Stochastic approximation algorithms with constant step size whose average is cooperative

Mapping Intimacies ◽

10.1214/aoap/1029962603 ◽

1999 ◽

Vol 9 (1) ◽

pp. 216-241 ◽

Author(s):

Michel Benaïm ◽

Morris W. Hirsch

Keyword(s):

Approximation Algorithms ◽

Stochastic Approximation ◽

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Rates of Convergence of Adaptive Step-Size of Stochastic Approximation Algorithms

Journal of Mathematical Analysis and Applications ◽

10.1006/jmaa.2000.6703 ◽

2000 ◽

Vol 244 (2) ◽

pp. 333-347

Author(s):

S. Shao ◽

Percy P.C. Yip

Keyword(s):

Approximation Algorithms ◽

Stochastic Approximation ◽

Rates Of Convergence ◽

Step Size ◽

Adaptive Step Size ◽

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Analysis of practical step size selection in stochastic approximation algorithms

Annals of Operations Research ◽

10.1007/s10479-015-1785-9 ◽

2015 ◽

Vol 229 (1) ◽

pp. 759-769 ◽

Author(s):

Qi Wang

Keyword(s):

Approximation Algorithms ◽

Stochastic Approximation ◽

Step Size ◽

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Non-asymptotic confidence bounds for stochastic approximation algorithms with constant step size

Monatshefte für Mathematik ◽

10.1007/bf01301683 ◽

1990 ◽

Vol 110 (3-4) ◽

pp. 297-314 ◽

Author(s):

Georg Ch. Pflug

Keyword(s):

Approximation Algorithms ◽

Stochastic Approximation ◽

Confidence Bounds ◽

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NN-approach to design of the optimal stochastic approximation algorithms

International Conference on Control '94 ◽

10.1049/cp:19940173 ◽

1994 ◽

Author(s):

A.V. Nazin

Keyword(s):

Approximation Algorithms ◽

Stochastic Approximation

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Necessary and sufficient conditions for convergence of stochastic approximation algorithms under arbitrary disturbances

Proceedings of 1995 34th IEEE Conference on Decision and Control ◽

10.1109/cdc.1995.479197 ◽

2002 ◽

Author(s):

S.R. Kulkarni ◽

C.S. Horn

Keyword(s):

Approximation Algorithms ◽

Stochastic Approximation ◽

Sufficient Conditions ◽

Necessary And Sufficient Conditions ◽

Sufficient Conditions For Convergence ◽

Necessary And Sufficient

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Linear and Nonlinear Stochastic Approximation Algorithms for Learning Systems

Pattern Recognition and Machine Learning ◽

10.1007/978-1-4615-7566-5_2 ◽

1971 ◽

pp. 18-28

Author(s):

Y. T. Chien

Keyword(s):

Approximation Algorithms ◽

Stochastic Approximation ◽

Learning Systems ◽

Linear And Nonlinear

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Randomized Difference Two-Timescale Simultaneous Perturbation Stochastic Approximation Algorithms for Simulation Optimization of Hidden Markov Models

10.21236/ada637176 ◽

2000 ◽

Author(s):

Shalabh Bhatnagar ◽

Michael C. Fu ◽

Steven I. Marcus ◽

Shashank Bhatnagar

Keyword(s):

Approximation Algorithms ◽

Hidden Markov Models ◽

Stochastic Approximation ◽

Markov Models ◽

Hidden Markov ◽

Simulation Optimization ◽

Simultaneous Perturbation Stochastic Approximation

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Formal analysis for practical gain sequence selection in recursive stochastic approximation algorithms

2013 47th Annual Conference on Information Sciences and Systems (CISS) ◽

10.1109/ciss.2013.6552320 ◽

2013 ◽

Author(s):

Qi Wang

Keyword(s):

Approximation Algorithms ◽

Stochastic Approximation ◽

Formal Analysis ◽

Sequence Selection

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Convergence Rates and Decoupling in Linear Stochastic Approximation Algorithms

SIAM Journal on Control and Optimization ◽

10.1137/14095707x ◽

2015 ◽

Vol 53 (3) ◽

pp. 1484-1508 ◽

Author(s):

Michael A. Kouritzin ◽

Samira Sadeghi

Keyword(s):

Approximation Algorithms ◽

Stochastic Approximation ◽

Convergence Rates

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Stochastic approximation algorithms: examples

Partially Observed Markov Decision Processes ◽

10.1017/cbo9781316471104.022 ◽

2016 ◽

pp. 380-424

Author(s):

Vikram Krishnamurthy

Keyword(s):

Approximation Algorithms ◽

Stochastic Approximation

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