Noise conditions for prespecified convergence rates of stochastic approximation algorithms

E.K.P. Chong;  I-Jeng Wang; S.R. Kulkarni

doi:10.1109/18.749035

Noise conditions for prespecified convergence rates of stochastic approximation algorithms

IEEE Transactions on Information Theory ◽

10.1109/18.749035 ◽

1999 ◽

Vol 45 (2) ◽

pp. 810-814 ◽

Cited By ~ 9

Author(s):

E.K.P. Chong ◽

I-Jeng Wang ◽

S.R. Kulkarni

Keyword(s):

Approximation Algorithms ◽

Stochastic Approximation ◽

Convergence Rates

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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 ◽

Cited By ~ 3

Author(s):

Michael A. Kouritzin ◽

Samira Sadeghi

Keyword(s):

Approximation Algorithms ◽

Stochastic Approximation ◽

Convergence Rates

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On conditions for convergence rates of stochastic approximation algorithms

Proceedings of the 36th IEEE Conference on Decision and Control ◽

10.1109/cdc.1997.657113 ◽

2002 ◽

Author(s):

E.K.P. Chong ◽

I.-J. Wang ◽

S.R. Kulkarni

Keyword(s):

Approximation Algorithms ◽

Stochastic Approximation ◽

Convergence Rates

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Convergence rates of finite difference stochastic approximation algorithms part I: general sampling

10.1117/12.2228250 ◽

2016 ◽

Author(s):

Liyi Dai

Keyword(s):

Approximation Algorithms ◽

Finite Difference ◽

Stochastic Approximation ◽

Convergence Rates

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Convergence rates of finite difference stochastic approximation algorithms part II: implementation via common random numbers

10.1117/12.2228251 ◽

2016 ◽

Author(s):

Liyi Dai

Keyword(s):

Approximation Algorithms ◽

Finite Difference ◽

Stochastic Approximation ◽

Convergence Rates ◽

Random Numbers ◽

Common Random Numbers

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NN-approach to design of the optimal stochastic approximation algorithms

International Conference on Control '94 ◽

10.1049/cp:19940173 ◽

1994 ◽

Cited By ~ 1

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 ◽

Cited By ~ 2

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 ◽

Cited By ~ 2

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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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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