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 language | English (US) |
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Pages (from-to) | 612-618 |
Number of pages | 7 |
Journal | IEEE Transactions on Automatic Control |
Volume | 32 |
Issue number | 7 |
DOIs | |
State | Published - Jul 1987 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering