Stochastic Approximation Algorithms and Applications

432 Pages·1997·13.45 MB·English

by  Harold J. Kushner

Stochastic Mechanics Applications of
Random Media Mathematics
Signal Processing Stochastic Modelling
and Image Synthesis and Applied Probability
Mathematical Economics
35
Stochastic Optimization
Stochastic Control
Edited by I. Karatzas
M. Yor
Advisory Board P. Bremaud
E. Carlen
R. Dobrushin
W. Fleming
D. Geman
G. Grimmett
G. Papanicolaou
J. Scheinkman
Springer Science+Business Media, LLC
Applications of Mathematics
1 FleminglRishel, Deterministic and Stochastic Optimal Control (1975)
2 Marchuk, Methods of Numerical Mathematics, Second Ed. (1982)
3 Balakrishnan, Applied Functional Analysis, Second Ed. (1981)
4 Borovkov, Stochastic Processes in Queueing Theory (1976)
5 LiptserlShiryayev, Statistics of Random Processes I: General Theory (1977)
6 LiptserlShiryayev, Statistics of Random Processes II: Applications (1978)
7 Vorob'ev, Game Theory: Lectures for Economists and Systems Scientists
(1977)
8 Shiryayev, Optimal Stopping Rules (1978)
9 IbragimovlRozanov, Gaussian Random Processes (1978)
10 Wonham, Linear Multivariable Control: A Geometric Approach, Third Ed.
(1985)
11 Hida, Brownian Motion (1980)
12 Hestenes, Conjugate Direction Methods in Optimization (1980)
13 Kallianpur, Stochastic Filtering Theory (1980)
14 Krylov, Controlled Diffusion Processes (1980)
15 Prabhu, Stochastic Storage Processes: Queues, Insurance Risk, and Dams
(1980)
16 Ibragirnov/Has'minskii, Statistical Estimation: Asymptotic Theory (1981)
17 Cesari, Optimization: Theory and Applications (1982)
18 Elliott, Stochastic Calculus and Applications (1982)
19 MarcbuklShaidourov, Difference Methods and Their Extrapolations (1983)
20 Hijab, Stabilization of Control Systems (1986)
21 Protter, Stochastic Integration and Differential Equations (1990)
22 BenvenistelMetivierlPriouret, Adaptive Algorithms and Stochastic
Approximations (1990)
23 KloedenIPlaten, Numerical Solution of Stochastic Differential Equations
(1992)
24 KushnerIDupuis, Numerical Methods for Stochastic Control Problems
in Continuous Time (1992)
25 Fleming/Soner, Controlled Markov Processes and Viscosity Solutions
(1993)
26 Baccelli/Bremaud, Elements of Queueing Theory (1994)
27 Winkler, Image Analysis, Random Fields, and Dynamic Monte Carlo
Methods: An Introduction to Mathematical Aspects (1994)
28 Kalpazidou, Cycle Representations of Markov Processes (1995)
29 Elliott!A ggoun/Moore, Bidden Markov Models: Estimation and Control
(1995)
30 Hemandez-LermalLasserre, Discrete-Time Markov Control Processes:
Basic Optimality Criteria (1996)
31 DevroyelGyOrfilLugosi, A Probabilistic Theory of Pattern Recognition (1996)
32 Maitra/Sudderth, Discrete Gambling and Stochastic Games (1996)
33 Embrecbts/Kldppelberg/Mikoscb, Modelling Extremal Events (1997)
34 Dutlo, Random Iterative Models (1997)
35 KushnerlYin, Stochastic Approximation Algorithms and Applications (1997)
36 MusielaIRutkowski, Martingale Methods in Financlal Modeling: Theory and
Application (1997)
Harold J. Kushner G. George Yin
Stochastic Approximation
Algorithms and Applications
With 24 Figures
, Springer
Harold J. Kushner G. George Yin
Division of Applied Mathematics Department of Mathematics
Brown University Wayne State University
Providence, RI02912, USA Detroit, MI 48202, USA
Managing Editors
I. Karatzas
Departments of Mathematics and Statistics
Columbia University
New York, NY 10027, USA
M.Yor
CNRS, Laboratoire de Probabilités
Université Pierre et Marie Curie
4, Place Jussieu, Tour 56
F-75252 Paris Cedex 05, France
Mathematics Subject Classification (1991): 62L20, 93E10, 93E23,65C05, 93E35, 93-02, 90C15
Library of Congress Cataloging-in-Publication Data
Kushner, Harold J. (Harold Joseph), 1933-
Stochastic approximation algorithms and applications/Harold J.
Kushner, G. George Yin.
p. cm. — (Applications of mathematics; 35)
Includes bibliographical references and index.
ISBN 978-1-4899-2698-2
1. Stochastic approximation. I. Yin, George, 1954-
II. Title. III. Series.
QA274.2.K88 1997
519.5 '4-dc21 96-48847
Printed on acid-free paper.
ISBN 978-1-4899-2698-2 ISBN 978-1-4899-2696-8 (eBook)
DOI 10.1007/978-1-4899-2696-8
© 1997 Springer Science+Business Media New York
Originally Published by Springer-Verlag New York, Inc in 1997
Softcover reprint of the hardcover 1st edition 1997
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Photocomposed copy prepared from the authors' LaTeX files.
98765432 1
To
Linda, Diana and Nina
and
Chen
Contents
Preface and Introduction xiii
1 Introduction: Applications and Issues 1
1.0 Outline of Chapter . . . . . . . . 1
1.1 The Robbins-Monro Algorithm 2
1.1.1 Introduction . . . . . . . 2
1.1.2 Finding the Zeros of an Unknown Function 5
1.1.3 A Linear Pattern Classifier: Best Linear Least
Squares Fit . . . . . . . . . . . . . . . . . . 8
1.1.4 Minimization by Recursive Monte Carlo 11
1.2 The Kiefer-Wolfowitz Procedure. . 13
1.2.1 The Basic Procedure ........... 13
1.2.2 Random Directions . . . . . . . . . . . . 16
1.3 Extensions of the Algorithms: Variance Reduction,
Robustness, Iterate Averaging, Constraints, and Convex
Optimization . . . . . . . . . . . . . . . . 18
1.3.1 A Variance Reduction Method. . . . . . . . . . 18
1.3.2 Constraints..................... 20
1.3.3 Averaging of the Iterates: "Polyak Averaging" 21
1.3.4 Robust Algorithms ............. 22
1.3.5 Nonexistence of the Derivative at Some (J ••• 22
2 Applications to Learning, State Dependent Noise, and Queueing 25
2.0 Outline of Chapter . . . . . . . . . . . . . . . . . . . . . .. 25
viii Contents
2.1 An Animal Learning Model . 26
2.2 A Neural Network . . . . . . 29
2.3 Q-Learning.......... . . . . . . . 32
2.4 State Dependent Noise: A Motivational Example . 35
2.5 Optimization of a GI/G/1 Queue .. . . . . . . 38
2.5.1 Derivative Estimation and Infinitesimal
Perturbation Analysis: A Brief Review . 39
2.5.2 The Derivative Estimate for the Queueing
Problem. . . . . . . . . . . 41
2.6 Passive Stochastic Approximation . . . . . . . . . . 45
3 Applications in Signal Processing and Adaptive Control 47
3.0 Outline of Chapter . . . . . . . . . . . . 47
3.1 Parameter Identification and Tracking 48
3.1.1 The Classical Model ...... . 48
3.1.2 ARMA and ARMAX Models . 51
3.2 Tracking Time Varying Systems: An Adaptive Step Size
Algorithm . . . . . . . . . . 53
3.2.1 The Algorithm. . . . . . . . . . . . . . . . . . . . 53
3.2.2 Some Data . . . . . . . . . . . . . . . . . . . . . . 56
3.3 Feedback and Averaging in the Identification Algorithm 58
3.4 Applications in Communication Theory. . . . . . . . . . 60
3.4.1 Adaptive Noise Cancellation and Disturbance
Rejection . . . . . . . . 60
3.4.2 Adaptive Equalizers . . . . . . . . . . . . . . . . . 62
4 Mathematical Background 67
4.0 Outline of Chapter . . . . . . . . . . . . . . . . 67
4.1 Martingales, Submartingales, and Inequalities 68
4.2 Ordinary Differential Equations . . . . . . . . 72
4.2.1 Limits of a Sequence of Continuous Functions . 72
4.2.2 Stability of Ordinary Differential Equations . . 74
4.3 Projected ODE . . . . . . . . . . . . . . . . . . . . . . . 77
4.4 Stochastic Stability and Perturbed Stochastic Liapunov
Functions . . . . . . . . . . . . . . . . . . . . . . . . . . .. 80
5 Convergence with Probability One: Martingale Difference Noise 85
5.0 Outline of Chapter. . . . . . . . . . . . . . . . . . . . . 85
5.1 Truncated Algorithms: Introduction. . . . . . . . . . . .. 87
5.2 The ODE Method: A Basic Convergence Theorem .... 93
5.2.1 Assumptions and the Main Convergence Theorem. 93
5.2.2 Chain Recurrence . . . . . . . . . . 102
5.3 A General Compactness Method. . . . . . 107
5.3.1 The Basic Convergence Theorem . 107
Contents ix
5.3.2 Sufficient Conditions for the Rate of Change
Condition . . . . . . . . . . . . . . . . . . . 109
5.3.3 The Kiefer-Wolfowitz Algorithm ..... 113
5.4 Stability and Stability-ODE Methods . . . . . . . 114
5.5 Soft Constraints . . . . . . . . . . . . . . . . . . . 120
5.6 Random Directions, Subgradients, and Differential
Inclusions . . . . . . . . . . . . . . . . . . . . . . . . 122
5.7 Convergence for the Lizard Learning and Pattern
Classification Problems . . . . . . . . . . . . . 125
5.7.1 The Lizard Learning Problem . . . . . . . . 125
5.7.2 The Pattern Classification Problem. . . . . 126
5.8 Convergence to a Local Minimum: A Perturbation
Method . . . . . . . . . . . . . . . . . . . . . . . 127
6 Convergence with Probability One: Correlated Noise 135
6.0 Outline of Chapter . . . . . . . . . . . . . . . . . 135
6.1 A General Compactness Method. . . . . . . . . 136
6.1.1 Introduction and General Assumptions. . 136
6.1.2 The Basic Convergence Theorem . . . . . 140
6.1.3 Local Convergence Results. . . . . . . . . 143
6.2 Sufficient Conditions for the Rate of Change
Assumptions: Laws of Large Numbers. . . . . . . 144
6.3 Perturbed State Criteria for the Rate of Change
Assumptions ... . . . . . . . . . . . . . . . . . . . . . . 146
6.3.1 Introduction to Perturbed Test Functions . . . . 146
6.3.2 General Conditions for the Asymptotic Rate of
Change. . . . . . . . . . . . 148
6.3.3 Alternative Perturbations . ...... 151
6.4 Examples Using State Perturbation ..... . 154
6.5 Kiefer-Wolfowitz Algorithms . . .. ...... .... . 157
6.6 A State Perturbation Method and State Dependent
Noise .............................. . 159
6.7 Stability Methods ...................... . 162
6.8 Differential Inclusions and the Parameter Identification
Problem. . . . . . . . . . . . . . . . . . . . . .. . .... 167
6.9 State Perturbation-Large Deviations Methods . . 168
6.10 Large Deviations Estimates . . . . . . . . . . . 173
6.10.1 Two-Sided Estimates ......... . 173
6.10.2 Upper Bounds and Weaker Conditions 179
6.10.3 Escape Times ............. . 182
7 Weak Convergence: Introduction 185
7.0 Outline of Chapter ..... . 185
7.1 Introduction .......... . 186
7.2 Martingale Difference Noise .. . 189
x Contents
7.3 Weak Convergence. . . . . . . . . . . . . . . . . . . . . .. 198
7.3.1 Definitions . . . . . . . . . . . . . . . . . . . . . .. 198
7.3.2 Basic Convergence Theorems ............ 201
7.4 Martingale Limit Processes and the Wiener Process. . .. 205
7.4.1 Verifying that a Process Is a Martingale . . . . .. 205
7.4.2 The Wiener Process. . . . . . . . . . . . . . . . .. 207
7.4.3 A Perturbed Test Function Method for Verifying
Tightness and the Wiener Process. . . . . . . . .. 208
8 Weak Convergence Methods for General Algorithms 213
8.0 Outline of Chapter. . . . . . . . . . . . . . . . . . . . . .. 213
8.1 Assumptions: Exogenous Noise and Constant Step Size.. 215
8.2 Convergence: Exogenous Noise. . . . . . . . . . . . . . .. 218
8.2.1 Constant Step Size: Martingale Difference Noise . 218
8.2.2 Correlated Noise . . . . . . . . . . . . . . . . . . .. 225
8.2.3 Step Size €n -t 0 . . . . . . . . . . . . . . . . . . .. 228
8.2.4 Random €n • • • • • • • • • • • • • • • • • • • • • •• 231
8.2.5 Differential Inclusions. . . . . . . . . . . . . . . .. 231
8.3 The Kiefer-Wolfowitz Algorithm. . . . . . . . . . . . . .. 232
8.3.1 Martingale Difference Noise .. . . . . . . . . . .. 232
8.3.2 Correlated Noise. . . . . . . . . . . . . . . . . . .. 234
8.4 Markov State Dependent Noise. . . . . . . . . . . . . . .. 238
8.4.1 Constant Step Size .................. 238
8.4.2 Decreasing Step Size €n -t 0 . . . . . . . . . . . .. 242
8.4.3 The Invariant Measure Method: Constant Step
Size . . . . . . . . . . . . . . . . . . . . . . . . . .. 244
8.4.4 An Alternative Form . . . . . . . . . . . . . . . .. 246
8.5 Unconstrained Algorithms .................. 247
9 AppUcations: Proofs of Convergence 251
9.0 Outline of Chapter. . . . . . . . . . . . . . . . . . . . . .. 251
9.1 Average Cost per Unit Time Criteria: Introduction. . .. 252
9.1.1 General Comments .................. 252
9.1.2 A Simple Illustrative SDE Example. . . . . . . .. 254
9.2 A Continuous Time Stochastic Differential Equation
Example . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 258
9.3 A Discrete Example: A GI/G/1 Queue . . . . . . . . . .. 263
9.4 Signal Processing Problems. . . . . . . . . . . . . . . . .. 266
10 Rate of Convergence 273
10.0 Outline of Chapter . . . . . . . . . . . . . . . . . . . . . .. 273
10.1 Exogenous Noise: Constant Step Size . . . . . . . . . . .. 274
10.1.1 Martingale Difference Noise ............. 275
10.1.2 Correlated Noise. . . . . . . . . . . . . . . . . . .. 283
10.2 Exogenous Noise: Decreasing Step Size . . . . . . . . . .. 286