Stochastic Approximation Methods for Constrained and Unconstrained Systems PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Stochastic Approximation Methods for Constrained and Unconstrained Systems PDF full book. Access full book title Stochastic Approximation Methods for Constrained and Unconstrained Systems by H.J. Kushner. Download full books in PDF and EPUB format.
Author: H.J. Kushner Publisher: Springer Science & Business Media ISBN: 1468493523 Category : Mathematics Languages : en Pages : 273
Book Description
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.
Author: H.J. Kushner Publisher: Springer Science & Business Media ISBN: 1468493523 Category : Mathematics Languages : en Pages : 273
Book Description
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.
Author: S. Bhatnagar Publisher: Springer ISBN: 1447142853 Category : Technology & Engineering Languages : en Pages : 310
Book Description
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.
Author: James C. Spall Publisher: John Wiley & Sons ISBN: 0471441902 Category : Mathematics Languages : en Pages : 620
Book Description
* 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.
Author: L. Ljung Publisher: Birkhäuser ISBN: 3034886098 Category : Mathematics Languages : en Pages : 120
Book Description
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.
Author: D. Kannan Publisher: CRC Press ISBN: 9780824706609 Category : Mathematics Languages : en Pages : 800
Book Description
An introduction to general theories of stochastic processes and modern martingale theory. The volume focuses on consistency, stability and contractivity under geometric invariance in numerical analysis, and discusses problems related to implementation, simulation, variable step size algorithms, and random number generation.
Author: Guanghui Lan Publisher: Springer Nature ISBN: 3030395685 Category : Mathematics Languages : en Pages : 591
Book Description
This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.
Author: J. Manley Publisher: Springer Science & Business Media ISBN: 9400906293 Category : Mathematics Languages : en Pages : 544
Book Description
The European Consortium for Mathematics in Industry (ECMI) was founded, largely due to the driving energy of Michiel Hazewinkel on the 14th April, 1986 in Neustadt-Mussbach in West Germany. The founder signatories were A. Bensoussan (INRIA, Paris), A. Fasano (University of Florence), M. Hazewinkel (CWI, Amsterdam), M. Heilio (Lappeenranta University, Finland), F. Hodnett (University of Limerick, Ireland), H. Martens (Norwegian Institute of Technology, Trondheim), S. McKee (University of Strathclyde, Scotland), H. NeURzert (University of Kaiserslautern, Germany), D. Sundstrom (The Swedish Institute of Applied Mathematics, Stockholm), A. Tayler (University of Oxford, England) and Hj. Wacker (University of Linz, Austria). The European Consortium for Mathematics in Industry is dedicated to: (a) promote the use of mathematical models in Industry (b) educate industrial mathematicians to meet the growing demand for such experts (c) operate on a European scale. ECMI is still a young organisation but its membership is growing fast. Although it has still to persuade more industrialists to join, ECMI certainly operates on a European scale and a flourishing postgraduate programme with student exchange has been underway for some time. It is perhaps fitting that the first open meeting of ECMI was held at the University of Strathclyde in Glasgow. Glasgow is and was the industrial capital of Scotland and was, and arguably still is, Britain's second city after London; when this volume appears it will have rightly donned the mantle of the cultural capital of Europe.
Author: Richard A Leondes Publisher: Newnes ISBN: 0323139515 Category : Science Languages : en Pages : 423
Book Description
Control and Dynamic Systems: Advances in Theory and Applications, Volume 36 reviews advances in theory and applications of large scale control and dynamic systems. Contributors focus on production control and the determination of optimal production rates, along with active control systems, uncertainty in control system design, and methods for analyzing multistage commodity markets. This volume is organized into eight chapters and begins with an introduction to multiobjective decision-tree analysis and its significance in applied situations, with two substantive examples. It then shifts to important techniques for the determination of robust economic policies, methods used in the analysis of multistage commodity markets, and a computationally effective algorithm for the determination of the optimal production rate. This book also describes many highly effective techniques for near optimal and robust model truncation. Robust adaptive identification and control algorithms for disturbances and unmodeled system dynamics are given consideration. The final chapter provides examples of the applied significance of the techniques presented in this book, including such large scale systems areas as aerospace, defense, chemical, environmental, and infrastructural industries. This book will be of interest to students and researchers in engineering and computer science.
Author: H.J. Kushner
Author: H.J. Kushner
Author: Harold Joseph Kushner
Author: H.J. Kushner
Author: S. Bhatnagar
Author: James C. Spall
Author: L. Ljung
Author: D. Kannan
Author: Guanghui Lan
Author: J. Manley
Author: Richard A Leondes