Stochastic Approximations And Adaptive Algorithms

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Adaptive Algorithms and Stochastic Approximations

Author : Albert Benveniste
Publisher : Springer Science & Business Media
ISBN 13 : 3642758940
Total Pages : 373 pages
Book Rating : 4.6/5 (427 download)

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Book Synopsis Adaptive Algorithms and Stochastic Approximations by : Albert Benveniste

Download or read book Adaptive Algorithms and Stochastic Approximations written by Albert Benveniste and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive systems are widely encountered in many applications ranging through adaptive filtering and more generally adaptive signal processing, systems identification and adaptive control, to pattern recognition and machine intelligence: adaptation is now recognised as keystone of "intelligence" within computerised systems. These diverse areas echo the classes of models which conveniently describe each corresponding system. Thus although there can hardly be a "general theory of adaptive systems" encompassing both the modelling task and the design of the adaptation procedure, nevertheless, these diverse issues have a major common component: namely the use of adaptive algorithms, also known as stochastic approximations in the mathematical statistics literature, that is to say the adaptation procedure (once all modelling problems have been resolved). The juxtaposition of these two expressions in the title reflects the ambition of the authors to produce a reference work, both for engineers who use these adaptive algorithms and for probabilists or statisticians who would like to study stochastic approximations in terms of problems arising from real applications. Hence the book is organised in two parts, the first one user-oriented, and the second providing the mathematical foundations to support the practice described in the first part. The book covers the topcis of convergence, convergence rate, permanent adaptation and tracking, change detection, and is illustrated by various realistic applications originating from these areas of applications.

Stochastic Approximations and Adaptive Algorithms

Author : Albert Benveniste
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (9 download)

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Book Synopsis Stochastic Approximations and Adaptive Algorithms by : Albert Benveniste

Download or read book Stochastic Approximations and Adaptive Algorithms written by Albert Benveniste and published by . This book was released on 1990 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Stochastic Approximations

Author : Albert Benveniste
Publisher :
ISBN 13 :
Total Pages : 365 pages
Book Rating : 4.:/5 (9 download)

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Book Synopsis Stochastic Approximations by : Albert Benveniste

Download or read book Stochastic Approximations written by Albert Benveniste and published by . This book was released on 1990 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Stochastic Approximation and Recursive Algorithms and Applications

Author : Harold Kushner
Publisher : Springer Science & Business Media
ISBN 13 : 038721769X
Total Pages : 485 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Stochastic Approximation and Recursive Algorithms and Applications by : Harold Kushner

Download or read book Stochastic Approximation and Recursive Algorithms and Applications written by Harold Kushner and published by Springer Science & Business Media. This book was released on 2006-05-04 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a thorough development of the modern theory of stochastic approximation or recursive stochastic algorithms for both constrained and unconstrained problems. This second edition is a thorough revision, although the main features and structure remain unchanged. It contains many additional applications and results as well as more detailed discussion.

Stochastic Approximation and Adaptive Algorithms for Non-autonomous Differential Equations

Author : Amber Catherine Griffiths
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (115 download)

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Book Synopsis Stochastic Approximation and Adaptive Algorithms for Non-autonomous Differential Equations by : Amber Catherine Griffiths

Download or read book Stochastic Approximation and Adaptive Algorithms for Non-autonomous Differential Equations written by Amber Catherine Griffiths and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Stochastic Approximation Methods for Constrained and Unconstrained Systems

Author : H.J. Kushner
Publisher : Springer Science & Business Media
ISBN 13 : 1468493523
Total Pages : 273 pages
Book Rating : 4.4/5 (684 download)

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Book Synopsis Stochastic Approximation Methods for Constrained and Unconstrained Systems by : H.J. Kushner

Download or read book Stochastic Approximation Methods for Constrained and Unconstrained Systems written by H.J. Kushner and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 and Its Applications

Author : Han-Fu Chen
Publisher : Springer Science & Business Media
ISBN 13 : 0306481669
Total Pages : 369 pages
Book Rating : 4.3/5 (64 download)

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Book Synopsis Stochastic Approximation and Its Applications by : Han-Fu Chen

Download or read book Stochastic Approximation and Its Applications written by Han-Fu Chen and published by Springer Science & Business Media. This book was released on 2005-12-30 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimating unknown parameters based on observation data conta- ing information about the parameters is ubiquitous in diverse areas of both theory and application. For example, in system identification the unknown system coefficients are estimated on the basis of input-output data of the control system; in adaptive control systems the adaptive control gain should be defined based on observation data in such a way that the gain asymptotically tends to the optimal one; in blind ch- nel identification the channel coefficients are estimated using the output data obtained at the receiver; in signal processing the optimal weighting matrix is estimated on the basis of observations; in pattern classifi- tion the parameters specifying the partition hyperplane are searched by learning, and more examples may be added to this list. All these parameter estimation problems can be transformed to a root-seeking problem for an unknown function. To see this, let - note the observation at time i. e. , the information available about the unknown parameters at time It can be assumed that the parameter under estimation denoted by is a root of some unknown function This is not a restriction, because, for example, may serve as such a function.

On-Line Learning in Neural Networks

Author : David Saad
Publisher : Cambridge University Press
ISBN 13 : 9780521117913
Total Pages : 412 pages
Book Rating : 4.1/5 (179 download)

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Book Synopsis On-Line Learning in Neural Networks by : David Saad

Download or read book On-Line Learning in Neural Networks written by David Saad and published by Cambridge University Press. This book was released on 2009-07-30 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: On-line learning is one of the most commonly used techniques for training neural networks. Though it has been used successfully in many real-world applications, most training methods are based on heuristic observations. The lack of theoretical support damages the credibility as well as the efficiency of neural networks training, making it hard to choose reliable or optimal methods. This book presents a coherent picture of the state of the art in the theoretical analysis of on-line learning. An introduction relates the subject to other developments in neural networks and explains the overall picture. Surveys by leading experts in the field combine new and established material and enable nonexperts to learn more about the techniques and methods used. This book, the first in the area, provides a comprehensive view of the subject and will be welcomed by mathematicians, scientists and engineers, both in industry and academia.

Introduction to Stochastic Search and Optimization

Author : James C. Spall
Publisher : John Wiley & Sons
ISBN 13 : 0471441902
Total Pages : 620 pages
Book Rating : 4.4/5 (714 download)

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Book Synopsis Introduction to Stochastic Search and Optimization by : James C. Spall

Download or read book Introduction to Stochastic Search and Optimization written by James C. Spall and published by John Wiley & Sons. This book was released on 2005-03-11 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: * Unique in its survey of the range of topics. * Contains a strong, interdisciplinary format that will appeal to both students and researchers. * Features exercises and web links to software and data sets.

A Rapid Introduction to Adaptive Filtering

Author : Leonardo Rey Vega
Publisher : Springer Science & Business Media
ISBN 13 : 3642302998
Total Pages : 128 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis A Rapid Introduction to Adaptive Filtering by : Leonardo Rey Vega

Download or read book A Rapid Introduction to Adaptive Filtering written by Leonardo Rey Vega and published by Springer Science & Business Media. This book was released on 2012-08-07 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, the authors provide insights into the basics of adaptive filtering, which are particularly useful for students taking their first steps into this field. They start by studying the problem of minimum mean-square-error filtering, i.e., Wiener filtering. Then, they analyze iterative methods for solving the optimization problem, e.g., the Method of Steepest Descent. By proposing stochastic approximations, several basic adaptive algorithms are derived, including Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS) and Sign-error algorithms. The authors provide a general framework to study the stability and steady-state performance of these algorithms. The affine Projection Algorithm (APA) which provides faster convergence at the expense of computational complexity (although fast implementations can be used) is also presented. In addition, the Least Squares (LS) method and its recursive version (RLS), including fast implementations are discussed. The book closes with the discussion of several topics of interest in the adaptive filtering field.

Adaptive Stochastic Methods

Author : Dmitry G. Arseniev
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3110553678
Total Pages : 323 pages
Book Rating : 4.1/5 (15 download)

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Book Synopsis Adaptive Stochastic Methods by : Dmitry G. Arseniev

Download or read book Adaptive Stochastic Methods written by Dmitry G. Arseniev and published by Walter de Gruyter GmbH & Co KG. This book was released on 2018-01-09 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph develops adaptive stochastic methods in computational mathematics. The authors discuss the basic ideas of the algorithms and ways to analyze their properties and efficiency. Methods of evaluation of multidimensional integrals and solutions of integral equations are illustrated by multiple examples from mechanics, theory of elasticity, heat conduction and fluid dynamics. Contents Part I: Evaluation of Integrals Fundamentals of the Monte Carlo Method to Evaluate Definite Integrals Sequential Monte Carlo Method and Adaptive Integration Methods of Adaptive Integration Based on Piecewise Approximation Methods of Adaptive Integration Based on Global Approximation Numerical Experiments Adaptive Importance Sampling Method Based on Piecewise Constant Approximation Part II: Solution of Integral Equations Semi-Statistical Method of Solving Integral Equations Numerically Problem of Vibration Conductivity Problem on Ideal-Fluid Flow Around an Airfoil First Basic Problem of Elasticity Theory Second Basic Problem of Elasticity Theory Projectional and Statistical Method of Solving Integral Equations Numerically

Stochastic Approximation

Author : Vivek S. Borkar
Publisher : Springer
ISBN 13 : 938627938X
Total Pages : 177 pages
Book Rating : 4.3/5 (862 download)

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Book Synopsis Stochastic Approximation by : Vivek S. Borkar

Download or read book Stochastic Approximation written by Vivek S. Borkar and published by Springer. This book was released on 2009-01-01 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Approximate Dynamic Programming

Author : Warren B. Powell
Publisher : John Wiley & Sons
ISBN 13 : 0470182954
Total Pages : 487 pages
Book Rating : 4.4/5 (71 download)

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Book Synopsis Approximate Dynamic Programming by : Warren B. Powell

Download or read book Approximate Dynamic Programming written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2007-10-05 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete and accessible introduction to the real-world applications of approximate dynamic programming With the growing levels of sophistication in modern-day operations, it is vital for practitioners to understand how to approach, model, and solve complex industrial problems. Approximate Dynamic Programming is a result of the author's decades of experience working in large industrial settings to develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. This groundbreaking book uniquely integrates four distinct disciplines—Markov design processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully model and solve a wide range of real-life problems using the techniques of approximate dynamic programming (ADP). The reader is introduced to the three curses of dimensionality that impact complex problems and is also shown how the post-decision state variable allows for the use of classical algorithmic strategies from operations research to treat complex stochastic optimization problems. Designed as an introduction and assuming no prior training in dynamic programming of any form, Approximate Dynamic Programming contains dozens of algorithms that are intended to serve as a starting point in the design of practical solutions for real problems. The book provides detailed coverage of implementation challenges including: modeling complex sequential decision processes under uncertainty, identifying robust policies, designing and estimating value function approximations, choosing effective stepsize rules, and resolving convergence issues. With a focus on modeling and algorithms in conjunction with the language of mainstream operations research, artificial intelligence, and control theory, Approximate Dynamic Programming: Models complex, high-dimensional problems in a natural and practical way, which draws on years of industrial projects Introduces and emphasizes the power of estimating a value function around the post-decision state, allowing solution algorithms to be broken down into three fundamental steps: classical simulation, classical optimization, and classical statistics Presents a thorough discussion of recursive estimation, including fundamental theory and a number of issues that arise in the development of practical algorithms Offers a variety of methods for approximating dynamic programs that have appeared in previous literature, but that have never been presented in the coherent format of a book Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction to dynamic modeling and is also a valuable guide for the development of high-quality solutions to problems that exist in operations research and engineering. The clear and precise presentation of the material makes this an appropriate text for advanced undergraduate and beginning graduate courses, while also serving as a reference for researchers and practitioners. A companion Web site is available for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book's main concepts.

An Adaptive Stochastic Approximation Algorithm for Estimating Time-varying Parameters

Author : Robert Carbone
Publisher :
ISBN 13 :
Total Pages : 68 pages
Book Rating : 4.:/5 (776 download)

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Book Synopsis An Adaptive Stochastic Approximation Algorithm for Estimating Time-varying Parameters by : Robert Carbone

Download or read book An Adaptive Stochastic Approximation Algorithm for Estimating Time-varying Parameters written by Robert Carbone and published by . This book was released on 1975 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Stochastic Recursive Algorithms for Optimization

Author : S. Bhatnagar
Publisher : Springer
ISBN 13 : 1447142853
Total Pages : 310 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Stochastic Recursive Algorithms for Optimization by : S. Bhatnagar

Download or read book Stochastic Recursive Algorithms for Optimization written by S. Bhatnagar and published by Springer. This book was released on 2012-08-11 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Advanced Lectures on Machine Learning

Author : Olivier Bousquet
Publisher : Springer
ISBN 13 : 3540286500
Total Pages : 249 pages
Book Rating : 4.5/5 (42 download)

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Book Synopsis Advanced Lectures on Machine Learning by : Olivier Bousquet

Download or read book Advanced Lectures on Machine Learning written by Olivier Bousquet and published by Springer. This book was released on 2011-03-22 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.

Stochastic Algorithms: Foundations and Applications

Author : Kathleen Steinhöfel
Publisher : Springer
ISBN 13 : 3540453229
Total Pages : 206 pages
Book Rating : 4.5/5 (44 download)

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Book Synopsis Stochastic Algorithms: Foundations and Applications by : Kathleen Steinhöfel

Download or read book Stochastic Algorithms: Foundations and Applications written by Kathleen Steinhöfel and published by Springer. This book was released on 2003-07-31 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: SAGA 2001, the ?rst Symposium on Stochastic Algorithms, Foundations and Applications, took place on December 13–14, 2001 in Berlin, Germany. The present volume comprises contributed papers and four invited talks that were included in the ?nal program of the symposium. Stochastic algorithms constitute a general approach to ?nding approximate solutions to a wide variety of problems. Although there is no formal proof that stochastic algorithms perform better than deterministic ones, there is evidence by empirical observations that stochastic algorithms produce for a broad range of applications near-optimal solutions in a reasonable run-time. The symposium aims to provide a forum for presentation of original research in the design and analysis, experimental evaluation, and real-world application of stochastic algorithms. It focuses, in particular, on new algorithmic ideas invo- ing stochastic decisions and exploiting probabilistic properties of the underlying problem domain. The program of the symposium re?ects the e?ort to promote cooperation among practitioners and theoreticians and among algorithmic and complexity researchers of the ?eld. In this context, we would like to express our special gratitude to DaimlerChrysler AG for supporting SAGA 2001. The contributed papers included in the proceedings present results in the following areas: Network and distributed algorithms; local search methods for combinatorial optimization with application to constraint satisfaction problems, manufacturing systems, motor control unit calibration, and packing ?exible - jects; and computational learning theory.