An augmented lagrangian based parallel splitting method for separable convex minimization with applications to image processing

Deren Han, Xiaoming YUAN*, Wenxing Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)

Abstract

This paper considers the convex minimization problem with linear constraints and a separable objective function which is the sum of many individual functions without coupled variables. An algorithm is developed by splitting the augmented Lagrangian function in a parallel way. The new algorithm differs substantially from existing splitting methods in alternating style which require solving the decomposed subproblems sequentially, while it remains the main superiority of existing splitting methods in that the resulting subproblems could be simple enough to have closed-form solutions for such an application whose functions in the objective are simple. We show applicability and encouraging efficiency of the new algorithm by some applications in image processing.

Original languageEnglish
Pages (from-to)2263-2291
Number of pages29
JournalMathematics of Computation
Volume83
Issue number289
DOIs
Publication statusPublished - 2014

Scopus Subject Areas

  • Algebra and Number Theory
  • Computational Mathematics
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'An augmented lagrangian based parallel splitting method for separable convex minimization with applications to image processing'. Together they form a unique fingerprint.

Cite this