Stochastic Approximation and Recursive Algorithms and Applications (2003)

Harold Kushner,G George Yin

ISBN 10: 0387008942 / ISBN 13: 9780387008943

Verlag: Springer July 2003, 2003

Beschreibung

used hardcover copy in illustrated boards, no jacket, as issued. light shelfwear, corners perhaps slightly bumped. pages and binding are clean, straight and tight. there are no marks to the text or other serious flaws.

Bestandsnummer des Verkäufers 1453331

Bibliografische Details

• Titel: Stochastic Approximation and Recursive Algorithms and Applications (2003)
• Verlag: Springer July 2003
• Erscheinungsjahr: 2003
• Sprache: Englisch
• ISBN-10: 0387008942
• ISBN-13: 9780387008943
• Einband: Hardcover
• Auflage: 2. Auflage
• Zustand: VG
• Verkäuferkataloge:
  • MATH misc. and higher

Über diesen Titel

Inhaltsangabe

This book presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both constrained and unconstrained problems. This second edition is a thorough revision, although the main features and structure remain unchanged. It contains many additional applications and results as well as more detailed discussion.

Von der hinteren Coverseite

 

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.