Multi-agent distributed optimization via inexact consensus ADMM

Tsung Hui Chang, Mingyi Hong, Xiangfeng Wang

Research output: Contribution to journalArticle

140 Scopus citations

Abstract

Multi-agent distributed consensus optimization problems arise in many signal processing applications. Recently, the alternating direction method of multipliers (ADMM) has been used for solving this family of problems. ADMM based distributed optimization method is shown to have faster convergence rate compared with classic methods based on consensus subgradient, but can be computationally expensive, especially for problems with complicated structures or large dimensions. In this paper, we propose low-complexity algorithms that can reduce the overall computational cost of consensus ADMM by an order of magnitude for certain large-scale problems. Central to the proposed algorithms is the use of an inexact step for each ADMM update, which enables the agents to perform cheap computation at each iteration. Our convergence analyses show that the proposed methods converge well under some convexity assumptions. Numerical results show that the proposed algorithms offer considerably lower computational complexity than the standard ADMM based distributed optimization methods.

Original languageEnglish (US)
Article number6945888
Pages (from-to)482-497
Number of pages16
JournalIEEE Transactions on Signal Processing
Volume63
Issue number2
DOIs
StatePublished - Jan 15 2015

Keywords

  • ADMM
  • consensus
  • distributed optimization

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