A customized proximal point algorithm for convex minimization with linear constraints

Bingsheng He, Xiaoming YUAN*, Wenxing Zhang

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

47 Citations (Scopus)


This paper demonstrates a customized application of the classical proximal point algorithm (PPA) to the convex minimization problem with linear constraints. We show that if the proximal parameter in metric form is chosen appropriately, the application of PPA could be effective to exploit the simplicity of the objective function. The resulting subproblems could be easier than those of the augmented Lagrangian method (ALM), a benchmark method for the model under our consideration. The efficiency of the customized application of PPA is demonstrated by some image processing problems.

Original languageEnglish
Pages (from-to)559-572
Number of pages14
JournalComputational Optimization and Applications
Issue number3
Publication statusPublished - Dec 2013

Scopus Subject Areas

  • Control and Optimization
  • Computational Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Augmented Lagrangian method
  • Convex minimization
  • Proximal point algorithm
  • Resolvent operator


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