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Applications of Stochastic Programming (Mps-Siam Series on Optimization) (MPS-SIAM Series on Optimization)
Manufacturer: Society for Industrial and Applied Mathematic
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Options, Futures and Other Derivatives (6th Edition)
ASIN: 0898715555 |
Product Description
Research on algorithms and applications of stochastic programming, the study of procedures for decision making under uncertainty over time, has been very active in recent years and deserves to be more widely known. This is the first book devoted to the full scale of applications of stochastic programming and also the first to provide access to publicly available algorithmic systems. The 32 contributed papers in this volume are written by leading stochastic programming specialists and reflect the high level of activity in recent years in research on algorithms and applications. The book introduces the power of stochastic programming to a wider audience and demonstrates the application areas where this approach is superior to other modeling approaches. Applications of Stochastic Programming consists of two parts. The first part presents papers describing publicly available stochastic programming systems that are currently operational. All the codes have been extensively tested and developed and will appeal to researchers and developers who want to make models without extensive programming and other implementation costs. The codes are a synopsis of the best systems available, with the requirement that they be user-friendly, ready to go, and publicly available. The second part of the book is a diverse collection of application papers in areas such as production, supply chain and scheduling, gaming, environmental and pollution control, financial modeling, telecommunications, and electricity. It contains the most complete collection of real applications using stochastic programming available in the literature. The papers show how leading researchers choose to treat randomness when making planning models, with an emphasis on modeling, data, and solution approaches. Contents Preface: Part I: Stochastic Programming Codes; Chapter 1: Stochastic Programming Computer Implementations, Horand I. Gassmann, SteinW.Wallace, and William T. Ziemba; Chapter 2: The SMPS Format for Stochastic Linear Programs, Horand I. Gassmann; Chapter 3: The IBM Stochastic Programming System, Alan J. King, Stephen E.Wright, Gyana R. Parija, and Robert Entriken; Chapter 4: SQG: Software for Solving Stochastic Programming Problems with Stochastic Quasi-Gradient Methods, Alexei A. Gaivoronski; Chapter 5: Computational Grids for Stochastic Programming, Jeff Linderoth and Stephen J.Wright; Chapter 6: Building and Solving Stochastic Linear Programming Models with SLP-IOR, Peter Kall and Jnos Mayer; Chapter 7: Stochastic Programming from Modeling Languages, Emmanuel Fragnire and Jacek Gondzio; Chapter 8: A Stochastic Programming Integrated Environment (SPInE), P. Valente, G. Mitra, and C. A. Poojari; Chapter 9: Stochastic Modelling and Optimization Using Stochastics , M. A. H. ! Dempster, J. E. Scott, and G.W. P. Thompson; Chapter 10: An Integrated Modelling Environment for Stochastic Programming, Horand I. Gassmann and David M. Gay; Part II: Stochastic Programming Applications; Chapter 11: Introduction to Stochastic Programming Applications Horand I. Gassmann, Sandra L. Schwartz, SteinW.Wallace, and William T. Ziemba Chapter 12: Fleet Management, Warren B. Powell and Huseyin Topaloglu; Chapter 13: Modeling Production Planning and Scheduling under Uncertainty, A. Alonso-Ayuso, L. F. Escudero, and M. T. Ortuo; Chapter 14: A Supply Chain Optimization Model for the Norwegian Meat Cooperative, A. Tomasgard and E. Heg; Chapter 15: Melt Control: Charge Optimization via Stochastic Programming, Jitka Dupaˇcov and Pavel Popela; Chapter 16: A Stochastic Programming Model for Network Resource Utilization in the Presence of Multiclass Demand Uncertainty, Julia L. Higle and Suvrajeet Sen; Chapter 17: Stochastic Optimization and Yacht Racing, A. B. Philpott; Chapter 18: Stochastic Approximation, Momentum, and Nash Play, H. Berglann and S. D. Flm; Chapter 19: Stochastic Optimization for Lake Eutrophication Management, Alan J. King, Lszl Somlydy, and Roger J
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Stochastic Approximation and Its Application (Nonconvex Optimization and Its Applications)
Han-Fu Chen
Manufacturer: Springer
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ASIN: 1402008066 |
Book Description
This book presents the recent development of stochastic approximation algorithms with expanding truncations based on the TS (trajectory-subsequence) method, a newly developed method for convergence analysis. This approach is so powerful that conditions used for guaranteeing convergence have been considerably weakened in comparison with those applied in the classical probability and ODE methods. The general convergence theorem is presented for sample paths and is proved in a purely deterministic way. The sample-path description of theorems is particularly convenient for applications. Convergence theory takes both observation noise and structural error of the regression function into consideration. Convergence rates, asymptotic normality and other asymptotic properties are presented as well. Applications of the developed theory to global optimization, blind channel identification, adaptive filtering, system parameter identification, adaptive stabilization and other problems arising from engineering fields are demonstrated.
Audience: Researchers and students of both graduate and undergraduate levels in systems and control, optimization, signal processing, communication and statistics.
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Applications Of Multi-Objective Evolutionary Algorithms (Advances in Natural Computation)
Manufacturer: World Scientific Publishing Company
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ASIN: 9812561064 |
Book Description
This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs). The topics discussed serve to promote a wider understanding as well as the use of MOEAs, the aim being to find good solutions for high-dimensional real-world design applications. The book contains a large collection of MOEA applications from many researchers, and thus provides the practitioner with detailed algorithmic direction to achieve good results in their selected problem domain.
Book Description
Stochastic local search (SLS) algorithms are among the most prominent and successful techniques for solving computationally difficult problems in many areas of computer science and operations research, including propositional satisfiability, constraint satisfaction, routing, and scheduling. SLS algorithms have also become increasingly popular for solving challenging combinatorial problems in many application areas, such as e-commerce and bioinformatics.
Hoos and Stützle offer the first systematic and unified treatment of SLS algorithms. In this groundbreaking new book, they examine the general concepts and specific instances of SLS algorithms and carefully consider their development, analysis and application. The discussion focuses on the most successful SLS methods and explores their underlying principles, properties, and features. This book gives hands-on experience with some of the most widely used search techniques, and provides readers with the necessary understanding and skills to use this powerful tool.
*Provides the first unified view of the field.
*Offers an extensive review of state-of-the-art stochastic local search algorithms and their applications.
*Presents and applies an advanced empirical methodology for analyzing the behavior of SLS algorithms.
*A companion website offers lecture slides as well as source code and Java applets for exploring and demonstrating SLS algorithms.
Customer Reviews:
The First Complete Solution.......2004-11-25
The Travelling Salesman Problem (more politically correctly called the Travelling Salesperson Problem)(How about we call it the TSP?) is a common real world problem. The problem is simplely stated: How do you find the shortest path for a travelling sales_____ to drive as he visits a series of customers.
If you're just running a few errands it's easy enough. If you're going to the supermarket, the post office, the dry cleaners, and the gas station, it's pretty easy to determine the shortest path. But then you add constraints, nearly out of gas, go to gas station first. Buying ice cream at the super market, better make it the last stop.
This is a real world problem. It is faced every day by delivery companies like the post office and air freight companies who spend huge amounts of fuel flying jet freighters around the world -- where do you put a hub, how do you schedule everything to come together.
FedEx solved this problem by running everything through Memphis. This works really well for a package going from New York to LA. It works less well for a package going from Manhattan to the Bronx. And if the package is going from London to Manchester ....
The computation of this and similar problems fall into the general category of Stochastic Local Search. And this book is the first to offer a systematic and unified treatment of SLS. Before this there were a series of technical papers, magazine articles and chapters in more general texts. The book provides the first unified view of the entire field and offers an extensive review of state-of-the-art algorithms and their applications. A companion website offers lecture slides as well as source code and Java applets for exploring and demonstrating the algorithms.
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Stochastic Adaptive Search for Global Optimization (Nonconvex Optimization and Its Applications)
Z.B. Zabinsky
Manufacturer: Springer
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Binding: Hardcover
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ASIN: 140207526X |
Book Description
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.
Book Description
This text is ideal for advanced undergraduate or beginning graduate students. The author first develops the necessary background in probability theory and Markov chains before using it to study a range of randomized algorithms with important applications in optimization and other problems in computing. The book will appeal not only to mathematicians, but to students of computer science who will discover much useful material. This clear and concise introduction to the subject has numerous exercises that will help students to deepen their understanding.
Customer Reviews:
Brilliant!.......2005-11-11
This book is nothing short of brilliant. The pace is
relaxed and discplined at the same time, the examples
are interesting, and the coverage surprisingly extensive
for its mere 124 pages. The idea to present Markov chains
in the context of algorithms and applications is innovative
and very useful. There is no other book of its kind.
Excellent introduction.......2002-12-28
This is a very clearly written, succinct introduction to Markov Chains (it does not aim to be exhaustive).
I think this book offers one of the most accessible and efficient routes to learning the basics about markov chain monte carlo, perfect simulation,sandwiching and simulated annealing.
I enjoyed this book a lot.Better to read this a few times and do the questions, and then if you must, tackle one of the more bloated expositions.
Well suited for courses in Markov chains.......2002-12-21
Markov chains, a sequence of actions with a random element, can be applied to nearly any process that is composed of discrete steps. Such processes include manufacturing operations, building large structures and even visiting web sites on the Internet. This book is a set of lecture notes for a course for advanced undergraduates offered by the author.
It begins with a basic introduction to probability theory and covers some applications of Markov chains that I had never thought about, such as surfing the Internet. There are exercises at the end of each section although solutions are not included. With a section on computer simulation of Markov chains, the book could also be used in a special topics course in computer science, where the emphasis is on optimization.
Markov chains are a valuable tool that should receive more emphasis in the education of math and computer science students. This book can serve as a basic text for classes aimed at both types of student.
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Stochastic Programming (Mathematics and Its Applications)
András Prékopa
Manufacturer: Springer
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ASIN: 0792334825 |
Book Description
Stochastic programming - the science that provides us with tools to design and control stochastic systems with the aid of mathematical programming techniques - lies at the intersection of statistics and mathematical programming. The book
Stochastic
Programming is a comprehensive introduction to the field and its basic mathematical tools. While the mathematics is of a high level, the developed models offer powerful applications, as revealed by the large number of examples presented. The material ranges form basic linear programming to algorithmic solutions of sophisticated systems problems and applications in water resources and power systems, shipbuilding, inventory control, etc.
Audience: Students and researchers who need to solve practical and theoretical problems in operations research, mathematics, statistics, engineering, economics, insurance, finance, biology and environmental protection.
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Stochastic Optimization: Algorithms and Applications (Applied Optimization, Volume 54) (Applied Optimization)
Manufacturer: Springer
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ASIN: 0792369513 |
Book Description
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.
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Optimal Control from Theory to Computer Programs (Solid Mechanics and Its Applications)
V. Arnautu , and
Pekka Neittaanmäki
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ASIN: 1402017715 |
Book Description
The aim of this book is to present the mathematical theory and the know-how to make computer programs for the numerical approximation of Optimal Control of PDE's. The computer programs are presented in a straightforward generic language. As a consequence they are well structured, clearly explained and can be translated easily into any high level programming language. Applications and corresponding numerical tests are also given and discussed. To our knowledge, this is the first book to put together mathematics and computer programs for Optimal Control in order to bridge the gap between mathematical abstract algorithms and concrete numerical ones.
The text is addressed to students and graduates in Mathematics, Mechanics, Applied Mathematics, Numerical Software, Information Technology and Engineering. It can also be used for Master and Ph.D. programs.
Book Description
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.
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- Applied Cryptography: Protocols, Algorithms, and Source Code in C, Second Edition
- Applied Mathematical Demography (Statistics for Biology and Health)
- Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences
- Applied Numerical Methods with MATLAB for Engineers and Scientists
- Applied Numerical Methods with MATLAB for Engineers and Scientists
- Approximation Algorithms
- Basic Business Statistics: Concepts and Applications and CD package (10th Edition)
- Basic Math Concepts for Water and Wastewater Plant Operators (Mathematics for Water and Wastewater Treatment Plant Operations)
- Basics Of Electric Power Transmission
- Biostatistics, Second Edition: A Guide to Design, Analysis and Discovery.
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