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Cubierta del libro
EBOOKS
Autor Birge, John R. author.

Título Introduction to Stochastic Programming [electronic resource] / by John R. Birge, François Louveaux.

Publicación New York, NY : Springer New York, 2011.
Descripción física XXV, 485 p. 44 illus. online resource.
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
text file PDF rda
Colección Springer Series in Operations Research and Financial Engineering, 1431-8598
Springer Series in Operations Research and Financial Engineering, 1431-8598
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Springer eBooks. Mathematics and Statistics
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Contiene: 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.
Resumen: 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)     .
Materia Mathematics.
Mathematical optimization.
Operations research.
Management science.
Statistics.
Mathematics.
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Operations Research, Management Science.
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Statistics and Computing/Statistics Programs.
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Optimization.
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Autor secundario Louveaux, François., author.
SpringerLink (Online service)
En Springer eBooks
OTRO SOPORTE Printed edition: 9781461402367
ISBN 9781461402374 978-1-4614-0237-4
ISBN/ISSN 10.1007/978-1-4614-0237-4 doi