Convergence Rate of Distributed Subgradient Methods under Communication Delays

Thinh T. Doan, Carolyn L. Beck, R. Srikant

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

Abstract

Motivated by broad applications in computer science and engineering, we study distributed algorithms for optimization problems over a network of nodes, where the goal is to optimize a global objective composed of a sum of local functions. In this paper, we consider a popular distributed gradient-based consensus algorithm, which only requires local computation and communication. A significant problem in this area is to analyze the convergence rate of such algorithms in the presence of communication delays that are inevitable in distributed systems. Our main contribution is to obtain an upper bound on the rate of convergence of the algorithm as a function of the network size, topology, and the inter-node communication delays.

Original languageEnglish (US)
Title of host publication2018 Annual American Control Conference, ACC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5310-5315
Number of pages6
ISBN (Print)9781538654286
DOIs
StatePublished - Aug 9 2018
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: Jun 27 2018Jun 29 2018

Publication series

NameProceedings of the American Control Conference
Volume2018-June
ISSN (Print)0743-1619

Other

Other2018 Annual American Control Conference, ACC 2018
CountryUnited States
CityMilwauke
Period6/27/186/29/18

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Doan, T. T., Beck, C. L., & Srikant, R. (2018). Convergence Rate of Distributed Subgradient Methods under Communication Delays. In 2018 Annual American Control Conference, ACC 2018 (pp. 5310-5315). [8431382] (Proceedings of the American Control Conference; Vol. 2018-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC.2018.8431382