ON THE LINEAR CONVERGENCE OF THE GENERAL FIRST ORDER PRIMAL-DUAL ALGORITHM

  • Kai Wang*
  • , Deren Han
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we consider the general first order primal-dual algorithm, which covers several recent popular algorithms such as the one proposed in [Chambolle, A. and Pock T., A first-order primal-dual algorithm for convex problems with applications to imaging, J. Math. Imaging Vis., 40 (2011) 120-145] as a special case. Under suitable conditions, we prove its global convergence and analyze its linear rate of convergence. As compared to the results in the literature, we derive the corresponding results for the general case and under weaker conditions.

Original languageEnglish
Pages (from-to)3749-3770
Number of pages22
JournalJournal of Industrial and Management Optimization
Volume18
Issue number5
DOIs
StatePublished - 2022

Keywords

  • Linear rate of convergence.
  • Primal-dual algorithm
  • Proximal point algorithm
  • Saddle point problem

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