Stochastic Approximation and Recursive Algorithms and Applications

• Main Authors: Kushner, Harold J. (Author), Yin, G. George. (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic
• Language: English
• Published: New York, NY : Springer New York, 2003.
• Edition: Second Edition.
• Series: Stochastic Modelling and Applied Probability, 35
• Subjects: Mathematics.; Artificial intelligence.; Approximation theory.; Applied mathematics.; Engineering mathematics.; Algorithms.; Probabilities.; Probability Theory and Stochastic Processes.; Artificial Intelligence (incl. Robotics).; Approximations and Expansions.; Applications of Mathematics.
• Online Access: View fulltext via EzAccess

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...

Table of Contents:

• 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.