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2014 An Implementable First-Order Primal-Dual Algorithm for Structured Convex Optimization
Feng Ma, Mingfang Ni, Lei Zhu, Zhanke Yu
Abstr. Appl. Anal. 2014(SI19): 1-9 (2014). DOI: 10.1155/2014/396753


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.


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Feng Ma. Mingfang Ni. Lei Zhu. Zhanke Yu. "An Implementable First-Order Primal-Dual Algorithm for Structured Convex Optimization." Abstr. Appl. Anal. 2014 (SI19) 1 - 9, 2014.


Published: 2014
First available in Project Euclid: 2 October 2014

zbMATH: 07022310
MathSciNet: MR3193509
Digital Object Identifier: 10.1155/2014/396753

Rights: Copyright © 2014 Hindawi

Vol.2014 • No. SI19 • 2014
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