A splitting method for separable convex programming

Bingsheng He, Min Tao, Xiaoming YUAN*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

59 Citations (Scopus)


We propose a splitting method for solving a separable convex minimization problem with linear constraints, where the objective function is expressed as the sum of m individual functions without coupled variables. Treating the functions in the objective separately, the new method belongs to the category of operator splitting methods. We show the global convergence and estimate a worst-case convergence rate for the new method, and then illustrate its numerical efficiency by some applications.

Original languageEnglish
Pages (from-to)394-426
Number of pages33
JournalIMA Journal of Numerical Analysis
Issue number1
Publication statusPublished - 21 Jun 2013

Scopus Subject Areas

  • Mathematics(all)
  • Computational Mathematics
  • Applied Mathematics

User-Defined Keywords

  • convex programming
  • image processing
  • operator splitting methods
  • separable structure


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