Stochastic Approximation Methods For Constrained And Unconstrained Systems
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Stochastic Approximation Methods For Constrained And Unconstrained Systems
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Author : H.J. Kushner
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Stochastic Approximation Methods For Constrained And Unconstrained Systems written by H.J. Kushner and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
The book deals with a powerful and convenient approach to a great variety of types of problems of the recursive monte-carlo or stochastic approximation type. Such recu- sive algorithms occur frequently in stochastic and adaptive control and optimization theory and in statistical esti- tion theory. Typically, a sequence {X } of estimates of a n parameter is obtained by means of some recursive statistical th st procedure. The n estimate is some function of the n_l estimate and of some new observational data, and the aim is to study the convergence, rate of convergence, and the pa- metric dependence and other qualitative properties of the - gorithms. In this sense, the theory is a statistical version of recursive numerical analysis. The approach taken involves the use of relatively simple compactness methods. Most standard results for Kiefer-Wolfowitz and Robbins-Monro like methods are extended considerably. Constrained and unconstrained problems are treated, as is the rate of convergence problem. While the basic method is rather simple, it can be elaborated to allow a broad and deep coverage of stochastic approximation like problems. The approach, relating algorithm behavior to qualitative properties of deterministic or stochastic differ ential equations, has advantages in algorithm conceptualiza tion and design. It is often possible to obtain an intuitive understanding of algorithm behavior or qualitative dependence upon parameters, etc., without getting involved in a great deal of deta~l.
Stochastic Approximation Methods For Constrained And Unconstrained Systems
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Author : H.J. Kushner
language : en
Publisher:
Release Date : 2014-09-01
Stochastic Approximation Methods For Constrained And Unconstrained Systems written by H.J. Kushner and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-01 with categories.
Stochastic Approximation Methods For Constrained And Unconstrained Systems
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Author : Harold Joseph Kushner
language : en
Publisher:
Release Date : 1978
Stochastic Approximation Methods For Constrained And Unconstrained Systems written by Harold Joseph Kushner and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1978 with Approximation stochastique categories.
Stochastic Approximation Methods For Constrained And Unconstrained Systems
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Author : H.J. Kushner
language : en
Publisher: Springer
Release Date : 1978-08-03
Stochastic Approximation Methods For Constrained And Unconstrained Systems written by H.J. Kushner and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1978-08-03 with Mathematics categories.
The book deals with a powerful and convenient approach to a great variety of types of problems of the recursive monte-carlo or stochastic approximation type. Such recu- sive algorithms occur frequently in stochastic and adaptive control and optimization theory and in statistical esti- tion theory. Typically, a sequence {X } of estimates of a n parameter is obtained by means of some recursive statistical th st procedure. The n estimate is some function of the n_l estimate and of some new observational data, and the aim is to study the convergence, rate of convergence, and the pa- metric dependence and other qualitative properties of the - gorithms. In this sense, the theory is a statistical version of recursive numerical analysis. The approach taken involves the use of relatively simple compactness methods. Most standard results for Kiefer-Wolfowitz and Robbins-Monro like methods are extended considerably. Constrained and unconstrained problems are treated, as is the rate of convergence problem. While the basic method is rather simple, it can be elaborated to allow a broad and deep coverage of stochastic approximation like problems. The approach, relating algorithm behavior to qualitative properties of deterministic or stochastic differ ential equations, has advantages in algorithm conceptualiza tion and design. It is often possible to obtain an intuitive understanding of algorithm behavior or qualitative dependence upon parameters, etc., without getting involved in a great deal of deta~l.
Stochastic Approximation Methods For Constrained Unconstrained Systems
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Author : Kushner H.J.
language : it
Publisher:
Release Date :
Stochastic Approximation Methods For Constrained Unconstrained Systems written by Kushner H.J. and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Stochastic Recursive Algorithms For Optimization
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Author : S. Bhatnagar
language : en
Publisher: Springer
Release Date : 2012-08-12
Stochastic Recursive Algorithms For Optimization written by S. Bhatnagar and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-12 with Technology & Engineering categories.
Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms: • are easily implemented; • do not require an explicit system model; and • work with real or simulated data. Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix. The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from similarly diverse backgrounds: workers in relevant areas of computer science, control engineering, management science, applied mathematics, industrial engineering and operations research will find the content of value.
Self Learning Control Of Finite Markov Chains
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Author : A.S. Poznyak
language : en
Publisher: CRC Press
Release Date : 2018-10-03
Self Learning Control Of Finite Markov Chains written by A.S. Poznyak and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-03 with Technology & Engineering categories.
Presents a number of new and potentially useful self-learning (adaptive) control algorithms and theoretical as well as practical results for both unconstrained and constrained finite Markov chains-efficiently processing new information by adjusting the control strategies directly or indirectly.
Selected Papers
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Author : Herbert Robbins
language : en
Publisher: Springer
Release Date : 2012-12-06
Selected Papers written by Herbert Robbins and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
Herbert Robbins is widely recognized as one of the most creative and original mathematical statisticians of our time. The purpose of this book is to reprint, on the occasion of his seventieth birthday, some of his most outstanding research. In making selections for reprinting we have tried to keep in mind three potential audiences: (1) the historian who would like to know Robbins' seminal role in stimulating a substantial proportion of current research in mathematical statistics; (2) the novice who would like a readable, conceptually oriented introduction to these subjects; and (3) the expert who would like to have useful reference material in a single collection. In many cases the needs of the first two groups can be met simulta neously. A distinguishing feature of Robbins' research is its daring originality, which literally creates new specialties for subsequent generations of statisticians to explore. Often these seminal papers are also models of exposition serving to introduce the reader, in the simplest possible context, to ideas that are important for contemporary research in the field. An example is the paper of Robbins and Monro which initiated the subject of stochastic approximation. We have also attempted to provide some useful guidance to the literature in various subjects by supplying additional references, particularly to books and survey articles, with some remarks about important developments in these areas.
Optimization And Learning Via Stochastic Gradient Search
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Author : Felisa Vázquez-Abad
language : en
Publisher: Princeton University Press
Release Date : 2025-10-28
Optimization And Learning Via Stochastic Gradient Search written by Felisa Vázquez-Abad and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-28 with Mathematics categories.
An introduction to gradient-based stochastic optimization that integrates theory and implementation This book explains gradient-based stochastic optimization, exploiting the methodologies of stochastic approximation and gradient estimation. Although the approach is theoretical, the book emphasizes developing algorithms that implement the methods. The underlying philosophy of this book is that when solving real problems, mathematical theory, the art of modeling, and numerical algorithms complement each other, with no one outlook dominating the others. The book first covers the theory of stochastic approximation including advanced models and state-of-the-art analysis methodology, treating applications that do not require the use of gradient estimation. It then presents gradient estimation, developing a modern approach that incorporates cutting-edge numerical algorithms. Finally, the book culminates in a rich set of case studies that integrate the concepts previously discussed into fully worked models. The use of stochastic approximation in statistics and machine learning is discussed, and in-depth theoretical treatments for selected gradient estimation approaches are included. Numerous examples show how the methods are applied concretely, and end-of-chapter exercises enable readers to consolidate their knowledge. Many chapters end with a section on “Practical Considerations” that addresses typical tradeoffs encountered in implementation. The book provides the first unified treatment of the topic, written for a wide audience that includes researchers and graduate students in applied mathematics, engineering, computer science, physics, and economics.
Control And Dynamic Systems V26
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Author : C.T. Leonides
language : en
Publisher: Elsevier
Release Date : 2012-12-02
Control And Dynamic Systems V26 written by C.T. Leonides and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-02 with Technology & Engineering categories.
Control and Dynamic Systems: Advances in Theory and Application, Volume 26: System Identification and Adaptive Control, Part 2 of 3 deals with system parameter identification and adaptive control. It presents useful techniques for effective stochastic adaptive control systems. This volume presents a powerful technique for identifying discrete time and continuous time linear time-invariant multivariable systems. It also includes the use of identifiable representations for linear multivariable systems; parametric identification of transfer functions of linear system; compares model reference adaptive control and model identification control; estimation of transfer function models; multivariable self-tuning control; and covariance analysis. This volume ends with powerful techniques for adaptive control for stochastic linear systems. This text is of great value to practitioners in the field who want a comprehensive reference source of techniques with significant applied implications.