Multi-agent distributed large-scale optimization by inexact consensus alternating direction method of multipliers

Tsung Hui Chang, Mingyi Hong, Xiangfeng Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

The multi-agent distributed consensus optimization problem arises in many engineering applications. Recently, the alternating direction method of multipliers (ADMM) has been applied to distributed consensus optimization which, referred to as the consensus ADMM (C-ADMM), can converge much faster than conventional consensus subgradient methods. However, C-ADMM can be computationally expensive when the cost function to optimize has a complicated structure or when the problem dimension is large. In this paper, we propose an inexact C-ADMM (IC-ADMM) where each agent only performs one proximal gradient (PG) update at each iteration. The PGs are often easy to obtain especially for structured sparse optimization problems. Convergence conditions for IC-ADMM are analyzed. Numerical results based on a sparse logistic regression problem show that IC-ADMM, though converges slower than the original C-ADMM, has a considerably reduced computational complexity.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6137-6141
Number of pages5
ISBN (Print)9781479928927
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: May 4 2014May 9 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period5/4/145/9/14

Keywords

  • ADMM
  • Distributed consensus optimization
  • logistic regression
  • multi-agent network

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