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A dynamical system approach to stochastic approximations

Author(s)
Benaim, Michel  
Chaire de probabilités  
Date issued
1996
In
Siam Journal on Control and Optimization
Vol
2
No
34
From page
437
To page
472
Subjects
stochastic approximations ordinary differential equations chain-recurrence neural networks SURE CONVERGENCE ALGORITHMS
Abstract
It is known that some problems of almost sure convergence for stochastic approximation processes can be analyzed via an ordinary differential equation (ODE) obtained by suitable averaging. The goal of this paper is to show that the asymptotic behavior of such a process can be related to the asymptotic behavior of the ODE without any particular assumption concerning the dynamics of this ODE. The main results are as follows: a) The limit sets of trajectory solutions to the stochastic approximation recursion are, under classical assumptions, almost surely nonempty compact connected sets invariant under the how of the ODE and contained in its set of chain-recurrence. b) If the gain parameter goes to zero at a suitable rate depending on the expansion rate of the ODE, any trajectory solution to the recursion is almost surely asymptotic to a forward trajectory solution to the ODE.
Publication type
journal article
Identifiers
https://libra.unine.ch/handle/20.500.14713/56077
DOI
10.1137/S0363012993253534
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A_Dynamical_System_Approach_to.pdf

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