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Stochastic Approximation and Recursive Algorithms and Applications

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Cover of 'Stochastic Approximation and Recursive Algorithms and Applications'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction: Applications and Issues
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    Chapter 2 Applications to Learning, Repeated Games, State Dependent Noise, and Queue Optimization
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    Chapter 3 Applications in Signal Processing, Communications, and Adaptive Control
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    Chapter 4 Mathematical Background
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    Chapter 5 Convergence with Probability One: Martingale Difference Noise
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    Chapter 6 Convergence with Probability One: Correlated Noise
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    Chapter 7 Weak Convergence: Introduction
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    Chapter 8 Weak Convergence Methods for General Algorithms
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    Chapter 9 Applications: Proofs of Convergence
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    Chapter 10 Rate of Convergence
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    Chapter 11 Averaging of the Iterates
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    Chapter 12 Distributed/Decentralized and Asynchronous Algorithms
Attention for Chapter 12: Distributed/Decentralized and Asynchronous Algorithms
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Chapter title
Distributed/Decentralized and Asynchronous Algorithms
Chapter number 12
Book title
Stochastic Approximation and Recursive Algorithms and Applications
Published by
Springer, New York, NY, January 2003
DOI 10.1007/0-387-21769-x_12
Book ISBNs
978-0-387-00894-3, 978-0-387-21769-7
View on publisher site

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