ASYMPTOTIC AGREEMENT AND CONVERGENCE OF ASYNCHRONOUS STOCHASTIC ALGORITHMS.

Shu Li; Tamer Basar

doi:10.1109/cdc.1986.267215

ASYMPTOTIC AGREEMENT AND CONVERGENCE OF ASYNCHRONOUS STOCHASTIC ALGORITHMS.

Research output: Contribution to journal › Conference article › peer-review

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 language	English (US)
Pages (from-to)	242-247
Number of pages	6
Journal	Proceedings of the IEEE Conference on Decision and Control
DOIs	https://doi.org/10.1109/cdc.1986.267215
State	Published - 1986

ASJC Scopus subject areas

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

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10.1109/cdc.1986.267215

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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.",

author = "Shu Li and Tamer Basar",

year = "1986",

doi = "10.1109/cdc.1986.267215",

language = "English (US)",

pages = "242--247",

journal = "Proceedings of the IEEE Conference on Decision and Control",

issn = "0191-2216",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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AU - Basar, Tamer

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N2 - 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.

AB - 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.

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ASYMPTOTIC AGREEMENT AND CONVERGENCE OF ASYNCHRONOUS STOCHASTIC ALGORITHMS.

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