods. This condition is flexible and effective. Self-adaptive strategies are proposed to improve the convergence in practice. We theoretically show under mild conditions that our method converges in a global sense. Finally, we discuss applications and perform numerical experiments which confirm the efficiency of the proposed method. Comparisons of our method with some state-of-the-art algorithms are also provided.
Mathematics Subject Classification ID: 90C25
zbMATH DE Number: 6306878
Wikidata QID: Q58916882
describes a project that uses: TFOCS
describes a project that uses: PDCO
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/492305
OpenAlex ID: W2091929055
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description: scientific article; zbMATH DE number 6306878