Stochastic Approximation and Optimization of Random Systems
The DMV seminar "Shastische Approximation und Optimierung zufalliger Systeme" was held at Blaubeuren, 28. 5. -4. 6. 1989. The goal was to give an approach to theory and application of shas­ tic approximation in view of optimization problems, especially in engineering systems. These notes are based on the seminar lectures. They consist of three parts: I. Foundations of shastic approximation (H. Walk); n. Applicational aspects of shastic approximation (G. PHug); In. Applications to adaptation :ugorithms (L. Ljung). The prerequisites for reading this book are basic knowledge in probability, mathematical statistics, optimization. We would like to thank Prof. M. Barner and Prof. G. Fischer for the or­ ganization of the seminar. We also thank the participants for their cooperation and our assistants and secretaries for typing the manuscript. November 1991 L. Ljung, G. PHug, H. Walk Table of contents I Foundations of shastic approximation (H. Walk) §1 Almost sure convergence of shastic approximation procedures 2 §2 Recursive methods for linear problems 17 §3 Shastic optimization under shastic constraints 22 §4 A learning model; recursive density estimation 27 §5 Invariance principles in shastic approximation 30 §6 On the theory of large deviations 43 References for Part I 45 11 Applicational aspects of shastic approximation (G. PHug) §7 Markovian shastic optimization and shastic approximation procedures 53 §8 Asymptotic distributions 71 §9 Stopping times 79 §1O Applications of shastic approximation methods 80 References for Part II 90 III Applications to adaptation algorithms (L.
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Stochastic Approximation and Optimization of Random Systems
The DMV seminar "Shastische Approximation und Optimierung zufalliger Systeme" was held at Blaubeuren, 28. 5. -4. 6. 1989. The goal was to give an approach to theory and application of shas­ tic approximation in view of optimization problems, especially in engineering systems. These notes are based on the seminar lectures. They consist of three parts: I. Foundations of shastic approximation (H. Walk); n. Applicational aspects of shastic approximation (G. PHug); In. Applications to adaptation :ugorithms (L. Ljung). The prerequisites for reading this book are basic knowledge in probability, mathematical statistics, optimization. We would like to thank Prof. M. Barner and Prof. G. Fischer for the or­ ganization of the seminar. We also thank the participants for their cooperation and our assistants and secretaries for typing the manuscript. November 1991 L. Ljung, G. PHug, H. Walk Table of contents I Foundations of shastic approximation (H. Walk) §1 Almost sure convergence of shastic approximation procedures 2 §2 Recursive methods for linear problems 17 §3 Shastic optimization under shastic constraints 22 §4 A learning model; recursive density estimation 27 §5 Invariance principles in shastic approximation 30 §6 On the theory of large deviations 43 References for Part I 45 11 Applicational aspects of shastic approximation (G. PHug) §7 Markovian shastic optimization and shastic approximation procedures 53 §8 Asymptotic distributions 71 §9 Stopping times 79 §1O Applications of shastic approximation methods 80 References for Part II 90 III Applications to adaptation algorithms (L.
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Stochastic Approximation and Optimization of Random Systems

Stochastic Approximation and Optimization of Random Systems

Stochastic Approximation and Optimization of Random Systems

Stochastic Approximation and Optimization of Random Systems

Paperback(1992)

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Overview

The DMV seminar "Shastische Approximation und Optimierung zufalliger Systeme" was held at Blaubeuren, 28. 5. -4. 6. 1989. The goal was to give an approach to theory and application of shas­ tic approximation in view of optimization problems, especially in engineering systems. These notes are based on the seminar lectures. They consist of three parts: I. Foundations of shastic approximation (H. Walk); n. Applicational aspects of shastic approximation (G. PHug); In. Applications to adaptation :ugorithms (L. Ljung). The prerequisites for reading this book are basic knowledge in probability, mathematical statistics, optimization. We would like to thank Prof. M. Barner and Prof. G. Fischer for the or­ ganization of the seminar. We also thank the participants for their cooperation and our assistants and secretaries for typing the manuscript. November 1991 L. Ljung, G. PHug, H. Walk Table of contents I Foundations of shastic approximation (H. Walk) §1 Almost sure convergence of shastic approximation procedures 2 §2 Recursive methods for linear problems 17 §3 Shastic optimization under shastic constraints 22 §4 A learning model; recursive density estimation 27 §5 Invariance principles in shastic approximation 30 §6 On the theory of large deviations 43 References for Part I 45 11 Applicational aspects of shastic approximation (G. PHug) §7 Markovian shastic optimization and shastic approximation procedures 53 §8 Asymptotic distributions 71 §9 Stopping times 79 §1O Applications of shastic approximation methods 80 References for Part II 90 III Applications to adaptation algorithms (L.

Product Details

ISBN-13: 9783764327330
Publisher: Birkhäuser Basel
Publication date: 03/31/1992
Series: Oberwolfach Seminars , #17
Edition description: 1992
Pages: 116
Product dimensions: 6.69(w) x 9.61(h) x 0.01(d)

Table of Contents

I Foundations of shastic approximation.- §1 Almost sure convergence of shastic approximation procedures.- §2 Recursive methods for linear problems.- §3 Shastic optimization under shastic constraints.- §4 A learning model; recursive density estimation.- §5 Invariance principles in shastic approximation.- §6 On the theory of large deviations.- References for Part I.- II Applicational aspects of shastic approximation.- §7 Markovian shastic optimization and shastic approximation procedures.- §8 Asymptotic distributions.- §9 Stopping times.- §10 Applications of shastic approximation methods.- References for Part II.- III Applications to adaptation algorithms.- §11 Adaptation and tracking.- §12 Algorithm development.- §13 Asymptotic Properties in the decreasing gain case.- §14 Estimation of the tracking ability of the algorithms.- References for Part III.
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