Introduction To Stochastic Search And Optimization
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Introduction To Stochastic Search And Optimization
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Author : James C. Spall
language : en
Publisher: John Wiley & Sons
Release Date : 2005-03-11
Introduction To Stochastic Search And Optimization written by James C. Spall and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-03-11 with Mathematics categories.
* 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.
Introduction To Stochastic Programming
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Author : John R. Birge
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-06
Introduction To Stochastic Programming written by John R. Birge 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 2006-04-06 with Mathematics categories.
This rapidly developing field encompasses many disciplines including operations research, mathematics, and probability. Conversely, it is being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors present a broad overview of the main themes and methods of the subject, thus helping students develop an intuition for how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems. The early chapters introduce some worked examples of stochastic programming, demonstrate how a stochastic model is formally built, develop the properties of stochastic programs and the basic solution techniques used to solve them. The book then goes on to cover approximation and sampling techniques and is rounded off by an in-depth case study. A well-paced and wide-ranging introduction to this subject.
Stochastic Adaptive Search For Global Optimization
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Author : Z.B. Zabinsky
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-09-30
Stochastic Adaptive Search For Global Optimization written by Z.B. Zabinsky 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 2003-09-30 with Computers categories.
The book overviews several stochastic adaptive search methods for global optimization and provides analytical results regarding their performance and complexity. It develops a class of hit-and-run algorithms that are theoretically motivated and do not require fine-tuning of parameters. Several engineering global optimization problems are summarized to demonstrate the kinds of practical problems that are now within reach. Audience: This book is suitable for graduate students, researchers and practitioners in operations research, engineering, and mathematics.
Reinforcement Learning And Stochastic Optimization
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Author : Warren B. Powell
language : en
Publisher: John Wiley & Sons
Release Date : 2022-03-15
Reinforcement Learning And Stochastic Optimization written by Warren B. Powell and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-15 with Mathematics categories.
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.
Stochastic Simulation Optimization An Optimal Computing Budget Allocation
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Author : Chun-hung Chen
language : en
Publisher: World Scientific
Release Date : 2010-06-04
Stochastic Simulation Optimization An Optimal Computing Budget Allocation written by Chun-hung Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-04 with Computers categories.
With the advance of new computing technology, simulation is becoming very popular for designing large, complex and stochastic engineering systems, since closed-form analytical solutions generally do not exist for such problems. However, the added flexibility of simulation often creates models that are computationally intractable. Moreover, to obtain a sound statistical estimate at a specified level of confidence, a large number of simulation runs (or replications) is usually required for each design alternative. If the number of design alternatives is large, the total simulation cost can be very expensive.Stochastic Simulation Optimization addresses the pertinent efficiency issue via smart allocation of computing resource in the simulation experiments for optimization, and aims to provide academic researchers and industrial practitioners with a comprehensive coverage of OCBA approach for stochastic simulation optimization. Starting with an intuitive explanation of computing budget allocation and a discussion of its impact on optimization performance, a series of OCBA approaches developed for various problems are then presented, from the selection of the best design to optimization with multiple objectives. Finally, this book discusses the potential extension of OCBA notion to different applications such as data envelopment analysis, experiments of design and rare-event simulation.
First Order And Stochastic Optimization Methods For Machine Learning
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Author : Guanghui Lan
language : en
Publisher: Springer Nature
Release Date : 2020-05-15
First Order And Stochastic Optimization Methods For Machine Learning written by Guanghui Lan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-15 with Mathematics categories.
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.
Stochastic Global Optimization
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Author : Gade Pandu Rangaiah
language : en
Publisher: World Scientific
Release Date : 2010
Stochastic Global Optimization written by Gade Pandu Rangaiah and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Computers categories.
Ch. 1. Introduction / Gade Pandu Rangaiah -- ch. 2. Formulation and illustration of Luus-Jaakola optimization procedure / Rein Luus -- ch. 3. Adaptive random search and simulated annealing optimizers : algorithms and application issues / Jacek M. Jezowski, Grzegorz Poplewski and Roman Bochenek -- ch. 4. Genetic algorithms in process engineering : developments and implementation issues / Abdunnaser Younes, Ali Elkamel and Shawki Areibi -- ch. 5. Tabu search for global optimization of problems having continuous variables / Sim Mong Kai, Gade Pandu Rangaiah and Mekapati Srinivas -- ch. 6. Differential evolution : method, developments and chemical engineering applications / Chen Shaoqiang, Gade Pandu Rangaiah and Mekapati Srinivas -- ch. 7. Ant colony optimization : details of algorithms suitable for process engineering / V.K. Jayaraman [und weitere] -- ch. 8. Particle swarm optimization for solving NLP and MINLP in chemical engineering / Bassem Jarboui [und weitere] -- ch. 9. An introduction to the harmony search algorithm / Gordon Ingram and Tonghua Zhang -- ch. 10. Meta-heuristics : evaluation and reporting techniques / Abdunnaser Younes, Ali Elkamel and Shawki Areibi -- ch. 11. A hybrid approach for constraint handling in MINLP optimization using stochastic algorithms / G.A. Durand [und weitere] -- ch. 12. Application of Luus-Jaakola optimization procedure to model reduction, parameter estimation and optimal control / Rein Luus -- ch. 13. Phase stability and equilibrium calculations in reactive systems using differential evolution and tabu search / Adrian Bonilla-Petriciolet [und weitere] -- ch. 14. Differential evolution with tabu list for global optimization : evaluation of two versions on benchmark and phase stability problems / Mekapati Srinivas and Gade Pandu Rangaiah -- ch. 15. Application of adaptive random search optimization for solving industrial water allocation problem / Grzegorz Poplewski and Jacek M. Jezowski -- ch. 16. Genetic algorithms formulation for retrofitting heat exchanger network / Roman Bochenek and Jacek M. Jezowski -- ch. 17. Ant colony optimization for classification and feature selection / V.K. Jayaraman [und weitere] -- ch. 18. Constraint programming and genetic algorithm / Prakash R. Kotecha, Mani Bhushan and Ravindra D. Gudi -- ch. 19. Schemes and implementations of parallel stochastic optimization algorithms application of tabu search to chemical engineering problems / B. Lin and D.C. Miller
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.
Lectures On Stochastic Programming
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Author : Alexander Shapiro
language : en
Publisher: SIAM
Release Date : 2009-10-08
Lectures On Stochastic Programming written by Alexander Shapiro and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-10-08 with Mathematics categories.
A comprehensive treatment of optimization problems involving uncertain parameters for which stochastic models are available.
Multistage Stochastic Optimization
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Author : Georg Ch. Pflug
language : en
Publisher: Springer
Release Date : 2014-11-12
Multistage Stochastic Optimization written by Georg Ch. Pflug and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-12 with Business & Economics categories.
Multistage stochastic optimization problems appear in many ways in finance, insurance, energy production and trading, logistics and transportation, among other areas. They describe decision situations under uncertainty and with a longer planning horizon. This book contains a comprehensive treatment of today’s state of the art in multistage stochastic optimization. It covers the mathematical backgrounds of approximation theory as well as numerous practical algorithms and examples for the generation and handling of scenario trees. A special emphasis is put on estimation and bounding of the modeling error using novel distance concepts, on time consistency and the role of model ambiguity in the decision process. An extensive treatment of examples from electricity production, asset liability management and inventory control concludes the book.