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Stochastic Approximation Approach to Stochastic Programming
"... In this paper we consider optimization problems where the objective function is given in a form of the expectation. A basic difficulty of solving such stochastic optimization problems is that the involved multidimensional integrals (expectations) cannot be computed with high accuracy. The aim of th ..."
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Cited by 267 (20 self)
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of this paper is to compare two computational approaches based on Monte Carlo sampling techniques, namely, the Stochastic Approximation (SA) and the Sample Average Approximation (SAA) methods. Both approaches, the SA and SAA methods, have a long history. Current opinion is that the SAA method can efficiently
Asynchronous stochastic approximation and Qlearning
 Machine Learning
, 1994
"... Abstract £ We provide some general results on the convergence of a class of stochastic approximation algorithms and their parallel and asynchronous variants. We then use these results to study the Qlearning algorithm, areinforcement learning method for solving Markov decision problems, and establi ..."
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Cited by 204 (4 self)
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Abstract £ We provide some general results on the convergence of a class of stochastic approximation algorithms and their parallel and asynchronous variants. We then use these results to study the Qlearning algorithm, areinforcement learning method for solving Markov decision problems
Multivariate Stochastic Approximation Using a Simultaneous Perturbation Gradient Approximation
 IEEE TRANSACTIONS ON AUTOMATIC CONTROL
, 1992
"... Consider the problem of finding a root of the multivariate gradient equation that arises in function minimization. When only noisy measurements of the function are available, a stochastic approximation (SA) algorithm of the general KieferWolfowitz type is appropriate for estimating the root. This p ..."
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Cited by 318 (14 self)
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Consider the problem of finding a root of the multivariate gradient equation that arises in function minimization. When only noisy measurements of the function are available, a stochastic approximation (SA) algorithm of the general KieferWolfowitz type is appropriate for estimating the root
STOCHASTIC APPROXIMATION FOR RELIABILITY PROBLEMS
"... Abstract: The article discusses the mathematical model for the stochastic approximation of reliability problems and an optimality system for the stochastic approximation plan given by M.T. Wasan. The end of the paper defines the concept of adapted stochastic approximation plan (SA). ..."
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Abstract: The article discusses the mathematical model for the stochastic approximation of reliability problems and an optimality system for the stochastic approximation plan given by M.T. Wasan. The end of the paper defines the concept of adapted stochastic approximation plan (SA).
On the convergence of linear stochastic approximation procedures
 IEEE Transactions on Information Theory
, 1996
"... On the convergence of linear stochastic approximation procedures ..."
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Cited by 7 (0 self)
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On the convergence of linear stochastic approximation procedures
Weighted averaging and stochastic approximation
 Math. Control Signals Systems
, 1997
"... We explore the relationship between weighted averaging and stochastic approximation algorithms, and study their convergence via a samplepath analysis. We prove that the convergence of a stochastic approximation algorithm is equivalent to the convergence of the weighted average of the associated n ..."
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Cited by 7 (2 self)
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We explore the relationship between weighted averaging and stochastic approximation algorithms, and study their convergence via a samplepath analysis. We prove that the convergence of a stochastic approximation algorithm is equivalent to the convergence of the weighted average of the associated
Stochastic approximations and differential inclusions
 SIAM Journal on Control and Optimization
, 2005
"... 2000211036251/1 and from UCL’s Centre for Economic Learning and Social Evolution (ELSE). We apply the theoretical results on “stochastic approximations and differential inclusions ” developed in Benaïm, Hofbauer and Sorin (2005) to several adaptive processes used in game theory including: classical ..."
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Cited by 61 (15 self)
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2000211036251/1 and from UCL’s Centre for Economic Learning and Social Evolution (ELSE). We apply the theoretical results on “stochastic approximations and differential inclusions ” developed in Benaïm, Hofbauer and Sorin (2005) to several adaptive processes used in game theory including
Stopping Stochastic Approximation
, 2003
"... The practical application of stochastic approximation methods require a reliable means to stop the iterative process when the estimate is close to the optimal value or when further improvement of the estimate is doubtful. Conventional ideas on stopping stochastic algorithms employ probabilistic crit ..."
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Cited by 2 (0 self)
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The practical application of stochastic approximation methods require a reliable means to stop the iterative process when the estimate is close to the optimal value or when further improvement of the estimate is doubtful. Conventional ideas on stopping stochastic algorithms employ probabilistic
On Samplingcontrolled Stochastic Approximation
 IEEE Transactions on Automatic Control
, 1991
"... In the general area of optimization under uncertainty, there are a large number of applications which require finding the `best' values for a set of control variables or parameters and for which the only data available consist of measurements prone to random errors. Stochastic approximation pro ..."
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Cited by 2 (0 self)
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In the general area of optimization under uncertainty, there are a large number of applications which require finding the `best' values for a set of control variables or parameters and for which the only data available consist of measurements prone to random errors. Stochastic approximation
Quasi stochastic approximation
 in American Control Conference, 2011. ACC ’11
, 2011
"... Abstract — In recent work it was shown that a deterministic analog of stochastic approximation can be formulated to obtain a Qlearning algorithm for approximate optimal control of deterministic and stochastic systems. This paper provides a general foundation for “quasistochastic approximation ” i ..."
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Cited by 2 (1 self)
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Abstract — In recent work it was shown that a deterministic analog of stochastic approximation can be formulated to obtain a Qlearning algorithm for approximate optimal control of deterministic and stochastic systems. This paper provides a general foundation for “quasistochastic approximation
Results 1  10
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