Accelerated Multi-Agent Optimization Method over Stochastic Networks

Wicak Ananduta, Carlos Ocampo-Martinez, Angelia Nedic

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

Abstract

We propose a distributed method to solve a multi-agent optimization problem with strongly convex cost function and equality coupling constraints. The method is based on Nesterov's accelerated gradient approach and works over stochastically time-varying communication networks. We consider the standard assumptions of Nesterov's method and show that the sequence of the expected dual values converge toward the optimal value with the rate of \mathcal{O}(1/{k^2}). Furthermore, we provide a simulation study of solving an optimal power flow problem with a well-known benchmark case.

Original languageEnglish
Title of host publicationProceedings of the 59th IEEE Conference on Decision and Control, CDC 2020
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages2961-2966
ISBN (Electronic)978-1-7281-7447-1
DOIs
Publication statusPublished - 2020
Event59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of
Duration: 14 Dec 202018 Dec 2020

Conference

Conference59th IEEE Conference on Decision and Control, CDC 2020
CountryKorea, Republic of
CityVirtual, Jeju Island
Period14/12/2018/12/20

Keywords

  • accelerated gradient method
  • distributed method
  • distributed optimal power flow problem
  • multi-agent optimization

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