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

Feng Ma, Mingfang Ni, Lei Zhu and Zhanke Yu

Abstract and Applied Analysis, 2014, vol. 2014, 1-9

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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlaaa:396753

DOI: 10.1155/2014/396753

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