Articles liés à Lectures on Stochastic Programming: Modeling and Theory
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Synopsis
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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À propos des auteurs
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 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 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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