E-Book, Englisch, Band 35, 417 Seiten, eBook
Kushner / Yin Stochastic Approximation and Recursive Algorithms and Applications
Erscheinungsjahr 2013
ISBN: 978-1-4899-2696-8
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, Band 35, 417 Seiten, eBook
Reihe: Stochastic Modelling and Applied Probability
ISBN: 978-1-4899-2696-8
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark
In recent years algorithms of the stochastic approximation type have found applications in new and diverse areas, and new techniques have been developed for proofs of convergence and rate of convergence. The actual and potential applications in signal processing have exploded. New challenges have arisen in applications to adaptive control. This book presents a thorough coverage of the ODE method used to analyze these algorithms.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
1 Introduction: Applications and Issues.- 2 Applications to Learning, State Dependent Noise, and Queueing.- 3 Applications in Signal Processing and Adaptive Control.- 4 Mathematical Background.- 5 Convergence with Probability One: Martingale Difference Noise.- 6 Convergence with Probability One: Correlated Noise.- 7 Weak Convergence: Introduction.- 8 Weak Convergence Methods for General Algorithms.- 9 Applications: Proofs of Convergence.- 10 Rate of Convergence.- 11 Averaging of the Iterates.- 12 Distributed/Decentralized and Asynchronous Algorithms.- References.- Symbol Index.
