Quantisation Effects in Distributed Optimisation

Joseph A.G. Jonkman, Thomas Sherson, Richard Heusdens

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)

Abstract

In this paper the effects of quantisation on distributed convex optimisation algorithms are explored via the lens of monotone operator theory. Specifically, by representing transmission quantisation via an additive noise model, we demonstrate how quantisation can be viewed as an instance of an inexact Krasnosel' skiľ-Mann scheme. In the case of two distributed solvers, the Alternating Direction Method of Multipliers and the Primal Dual Method of Multipliers, we further demonstrate how an adaptive quantisation scheme can be constructed to reduce transmission costs between nodes. Finally for the Gaussian channel capacity maximisation problem, we demonstrate convergence even in the presence of one-bit uniform quantisation based on the aforementioned adaptive quantisation scheme.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages3649-3653
Number of pages5
ISBN (Electronic)978-1-5386-4658-8
ISBN (Print)978-1-5386-4659-5
DOIs
Publication statusPublished - 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018: Signal Processing and Artificial Intelligence: Changing the World - Calgary Telus Convention Center (CTCC), Calgary, Canada
Duration: 15 Apr 201820 Apr 2018
https://2018.ieeeicassp.org

Conference

Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period15/04/1820/04/18
Internet address

Keywords

  • ADMM
  • Distributed Convex Optimisation
  • Monotone Operator Theory
  • PDMM
  • Quantisation

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