Stochastic approximation and recursive algorithms and applications

• 作者: Kushner, Harold J.
• 其他作者: Yin, George, , Kushner, Harold J.
• 出版: New York : Springer ©2003.
• 版本: Second edition.
• 稽核項: 1 online resource (xxii, 474 pages) :illustrations.
• 叢書名: Applications of mathematics ;35
• 標題: Approximation stochastique. , Probability & StatisticsGeneral. , Algorithmes d'approximation. , MATHEMATICS Probability & Statistics -- General. , Fonctions récursives. , Electronic books. , MATHEMATICS , Approximation algorithms. , Stochastic approximation. , Recursive functions.
• ISBN: 038721769X , 9780387217697
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• 附註: Revised edition of: Stochastic approximation algorithms and applications. c1997. Includes bibliographical references (pages 443-463) and indexes. Introduction: Applications and Issues -- Applications to Learning, Repeated Games, State Dependent Noise, and Queue Optimization -- Applications to Signal Processing, Communications, and Adaptive Control -- Mathematical Background -- Convergence w.p.1: Martingale Difference Noise -- Convergence w.p.1: Correlated Noise -- Weak Convergence: Introduction -- Weak Convergence Methods for General Algorithms -- Applications: Proofs of Convergence -- Rate of Convergence -- Averaging of the Iterates -- Distributed/Decentralized and Asynchronous Algorithms.
• 摘要: 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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• 系統號: 005308689
• 資料類型: 電子書
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