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An accessible and rigorous presentation of contemporary models and ideas of stochastic programming, this book focuses on optimization problems involving uncertain parameters for which stochastic models are available. Since these problems occur in vast, diverse areas of science and engineering, there is much interest in rigorous ways of formulating, analyzing, and solving them.
This substantially revised edition
– presents a modern theory of stochastic programming, including expanded and detailed coverage of sample complexity, risk measures, and distributionally robust optimization
– adds two new chapters that provide readers with a solid understanding of emerging topics
– updates Chapter 6 to now include a detailed discussion of the interchangeability principle for risk measures and
– presents new material on formulation and numerical approaches to solving periodical multistage stochastic programs.
Lectures on Stochastic Programming: Modeling and Theory, Third Edition is written for researchers and graduate students working on theory and applications of optimization, with the hope that it will encourage them to apply stochastic programming models and undertake further studies of this fascinating and rapidly developing area.
Table of contents:
Stochastic Programming Models
Two-Stage Problems
Multistage Problems
Optimization Models with Probabilistic Constraints
Statistical Inference
Risk Averse Optimization
Distributionally Robust Stochastic Programming
Computational Methods
Background Material
Bibliographical Remarks