PDF Ebook Stochastic Approximation and Recursive Algorithms and Applications (Stochastic Modelling and Applied Probability) (v. 35)
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Stochastic Approximation and Recursive Algorithms and Applications (Stochastic Modelling and Applied Probability) (v. 35)
PDF Ebook Stochastic Approximation and Recursive Algorithms and Applications (Stochastic Modelling and Applied Probability) (v. 35)
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Review
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)
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From the Back Cover
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.
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Product details
Series: Stochastic Modelling and Applied Probability (Book 35)
Hardcover: 478 pages
Publisher: Springer; 2nd edition (July 17, 2003)
Language: English
ISBN-10: 0387008942
ISBN-13: 978-0387008943
Product Dimensions:
6.1 x 1.1 x 9.2 inches
Shipping Weight: 1.8 pounds (View shipping rates and policies)
Average Customer Review:
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Amazon Best Sellers Rank:
#2,150,500 in Books (See Top 100 in Books)
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