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Abstract and Applied Analysis
Volume 2014 (2014), Article ID 396753, 9 pages
http://dx.doi.org/10.1155/2014/396753
Research Article

An Implementable First-Order Primal-Dual Algorithm for Structured Convex Optimization

College of Communications Engineering, PLA University of Science and Technology, Nanjing 210007, China

Received 2 December 2013; Accepted 17 February 2014; Published 30 March 2014

Academic Editor: Guanglu Zhou

Copyright © 2014 Feng Ma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Many application problems of practical interest can be posed as structured convex optimization models. In this paper, we study a new first-order primaldual algorithm. The method can be easily implementable, provided that the resolvent operators of the component objective functions are simple to evaluate. We show that the proposed method can be interpreted as a proximal point algorithm with a customized metric proximal parameter. Convergence property is established under the analytic contraction framework. Finally, we verify the efficiency of the algorithm by solving the stable principal component pursuit problem.