Asymptotic Agreement and Convergence of Asynchronous Stochastic Algorithms

Shu Li, Tamer Başar

Research output: Contribution to journalArticle

Abstract

In this paper, we present results on the convergence and asymptotic agreement of a class of asynchronous stochastic distributed algorithms which are in general time-varying, memory-dependent, and not necessarily associated with the optimization of a common cost functional. We show that convergence and agreement can be reached by distributed learning and computation under a number of conditions, in which case a separation of fast and slow parts of the algorithm is possible, leading to a separation of the estimation part from the main algorithm.

Original languageEnglish (US)
Pages (from-to)612-618
Number of pages7
JournalIEEE Transactions on Automatic Control
Volume32
Issue number7
DOIs
StatePublished - Jul 1987

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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