Author	Kushner, Harold J. author
Title	Stochastic Approximation Algorithms and Applications [electronic resource] / by Harold J. Kushner, G. George Yin
Imprint	New York, NY : Springer New York : Imprint: Springer, 1997
Connect to	http://dx.doi.org/10.1007/978-1-4899-2696-8
Descript	XXI, 417 p. online resource

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

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

1. Mathematics
2. Probabilities
3. Mathematics
4. Probability Theory and Stochastic Processes