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Introduction to Stochastic Programming (Paperback, 2nd ed. 2011)
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Introduction to Stochastic Programming (Paperback, 2nd ed. 2011)
Series: Springer Series in Operations Research and Financial Engineering
Expected to ship within 9 - 15 working days
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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 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)
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