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Lectures on Stochastic Programming

Lectures on Stochastic Programming
Modeling and Theory

Part of MPS-SIAM Series on Optimization

  • Date Published: October 2009
  • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • format: Paperback
  • isbn: 9780898716870

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About the Authors
  • Optimization problems involving stochastic models occur in almost all areas of science and engineering, such as telecommunications, medicine, and finance. Their existence compels a need for rigorous ways of formulating, analyzing, and solving such problems. This book focuses on optimization problems involving uncertain parameters and covers the theoretical foundations and recent advances in areas where stochastic models are available. Readers will find coverage of the basic concepts of modeling these problems, including recourse actions and the nonanticipativity principle. The book also includes the theory of two-stage and multistage stochastic programming problems; the current state of the theory on chance (probabilistic) constraints, including the structure of the problems, optimality theory, and duality; and statistical inference in and risk-averse approaches to stochastic programming.

    • Suitable as a textbook for advanced graduates in optimization
    • Covers the theoretical foundations of modeling optimization problems
    • Includes recent advances in areas where stochastic models are available
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    Product details

    • Date Published: October 2009
    • format: Paperback
    • isbn: 9780898716870
    • length: 450 pages
    • dimensions: 255 x 178 x 20 mm
    • weight: 0.8kg
    • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • Table of Contents

    Preface
    1. Stochastic programming models
    2. Two-stage problems
    3. Multistage problems
    4. Optimization models with probabilistic constraints
    5. Statistical inference
    6. Risk averse optimization
    7. Background material
    8. Bibliographical remarks
    Bibliography
    Index.

  • Resources for

    Lectures on Stochastic Programming

    Alexander Shapiro, Darinka Dentcheva, Andrzej Ruszczyński

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  • Authors

    Alexander Shapiro, Georgia Institute of Technology
    Alexander Shapiro is a Professor in the School of Industrial and Systems Engineering at Georgia Institute of Technology. He has published more than 100 articles in peer-reviewed journals and is the co-author of several books.

    Darinka Dentcheva, Stevens Institute of Technology, New Jersey
    Darinka Dentcheva is a Professor of Mathematics at Stevens Institute of Technology. She works in the areas of decisions under uncertainty, convex analysis, and stability of optimization problems.

    Andrzej Ruszczyński, Rutgers University, New Jersey
    Andrzej Ruszczyński is a Professor in the Department of Operations Research at Rutgers University. His research is devoted to the theory and methods of optimization under uncertainty and risk.

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