Distributed dual subgradient method with double averaging: Application to QoS optimization in wireless networks

Changxin Liu, Huiping Li, Yang Shi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This work addresses multi-agent optimization problems with decoupled local objective functions and coupled inequality constraints. A distributed dual subgradient method with double averaging that is built on the dual decomposition, the subgradient method with double averaging, and the dynamic average consensus is developed to solve the global problem with only local computation and peer-to-peer communication. It is theoretically proved that, for the primal-dual sequence, both the dual objective error and the quadratic penalty for the coupled constraints have Oleft( {frac{1}{{sqrt t }}} right) upper bounds, and the primal objective error asymptotically vanishes. The proposed algorithm is applied to optimize the Quality of Service (QoS) in wireless networks; numerical results verify the effectiveness of the proposed algorithm.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 28th International Symposium on Industrial Electronics, ISIE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1239-1244
Number of pages6
ISBN (Electronic)9781728136660
DOIs
StatePublished - Jun 2019
Event28th IEEE International Symposium on Industrial Electronics, ISIE 2019 - Vancouver, Canada
Duration: 12 Jun 201914 Jun 2019

Publication series

NameIEEE International Symposium on Industrial Electronics
Volume2019-June

Conference

Conference28th IEEE International Symposium on Industrial Electronics, ISIE 2019
Country/TerritoryCanada
CityVancouver
Period12/06/1914/06/19

Keywords

  • coupled inequality constraints
  • Distributed optimization
  • dual subgradient method
  • QoS optimization

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