Linearly convergent decentralized consensus optimization over directed networks

Angelia Nedich, Alex Olshevsky, Wei Shi

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

1 Scopus citations

Abstract

Recently, there have been growing interests in solving distributed consensus optimization problems over directed networks that consist of multiple agents. In this paper, we develop a first-order (gradient-based) algorithm, referred to as Push-DIGing, for this class of problems. To run Push-DIGing, each agent in the network only needs to know its own out-degree and employs a fixed step-size. Under the strong convexity assumption, we prove that the introduced algorithm converges to the global minimizer at some R-linear (geometric) rate as long as the nonnegative step-size is no greater than some explicit bound.

Original languageEnglish (US)
Title of host publication2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages485-489
Number of pages5
ISBN (Electronic)9781509045457
DOIs
StatePublished - Apr 19 2017
Externally publishedYes
Event2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States
Duration: Dec 7 2016Dec 9 2016

Other

Other2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
CountryUnited States
CityWashington
Period12/7/1612/9/16

Keywords

  • Directed network
  • Distributed optimization
  • Linear convergence
  • Small-gain theorem

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

Cite this

Nedich, A., Olshevsky, A., & Shi, W. (2017). Linearly convergent decentralized consensus optimization over directed networks. In 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings (pp. 485-489). [7905889] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2016.7905889