Lectures on Stochastic Programming: Modeling and Theory (MPS-SIAM Series on Optimization) - Softcover
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.
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Book Description:
This book focuses on optimization problems involving uncertain parameters and covers the theoretical foundations and recent advances in areas where stochastic models are available. It will be useful to researchers and graduate students working on theory and applications of optimization.
About the Author:
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.
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- PublisherSociety for Industrial and Applied Mathematics
- Publication date2009
- ISBN 10 089871687X
- ISBN 13 9780898716870
- BindingPaperback
- Number of pages450
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