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Stochastic Approximation Algorithms and Applications

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Cover of 'Stochastic Approximation 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, State Dependent Noise, and Queueing
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    Chapter 3 Applications in Signal Processing 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 9: Applications: Proofs of Convergence
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