Stochastic Recursive Algorithms For Optimization

Author : S. Bhatnagar,H.L. Prasad,L.A. Prashanth
Publisher : Springer
Page : 302 pages
File Size : 43,5 Mb
Release : 2012-08-12
Category : Technology & Engineering
ISBN : 1447142861

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Stochastic Recursive Algorithms for Optimization by S. Bhatnagar,H.L. Prasad,L.A. Prashanth Pdf

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 : S. Bhatnagar,H.L. Prasad,L.A. Prashanth
Publisher : Springer
Page : 310 pages
File Size : 50,6 Mb
Release : 2012-08-11
Category : Technology & Engineering
ISBN : 9781447142850

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Stochastic Recursive Algorithms for Optimization by S. Bhatnagar,H.L. Prasad,L.A. Prashanth Pdf

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 : Harold Kushner,G. George Yin
Publisher : Springer Science & Business Media
Page : 478 pages
File Size : 49,7 Mb
Release : 2006-05-04
Category : Mathematics
ISBN : 9780387217697

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Stochastic Approximation and Recursive Algorithms and Applications by Harold Kushner,G. George Yin Pdf

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.

Author : James C. Spall
Publisher : John Wiley & Sons
Page : 620 pages
File Size : 43,8 Mb
Release : 2005-03-11
Category : Mathematics
ISBN : 9780471441908

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Introduction to Stochastic Search and Optimization by James C. Spall Pdf

* 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,G. Pflug,H. Walk
Publisher : Birkhäuser
Page : 120 pages
File Size : 47,8 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9783034886093

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Stochastic Approximation and Optimization of Random Systems by L. Ljung,G. Pflug,H. Walk Pdf

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 : Stanislav Uryasev,Panos M. Pardalos
Publisher : Springer Science & Business Media
Page : 438 pages
File Size : 51,7 Mb
Release : 2013-03-09
Category : Technology & Engineering
ISBN : 9781475765946

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Stochastic Optimization by Stanislav Uryasev,Panos M. Pardalos Pdf

Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic programming approaches have been successfully used in a number of areas such as energy and production planning, telecommunications, and transportation. Recently, the practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modeling, risk management, and probabilistic risk analysis. Major topics in this volume include: (1) advances in theory and implementation of stochastic programming algorithms; (2) sensitivity analysis of stochastic systems; (3) stochastic programming applications and other related topics. Audience: Researchers and academies working in optimization, computer modeling, operations research and financial engineering. The book is appropriate as supplementary reading in courses on optimization and financial engineering.

Author : Warren B. Powell
Publisher : John Wiley & Sons
Page : 1090 pages
File Size : 41,8 Mb
Release : 2022-03-15
Category : Mathematics
ISBN : 9781119815037

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Reinforcement Learning and Stochastic Optimization by Warren B. Powell Pdf

REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.

Author : Kurt Marti
Publisher : Springer Science & Business Media
Page : 332 pages
File Size : 42,7 Mb
Release : 2005
Category : Business & Economics
ISBN : 3540222723

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Stochastic Optimization Methods by Kurt Marti Pdf

This text provides a concise overview of stochastic optimization and considers nonlinear optimization problems. Optimization problems arising in practice involve random parameters. For the computation of robust optimal solutions, deterministic substitute problems are needed. Based on the distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into deterministic substitute problems.

Author : H.J. Kushner,D.S. Clark
Publisher : Springer Science & Business Media
Page : 273 pages
File Size : 40,9 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781468493528

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Stochastic Approximation Methods for Constrained and Unconstrained Systems by H.J. Kushner,D.S. Clark Pdf

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 : Kaddour Najim,Enso Ikonen,Ait-Kadi Daoud
Publisher : Elsevier
Page : 345 pages
File Size : 51,7 Mb
Release : 2004-07-01
Category : Mathematics
ISBN : 9780080517797

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Stochastic Processes by Kaddour Najim,Enso Ikonen,Ait-Kadi Daoud Pdf

A ‘stochastic’ process is a ‘random’ or ‘conjectural’ process, and this book is concerned with applied probability and statistics. Whilst maintaining the mathematical rigour this subject requires, it addresses topics of interest to engineers, such as problems in modelling, control, reliability maintenance, data analysis and engineering involvement with insurance.This book deals with the tools and techniques used in the stochastic process – estimation, optimisation and recursive logarithms – in a form accessible to engineers and which can also be applied to Matlab. Amongst the themes covered in the chapters are mathematical expectation arising from increasing information patterns, the estimation of probability distribution, the treatment of distribution of real random phenomena (in engineering, economics, biology and medicine etc), and expectation maximisation. The latter part of the book considers optimization algorithms, which can be used, for example, to help in the better utilization of resources, and stochastic approximation algorithms, which can provide prototype models in many practical applications. * An engineering approach to applied probabilities and statistics * Presents examples related to practical engineering applications, such as reliability, randomness and use of resources* Readers with varying interests and mathematical backgrounds will find this book accessible

Author : Johannes Schneider,Scott Kirkpatrick
Publisher : Springer Science & Business Media
Page : 568 pages
File Size : 52,5 Mb
Release : 2007-08-06
Category : Computers
ISBN : 9783540345602

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Stochastic Optimization by Johannes Schneider,Scott Kirkpatrick Pdf

This book addresses stochastic optimization procedures in a broad manner. The first part offers an overview of relevant optimization philosophies; the second deals with benchmark problems in depth, by applying a selection of optimization procedures. Written primarily with scientists and students from the physical and engineering sciences in mind, this book addresses a larger community of all who wish to learn about stochastic optimization techniques and how to use them.

Author : Mykel J. Kochenderfer,Tim A. Wheeler
Publisher : MIT Press
Page : 521 pages
File Size : 41,9 Mb
Release : 2019-03-12
Category : Computers
ISBN : 9780262039420

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Algorithms for Optimization by Mykel J. Kochenderfer,Tim A. Wheeler Pdf

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.

Author : Saul Brian Gelfand,Sanjoy K. Mitter,Center for Intelligent Control Systems (U.S.),Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Publisher : Unknown
Page : 70 pages
File Size : 45,7 Mb
Release : 1990
Category : Electronic
ISBN : OCLC:20940740

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Recursive Stochastic Algorithms for Global Optimization in IRd̳ by Saul Brian Gelfand,Sanjoy K. Mitter,Center for Intelligent Control Systems (U.S.),Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Pdf

Author : Wolfgang Bühler,Gustav Feichtinger,Richard F. Hartl,Franz Josef Radermacher,Paul Stähly
Publisher : Springer Science & Business Media
Page : 654 pages
File Size : 47,5 Mb
Release : 2012-12-06
Category : Business & Economics
ISBN : 9783642772542

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DGOR by Wolfgang Bühler,Gustav Feichtinger,Richard F. Hartl,Franz Josef Radermacher,Paul Stähly Pdf

The book presents the results of the joint annual conference of the four Operations Research Societies DGOR, GM\OR, \GOR and SVOR, held in Vienna in 1990. The main goal was to present practical experiences as well as theoretical results. Both aspects are covered in a balanced way. Papers cover topics from the fields Optimization, Stochastic Modells, Decision Theory and Multicriteria Decision Making, Control Theory, Mathematical Economics, Game Theory, Macroeconomics, Econometrics and Statistics, Supercomputing and Simulation, Non-linear Systems, Artificial Intelligence and Expert Systems, Fuzzy Sets and Systems, Production, Logistics, Inventory and Marketing among others.

Author : Xi-Ren Cao
Publisher : Springer Science & Business Media
Page : 575 pages
File Size : 43,6 Mb
Release : 2007-10-23
Category : Computers
ISBN : 9780387690827

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Stochastic Learning and Optimization by Xi-Ren Cao Pdf

Performance optimization is vital in the design and operation of modern engineering systems, including communications, manufacturing, robotics, and logistics. Most engineering systems are too complicated to model, or the system parameters cannot be easily identified, so learning techniques have to be applied. This book provides a unified framework based on a sensitivity point of view. It also introduces new approaches and proposes new research topics within this sensitivity-based framework. This new perspective on a popular topic is presented by a well respected expert in the field.