Stochastic Approximation and Recursive Algorithms and Applications

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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Kushner, Harold J. (Συγγραφέας), Yin, G. George (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: New York, NY : Springer New York, 2003.
Έκδοση:Second Edition.
Σειρά:Stochastic Modelling and Applied Probability, 35
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • Introduction: Applications and Issues
  • Applications to Learning, Repeated Games, State Dependent Noise, and Queue Optimization
  • Applications in Signal Processing, Communications, and Adaptive Control
  • Mathematical Background
  • Convergence with Probability One: Martingale Difference Noise
  • Convergence with Probability One: 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.