Convergence rate analysis for the alternating direction method of multipliers with a substitution procedure for separable convex programming

Bingsheng He*, Min Tao, Xiaoming YUAN

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

Recently, in He et al. [He BS, Tao M, Yuan XM (2012) Alternating direction method with Gaussian back substitution for separable convex programming. SIAM J. Optim. 22(2):313-340], we have showed the first possibility of combining the Douglas- Rachford alternating direction method of multipliers (ADMM) with a Gaussian back substitution procedure for solving a convex minimization model with a general separable structure. This paper is a further study on this theme. We first derive a general algorithmic framework to combine ADMM with either a forward or backward substitution procedure. Then, we show that convergence of this framework can be easily proved from the contraction perspective, and its local linear convergence rate is provable if certain error bound condition is assumed. Without such an error bound assumption, we can estimate its worst-case convergence rate measured by the iteration complexity.

Original languageEnglish
Pages (from-to)662-691
Number of pages30
JournalMathematics of Operations Research
Volume42
Issue number3
DOIs
Publication statusPublished - Aug 2017

Scopus Subject Areas

  • Mathematics(all)
  • Computer Science Applications
  • Management Science and Operations Research

User-Defined Keywords

  • Alternating direction method of multipliers
  • Contraction methods
  • Convergence rate
  • Convex programming
  • Iteration complexity

Fingerprint

Dive into the research topics of 'Convergence rate analysis for the alternating direction method of multipliers with a substitution procedure for separable convex programming'. Together they form a unique fingerprint.

Cite this