Stochastic Modelling and Applied Probability

Stochastic Approximation and Recursive Algorithms and Applications

Authors: Kushner, Harold, Yin, George

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About this book

 

This revised and expanded second edition presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both constrained and unconstrained problems. There is a complete development of both probability one and weak convergence methods for very general noise processes. The proofs of convergence use the ODE method, the most powerful to date. The assumptions and proof methods are designed to cover the needs of recent applications. The development proceeds from simple to complex problems, allowing the underlying ideas to be more easily understood. Rate of convergence, iterate averaging, high-dimensional problems, stability-ODE methods, two time scale, asynchronous and decentralized algorithms, state-dependent noise, stability methods for correlated noise, perturbed test function methods, and large deviations methods are covered. Many motivating examples from learning theory, ergodic cost problems for discrete event systems, wireless communications, adaptive control, signal processing, and elsewhere illustrate the applications of the theory.

Reviews

From the reviews of the second edition:

"This is the second edition of an excellent book on stochastic approximation, recursive algorithms and applications … . Although the structure of the book has not been changed, the authors have thoroughly revised it and added additional material … ." (Evelyn Buckwar, Zentralblatt MATH, Vol. 1026, 2004)

"The book attempts to convince that … algorithms naturally arise in many application areas … . I do not hesitate to conclude that this book is exceptionally well written. The literature citation is extensive, and pertinent to the topics at hand, throughout. This book could be well suited to those at the level of the graduate researcher and upwards." (A. C. Brooms, Journal of the Royal Statistical Society Series A: Statistics in Society, Vol. 169 (3), 2006)


Table of contents (12 chapters)

  • Introduction: Applications and Issues

    Pages 1-27

  • Applications to Learning, Repeated Games, State Dependent Noise, and Queue Optimization

    Pages 29-62

  • Applications in Signal Processing, Communications, and Adaptive Control

    Pages 63-93

  • Mathematical Background

    Pages 95-115

  • Convergence with Probability One: Martingale Difference Noise

    Pages 117-159

Buy this book

eBook $149.00
price for USA (gross)
  • ISBN 978-0-387-21769-7
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $199.00
price for USA
  • ISBN 978-0-387-00894-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $199.00
price for USA
  • ISBN 978-1-4419-1847-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Stochastic Approximation and Recursive Algorithms and Applications
Authors
Series Title
Stochastic Modelling and Applied Probability
Series Volume
35
Copyright
2003
Publisher
Springer-Verlag New York
Copyright Holder
Springer-Verlag New York
eBook ISBN
978-0-387-21769-7
DOI
10.1007/b97441
Hardcover ISBN
978-0-387-00894-3
Softcover ISBN
978-1-4419-1847-5
Series ISSN
0172-4568
Edition Number
2
Number of Pages
XXII, 478
Topics