Finite Rate Quantized Distributed optimization with Geometric Convergence

Chang Shen Lee, Nicolo Michelusi, Gesualdo Scutari

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

4 Scopus citations

Abstract

This paper studies distributed (strongly convex) optimization over multi-agent networks, subject to finite rate communications. We propose the first distributed algorithm achieving geometric convergence to the exact solution of the problem, matching thus the rate of the centralized gradient algorithm (although with different constants). The algorithm combines gradient tracking with a quantized perturbed consensus scheme. The impact on the convergence (rate) of design and network parameters, such as number of bits, algorithm step-size, and network connectivity, is also investigated. Finally, numerical results validate our theoretical findings. They demonstrate the existence of an interesting trade-off among solution accuracy, convergence time and communication cost, defined as the total number of bits transmitted on one link to achieve a target solution error.

Original languageEnglish (US)
Title of host publicationConference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1876-1880
Number of pages5
ISBN (Electronic)9781538692189
DOIs
StatePublished - Feb 19 2019
Externally publishedYes
Event52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States
Duration: Oct 28 2018Oct 31 2018

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2018-October
ISSN (Print)1058-6393

Conference

Conference52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
CountryUnited States
CityPacific Grove
Period10/28/1810/31/18

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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