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

Author

Listed:
  • Feng Ma
  • Mingfang Ni
  • Lei Zhu
  • Zhanke Yu

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.

Suggested Citation

  • Feng Ma & Mingfang Ni & Lei Zhu & Zhanke Yu, 2014. "An Implementable First-Order Primal-Dual Algorithm for Structured Convex Optimization," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-9, March.
    • Handle: RePEc:hin:jnlaaa:396753
    DOI: 10.1155/2014/396753
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