Accelerated Multi-Agent Optimization Method over Stochastic Networks

Wicak Ananduta, Carlos Ocampo-Martinez, Angelia Nedic

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

1 Scopus citations

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 (US)
Title of host publication2020 59th IEEE Conference on Decision and Control, CDC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2961-2966
Number of pages6
ISBN (Electronic)9781728174471
DOIs
StatePublished - Dec 14 2020
Event59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of
Duration: Dec 14 2020Dec 18 2020

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2020-December
ISSN (Print)0743-1546

Conference

Conference59th IEEE Conference on Decision and Control, CDC 2020
Country/TerritoryKorea, Republic of
CityVirtual, Jeju Island
Period12/14/2012/18/20

Keywords

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

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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