Stochastic Approximation and Optimization of Random Systems

Ljung, Lennart; Pflug, Georg Ch. (Institute of Statistics, University of Vienna, Austria); Walk, Harro

ISBN 10: 3764327332 / ISBN 13: 9783764327330

Published by Birkhauser Verlag AG, 1992

About this Item

Series: Oberwolfach Seminars. Num Pages: 124 pages, biography. BIC Classification: PBT; PBWL. Category: (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 244 x 170 x 6. Weight in Grams: 217. . 1992. Paperback. . . . . Books ship from the US and Ireland.

Seller Inventory # V9783764327330

Bibliographic Details

• Title: Stochastic Approximation and Optimization of Random Systems
• Publisher: Birkhauser Verlag AG
• Publication Date: 1992
• Language: English
• ISBN 10: 3764327332
• ISBN 13: 9783764327330
• Binding: Soft cover
• Condition: New

About this title

Synopsis

The DMV seminar "Stochastische 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 stochas­ 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 stochastic approximation (H. Walk); n. Applicational aspects of stochastic 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 stochastic approximation (H. Walk) §1 Almost sure convergence of stochastic approximation procedures 2 §2 Recursive methods for linear problems 17 §3 Stochastic optimization under stochastic constraints 22 §4 A learning model; recursive density estimation 27 §5 Invariance principles in stochastic approximation 30 §6 On the theory of large deviations 43 References for Part I 45 11 Applicational aspects of stochastic approximation (G. PHug) §7 Markovian stochastic optimization and stochastic approximation procedures 53 §8 Asymptotic distributions 71 §9 Stopping times 79 §1O Applications of stochastic approximation methods 80 References for Part II 90 III Applications to adaptation algorithms (L.