Stochastic Approximation Algorithms and Applications /
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 ar...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New York, NY :
Springer New York : Springer,
1997.
|
| Series: | Applications of Mathematics, Stochastic Modelling and Applied Probability ;
35. |
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- 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.