Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorithms and as models of some stochastic dynamic phenomena. This article gives an overview of the known results about their asymptotic behaviour, highlights recent developments such as distributed and multiscale algorithms, and describes existing and potential applications, and other related issues
Stochastic approximations is a rich branch of probability theory and has a wide range of application...
Stochastic approximations is a rich branch of probability theory and has a wide range of application...
We present a sufficient and necessary condition for the convergence of stochastic approximation algo...
Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorit...
Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorit...
Stochastic approximation algorithms are iterative procedures which are used to approximate a target ...
Approximation algorithms are the prevalent solution methods in the field of stochastic programming. ...
abstract (abridged): many of the present problems in automatic control economic systems and living o...
In this paper, a distributed stochastic approximation algorithm is studied. Applications of such alg...
Approximation algorithms are the prevalent solution methods in the field of stochastic programming. ...
Solutions techniques for stochastic programs are reviewed. Particular emphasis is placed on those me...
We present a sufficient and necessary condition for the convergence of stochastic approximation algo...
We propose a new adaptive algorithm with decreasing step-size for stochastic approximations. The use...
AbstractWe propose a new adaptive algorithm with decreasing step-size for stochastic approximations....
Stochastic approximations is a rich branch of probability theory and has a wide range of application...
Stochastic approximations is a rich branch of probability theory and has a wide range of application...
Stochastic approximations is a rich branch of probability theory and has a wide range of application...
We present a sufficient and necessary condition for the convergence of stochastic approximation algo...
Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorit...
Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorit...
Stochastic approximation algorithms are iterative procedures which are used to approximate a target ...
Approximation algorithms are the prevalent solution methods in the field of stochastic programming. ...
abstract (abridged): many of the present problems in automatic control economic systems and living o...
In this paper, a distributed stochastic approximation algorithm is studied. Applications of such alg...
Approximation algorithms are the prevalent solution methods in the field of stochastic programming. ...
Solutions techniques for stochastic programs are reviewed. Particular emphasis is placed on those me...
We present a sufficient and necessary condition for the convergence of stochastic approximation algo...
We propose a new adaptive algorithm with decreasing step-size for stochastic approximations. The use...
AbstractWe propose a new adaptive algorithm with decreasing step-size for stochastic approximations....
Stochastic approximations is a rich branch of probability theory and has a wide range of application...
Stochastic approximations is a rich branch of probability theory and has a wide range of application...
Stochastic approximations is a rich branch of probability theory and has a wide range of application...
We present a sufficient and necessary condition for the convergence of stochastic approximation algo...
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