Decentralized online convex programming with local information

Maxim Raginsky, Nooshin Kiarashi, Rebecca Willett

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

Abstract

This paper describes a novel approach to decentralized online optimization in a large network of agents. At each stage, the agents face a new objective function that reflects the effects of a changing environment, and each agent can share information pertaining to past decisions and cost functions only with his neighbors. These operating conditions arise in many practical applications, but introduce challenging questions related to the performance of distributed strategies relative to impractical centralized approaches. The proposed algorithm yields small regret (i.e., the difference between the total cost incurred using causally available information and the total cost that would have been incurred in hindsight had all the relevant information been available all at once) and is robust to evolving network topologies. It combines a subgradient-based sequential convex optimization scheme with decentralized decision-making via approximate dynamic programming.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
Pages5363-5369
Number of pages7
StatePublished - Sep 29 2011
Externally publishedYes
Event2011 American Control Conference, ACC 2011 - San Francisco, CA, United States
Duration: Jun 29 2011Jul 1 2011

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2011 American Control Conference, ACC 2011
Country/TerritoryUnited States
CitySan Francisco, CA
Period6/29/117/1/11

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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