| Stochastic Approximation and Recursive Algorithms and Applications 2003 Edition Contributor(s): Kushner, Harold (Author), Yin, G. George (Author) |
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ISBN: 0387008942 ISBN-13: 9780387008943 Publisher: Springer OUR PRICE: $208.99 Product Type: Hardcover - Other Formats Published: July 2003 Annotation: The book 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, with which the asymptotic behavior is characterized by the limit behavior of a mean ODE. 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, general correlated and state-dependent noise, perturbed test function methods, and large devitations methods, are covered. Many motivational examples from learning theory, ergodic cost problems for discrete event systems, wireless communications, adaptive control, signal processing, and elsewhere, illustrate the application of the theory. This second edition is a thorough revision, although the main features and the structure remain unchanged. It contains many additional applications and results, and more detailed discussion. Harold J. Kushner is a University Professor and Professor of Applied Mathematics at Brown University. He has written numerous books and articles on virtually all aspects of stochastic systems theory, and has received various awards including the IEEE Control Systems Field Award. |
| Additional Information |
| BISAC Categories: - Mathematics | Research - Medical |
| Dewey: 511.4 |
| LCCN: 2003045459 |
| Series: Stochastic Modelling and Applied Probability |
| Physical Information: 1.14" H x 6.42" W x 9.56" (1.83 lbs) 478 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: The basic stochastic approximation algorithms introduced by Robbins and MonroandbyKieferandWolfowitzintheearly1950shavebeenthesubject of an enormous literature, both theoretical and applied. This is due to the large number of applications and the interesting theoretical issues in the analysis of "dynamically de?ned" stochastic processes. The basic paradigm is a stochastic di?erence equation such as ? = ? + Y, where ? takes n+1 n n n n its values in some Euclidean space, Y is a random variable, and the "step n size" > 0 is small and might go to zero as n . In its simplest form, n ? is a parameter of a system, and the random vector Y is a function of n "noise-corrupted" observations taken on the system when the parameter is set to ? . One recursively adjusts the parameter so that some goal is met n asymptotically. Thisbookisconcernedwiththequalitativeandasymptotic properties of such recursive algorithms in the diverse forms in which they arise in applications. There are analogous continuous time algorithms, but the conditions and proofs are generally very close to those for the discrete time case. The original work was motivated by the problem of ?nding a root of a continuous function g (?), where the function is not known but the - perimenter is able to take "noisy" measurements at any desired value of ?. Recursive methods for root ?nding are common in classical numerical analysis, and it is reasonable to expect that appropriate stochastic analogs would also perform well. |