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This course will focus on the optimization problems wit uncertain parameters, specifically stochastic programming models. The course will start with the modeling aspect of stochastic programs and their properties. Then we will study the approximation, bounding techniques, and related statistical properties for stochastic optimization. Different decomposition methods will be discussed for large-scale stochastic convex programs and stochastic mixed-integer programs. To take this course, knowledge of probability theory and graduate-level optimization is required.
This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems.
Third Edition covers optimization problems involving uncertain parameters for which stochastic models are available. These problems occur in almost all areas of science and engineering. This substantial revision of the previous edition presents a modern theory of stochastic programming, including expanded coverage of sample complexity, risk measures, and distributionally robust optimization.
