A General Self-Adaptive Relaxed-PPA Method for Convex Programming with Linear Constraints

A General Self-Adaptive Relaxed-PPA Method for Convex Programming with Linear Constraints

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 tha...

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Main Author: Xiaoling Fu
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:Abstract and Applied Analysis
Online Access:http://dx.doi.org/10.1155/2013/492305
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spelling doaj-e8fdadab06cd40249353bf3a46b9b6862020-11-24T21:20:17ZengHindawi LimitedAbstract and Applied Analysis1085-33751687-04092013-01-01201310.1155/2013/492305492305A General Self-Adaptive Relaxed-PPA Method for Convex Programming with Linear ConstraintsXiaoling Fu0Institute of Systems Engineering, Southeast University, Nanjing 210096, ChinaWe 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.http://dx.doi.org/10.1155/2013/492305
collection DOAJ
language English
format Article
sources DOAJ
author Xiaoling Fu
spellingShingle Xiaoling Fu
A General Self-Adaptive Relaxed-PPA Method for Convex Programming with Linear Constraints
Abstract and Applied Analysis
author_facet Xiaoling Fu
author_sort Xiaoling Fu
title A General Self-Adaptive Relaxed-PPA Method for Convex Programming with Linear Constraints
title_short A General Self-Adaptive Relaxed-PPA Method for Convex Programming with Linear Constraints
title_full A General Self-Adaptive Relaxed-PPA Method for Convex Programming with Linear Constraints
title_fullStr A General Self-Adaptive Relaxed-PPA Method for Convex Programming with Linear Constraints
title_full_unstemmed A General Self-Adaptive Relaxed-PPA Method for Convex Programming with Linear Constraints
title_sort general self-adaptive relaxed-ppa method for convex programming with linear constraints
publisher Hindawi Limited
series Abstract and Applied Analysis
issn 1085-3375
1687-0409
publishDate 2013-01-01
description 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.
url http://dx.doi.org/10.1155/2013/492305
work_keys_str_mv AT xiaolingfu ageneralselfadaptiverelaxedppamethodforconvexprogrammingwithlinearconstraints
AT xiaolingfu generalselfadaptiverelaxedppamethodforconvexprogrammingwithlinearconstraints
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