Stochastic proximal gradient consensus over time-varying networks

Mingyi Hong, Tsung Hui Chang

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

2 Scopus citations

Abstract

We consider solving a convex, nonsmooth and stochastic optimization problem over a multi-agent network. Each agent has access to a local objective function and can communicate with its immediate neighbors only. We develop a dynamic stochastic proximal-gradient consensus (DySPGC) algorithm, featuring: i) it works for both the static and randomly time-varying networks; ii) it can deal with either the exact or the stochastic gradient information; iii) it has provable rate of convergence. Interestingly, the developed algorithm includes as special cases many existing (and seemingly unrelated) first-order algorithms for distributed optimization over static networks, such as the EXTRA (Shi et al 2014), the PG-EXTRA (Shi at 2015), the IC/IDC-ADMM (Chang et al 2014), and the DLM (Ling et al 2015). It is also closely related to the classical distributed gradient method.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4776-4780
Number of pages5
ISBN (Electronic)9781479999880
DOIs
StatePublished - May 18 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: Mar 20 2016Mar 25 2016

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2016-May
ISSN (Print)1520-6149

Other

Other41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Country/TerritoryChina
CityShanghai
Period3/20/163/25/16

Bibliographical note

Funding Information:
M. Hong is supported by NSF, Grant No. CCF-1526078. T.-H. Chang is supported by NSFC, China, Grant No. 61571385.

Publisher Copyright:
© 2016 IEEE.

Keywords

  • Consensus optimization
  • alternating direction method of multipliers
  • stochastic optimization

Fingerprint

Dive into the research topics of 'Stochastic proximal gradient consensus over time-varying networks'. Together they form a unique fingerprint.

Cite this