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Introduction to Stochastic Programming / by John R. Birge, François Louveaux
(Springer Series in Operations Research and Financial Engineering. ISSN:21971773)
版 | 2nd ed. 2011. |
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出版者 | (New York, NY : Springer New York : Imprint: Springer) |
出版年 | 2011 |
本文言語 | 英語 |
大きさ | XXV, 485 p. 44 illus : online resource |
著者標目 | *Birge, John R author Louveaux, François author SpringerLink (Online service) |
件 名 | LCSH:Operations research LCSH:Management science LCSH:Mathematical statistics -- Data processing 全ての件名で検索 LCSH:Mathematical optimization FREE:Operations Research, Management Science FREE:Statistics and Computing FREE:Optimization |
一般注記 | Introduction and Examples -- Uncertainty and Modeling Issues -- Basic Properties and Theory -- The Value of Information and the Stochastic Solution -- Two-Stage Recourse Problems -- Multistage Stochastic Programs -- Stochastic Integer Programs -- Evaluating and Approximating Expectations -- Monte Carlo Methods -- Multistage Approximations -- Sample Distribution Functions -- References The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. At the same time, it is now 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 aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on 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. In this extensively updated new edition there is more material on methods and examples including several new approaches for discrete variables, new results on risk measures in modeling and Monte Carlo sampling methods, a new chapter on relationships to other methods including approximate dynamic programming, robust optimization and online methods. The book is highly illustrated with chapter summaries and many examples and exercises. Students, researchers and practitioners in operations research and the optimization area will find it particularly of interest. Review of First Edition: "The discussion on modeling issues, the large number of examples used to illustrate the material, and the breadth of the coverage make 'Introduction to Stochastic Programming' an ideal textbook for the area." (Interfaces, 1998) HTTP:URL=https://doi.org/10.1007/978-1-4614-0237-4 |
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電子ブック | 配架場所 | 資料種別 | 巻 次 | 請求記号 | 状 態 | 予約 | コメント | ISBN | 刷 年 | 利用注記 | 指定図書 | 登録番号 |
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電子ブック | オンライン | 電子ブック |
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Springer eBooks | 9781461402374 |
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EB00228517 |
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データ種別 | 電子ブック |
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分 類 | LCC:T57.6-57.97 LCC:T55.4-60.8 DC23:003 |
書誌ID | 4000114977 |
ISBN | 9781461402374 |
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