Stochastic Approximation and Recursive Algorithms and Applications Tapa dura - 2003
de Harold Kushner; G. George Yin
Descripción de contraportada
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.
Detalles
- Título Stochastic Approximation and Recursive Algorithms and Applications
- Autor Harold Kushner; G. George Yin
- Encuadernación Tapa dura
- Edición 2nd
- Páginas 478
- Volúmenes 1
- Idioma ENG
- Editorial Springer
- Fecha de publicación 2003-07-17
- Ilustrado Sí
- ISBN 9780387008943 / 0387008942
- Peso 1.83 libras (0.83 kg)
- Dimensiones 9.56 x 6.42 x 1.14 pulgadas (24.28 x 16.31 x 2.90 cm)
- Library of Congress subjects Algorithms, Recursive functions
- Número de catálogo de la Librería del Congreso de EEUU 2003045459
- Dewey Decimal Code 511.4
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