An alternating direction-based contraction method for linearly constrained separable convex programming problems

Bingsheng He, Min Tao, Minghua Xu, Xiaoming YUAN*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

Abstract

The classical alternating direction method (ADM) has been well studied in the context of linearly constrained convex programming and variational inequalities where the involved operator is formed as the sum of two individual functions without crossed variables. Recently, ADM has found many novel applications in diversified areas, such as image processing and statistics. However, it is still not clear whether ADM can be extended to the case where the operator is the sum of more than two individual functions. In this article, we extend the spirit of ADM to solve the general case of the linearly constrained separable convex programming problems whose involved operator is separable into finitely many individual functions. As a result, an alternating direction-based contraction-type method is developed. The realization of tackling this class of problems broadens the applicable scope of ADM substantially.

Original languageEnglish
Pages (from-to)573-596
Number of pages24
JournalOptimization
Volume62
Issue number4
DOIs
Publication statusPublished - Apr 2013

Scopus Subject Areas

  • Control and Optimization
  • Management Science and Operations Research
  • Applied Mathematics

User-Defined Keywords

  • alternating direction method
  • contraction method
  • convex programming
  • linear constraint
  • separable structure

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