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Introduction to Stochastic Programming - John R. Birge; Francois Louveaux
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Introduction to Stochastic Programming - nouveau livre

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eBooks, eBook Download (PDF), Auflage, The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly… Plus…

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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 disciplin… Plus…

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Introduction to Stochastic Programming (Springer Series in Operations Research and Financial Engineering) - Birge, John R. Louveaux, François
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Introduction to Stochastic Programming (Springer Series in Operations Research and Financial Engineering) - nouveau livre

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ISBN: 9781461402374

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Introduction to Stochastic Programming - nouveau livre

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EAN (ISBN-13): 9781461402374
Date de parution: 2011
Editeur: Springer New York

Livre dans la base de données depuis 2008-08-03T05:49:56+02:00 (Paris)
Page de détail modifiée en dernier sur 2024-01-11T11:34:30+01:00 (Paris)
ISBN/EAN: 9781461402374

ISBN - Autres types d'écriture:
978-1-4614-0237-4
Autres types d'écriture et termes associés:
Auteur du livre: franco, louveau, lou, louv, franc, hackmann
Titre du livre: stochastic


Données de l'éditeur

Auteur: John R. Birge; François Louveaux
Titre: Springer Series in Operations Research and Financial Engineering; Introduction to Stochastic Programming
Editeur: Springer; Springer US
485 Pages
Date de parution: 2011-06-15
New York; NY; US
Langue: Anglais
64,19 € (DE)
66,00 € (AT)
71,00 CHF (CH)
Available
XXV, 485 p. 44 illus.

EA; E107; eBook; Nonbooks, PBS / Mathematik/Sonstiges; Unternehmensforschung; Verstehen; Stochastic optimization; Two-Stage Linear Recourse Problems; decision making under uncertainty; dynamic programming; C; Operations Research, Management Science; Statistics and Computing; Optimization; Mathematics and Statistics; Wahrscheinlichkeitsrechnung und Statistik; Mathematische und statistische Software; Optimierung; BB

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)   
Well-paced and wide-ranging introduction to this subject Prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems Provides a first course in stochastic programming suitable for students Includes supplementary material: sn.pub/extras

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