ASYMPTOTIC AGREEMENT AND CONVERGENCE OF ASYNCHRONOUS STOCHASTIC ALGORITHMS.

Shu Li, Tamer Basar

Research output: Contribution to journalConference article

Abstract

Results on the convergence and asymptotic agreement of a class of asynchronous distributed algorithms which are in general time-varying, memory-dependent, and not necessarily associated with the optimization of a common cost functional are presented. It is shown 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)242-247
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - Dec 1 1986

    Fingerprint

ASJC Scopus subject areas

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

Cite this