On the rate of convergence of distributed subgradient methods for multi-agent optimization

Angelia Nedić, Asuman Ozdaglar

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

53 Scopus citations

Abstract

We study a distributed computation model for optimizing the sum of convex (nonsmooth) objective functions of multiple agents. We provide convergence results and estimates for convergence rate. Our analysis explicitly characterizes the tradeoff between the accuracy of the approximate optimal solutions generated and the number of iterations needed to achieve the given accuracy.

Original languageEnglish (US)
Title of host publicationProceedings of the 46th IEEE Conference on Decision and Control 2007, CDC
Pages4711-4716
Number of pages6
DOIs
StatePublished - Dec 1 2007
Externally publishedYes
Event46th IEEE Conference on Decision and Control 2007, CDC - New Orleans, LA, United States
Duration: Dec 12 2007Dec 14 2007

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other46th IEEE Conference on Decision and Control 2007, CDC
CountryUnited States
CityNew Orleans, LA
Period12/12/0712/14/07

ASJC Scopus subject areas

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

Cite this

Nedić, A., & Ozdaglar, A. (2007). On the rate of convergence of distributed subgradient methods for multi-agent optimization. In Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC (pp. 4711-4716). [4434693] (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2007.4434693