A two-time scale stochastic approximation algorithm is proposed for simulation-based parametric optimization of hidden Markov models, as an alternative to the traditional approaches to "infinitesimal perturbation analysis." Its convergence is analyzed, and a queueing example is presented
Simultaneous perturbation stochastic approximation (SPSA) algorithms have been found to be very effe...
Simultaneous perturbation stochastic approximation (SPSA) algorithms have been found to be very effe...
IIn this paper, we extend the framework of the convergence ofstochastic approximations. Such a proce...
A two-time scale stochastic approximation algorithm is proposed for simulation-based parametric opti...
A two timescale stochastic approximation scheme which uses coupled iterations is used for simulation...
Several robust algorithms for parametric optimization of hidden Markov models are presented. These c...
The authors develop a two-timescale simultaneous perturbation stochastic approximation algorithm for...
The actor-critic algorithm of Barto and others for simulation-based optimization of Markov decision ...
Approaches like finite differences with common random numbers, infinitesimal perturbation analysis, ...
In this paper, the problem of the optimal quantization of a signal generated by a hidden Markov mode...
Stochastic realization is still an open problem for the class of hidden Markov models (HMM): given t...
We develop in this article, four adaptive three-timescale stochastic approximation algorithms for si...
summary:A general multistage stochastic programming problem can be introduced as a finite system of ...
Abstract. This paper studies multi-level stochastic approximation algorithms. Our aim is to extend t...
Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorit...
Simultaneous perturbation stochastic approximation (SPSA) algorithms have been found to be very effe...
Simultaneous perturbation stochastic approximation (SPSA) algorithms have been found to be very effe...
IIn this paper, we extend the framework of the convergence ofstochastic approximations. Such a proce...
A two-time scale stochastic approximation algorithm is proposed for simulation-based parametric opti...
A two timescale stochastic approximation scheme which uses coupled iterations is used for simulation...
Several robust algorithms for parametric optimization of hidden Markov models are presented. These c...
The authors develop a two-timescale simultaneous perturbation stochastic approximation algorithm for...
The actor-critic algorithm of Barto and others for simulation-based optimization of Markov decision ...
Approaches like finite differences with common random numbers, infinitesimal perturbation analysis, ...
In this paper, the problem of the optimal quantization of a signal generated by a hidden Markov mode...
Stochastic realization is still an open problem for the class of hidden Markov models (HMM): given t...
We develop in this article, four adaptive three-timescale stochastic approximation algorithms for si...
summary:A general multistage stochastic programming problem can be introduced as a finite system of ...
Abstract. This paper studies multi-level stochastic approximation algorithms. Our aim is to extend t...
Stochastic approximation is a common paradigm for many stochastic recursions arising both as algorit...
Simultaneous perturbation stochastic approximation (SPSA) algorithms have been found to be very effe...
Simultaneous perturbation stochastic approximation (SPSA) algorithms have been found to be very effe...
IIn this paper, we extend the framework of the convergence ofstochastic approximations. Such a proce...
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