Stochastic programming
by Peter Kall
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'Stochastic Programming' is the first textbook to provide a thorough and self-contained introduction to the subject. Carefully written to cover all necessary background material from both linear and non-linear programming, as well as probability theory, the book draws together the methods and techniques previously described in disparate sources.
After introducing the terms and modelling issues when randomness is introduced in a deterministic mathematical programming model, the authors cover decision trees and dynamic programming, recourse problems, probabilistic constraints, preprocessing and network problems. Exercises are provided at the end of each chapter.
Throughout, the emphasis is on the appropriate use of the techniques, rather than on the underlying mathematical proofs and theories, making the book ideal for researchers and students in mathematical programming and operations research who wish to develop their skills in stochastic programming.
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Stochastic programming, Stochastic processesShowing 1 featured edition. View all 1 editions?
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Includes bibliographical references and index.
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| July 29, 2024 | Edited by MARC Bot | import existing book |
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