An Implementable First-Order Primal-Dual Algorithm for Structured Convex Optimization. (30th March 2014)

• Record Type: Journal Article
• Authors: Ma, Feng; Ni, Mingfang; Zhu, Lei; Yu, Zhanke
• Other Names: Zhou Guanglu Academic Editor.
• Is Part Of: Abstract and applied analysis. Volume 2014(2014)
• Publication Date: 2014-03-30
• Subjects: Mathematical analysis -- Periodicals; Mathematical analysis; Applied Mathematics; Mathematical Analysis; Periodicals; 515.05
• DOI: 10.1155/2014/396753
• Languages: English
• ISSNs: 1085-3375

Abstract

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.