We consider a Robbins-Monro type iteration wherein noisy measurements are event-driven and therefore arrive asynchronously. We propose a modification of step-sizes that ensures desired asymptotic behaviour regardless of this aspect. This generalizes earlier results on asynchronous stochastic approximation wherein the asynchronous behaviour is across different components, but not along the same component of the vector iteration, as is the case considered here
Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorit...
In this paper, we examine the intuition that TD() is meant to operate by approximating asynchronous ...
Asynchronous stochastic approximations (SAs) are an important class of model-free algorithms, tools,...
Asymptotic properties of two time-scale stochastic approximation algorithms with constant step sizes...
AbstractA generalization of Robbins-Monro stochastic approximation is presented in the paper. It is ...
Stochastic approximation algorithms are iterative procedures which are used to approximate a target ...
The asymptotic behavior of a distributed, asynchronous stochastic approximation scheme is analyzed i...
In this paper, we give a generalization of a result by Borkar and Meyn (2000) 1], on the stability a...
Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorit...
AbstractWe present and analyze a probabilistic model for asynchronous iteration of linear systems. T...
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....
Under general conditions on the observation processes the almost sure convergence properties of an u...
The Robbins-Monro stochastic approximation procedure is modified so as to be applicable in the prese...
We study the rate of convergence of linear two-time-scale stochastic approximation methods. We consi...
Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorit...
In this paper, we examine the intuition that TD() is meant to operate by approximating asynchronous ...
Asynchronous stochastic approximations (SAs) are an important class of model-free algorithms, tools,...
Asymptotic properties of two time-scale stochastic approximation algorithms with constant step sizes...
AbstractA generalization of Robbins-Monro stochastic approximation is presented in the paper. It is ...
Stochastic approximation algorithms are iterative procedures which are used to approximate a target ...
The asymptotic behavior of a distributed, asynchronous stochastic approximation scheme is analyzed i...
In this paper, we give a generalization of a result by Borkar and Meyn (2000) 1], on the stability a...
Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorit...
AbstractWe present and analyze a probabilistic model for asynchronous iteration of linear systems. T...
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....
Under general conditions on the observation processes the almost sure convergence properties of an u...
The Robbins-Monro stochastic approximation procedure is modified so as to be applicable in the prese...
We study the rate of convergence of linear two-time-scale stochastic approximation methods. We consi...
Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorit...
In this paper, we examine the intuition that TD() is meant to operate by approximating asynchronous ...
Asynchronous stochastic approximations (SAs) are an important class of model-free algorithms, tools,...