Lectures on Stochastic Programming: Modeling and Theory, 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:

Chapter 6 is updated and the interchangeability principle for risk measures is discussed in detail.
Two new chapters, 'Distributionally Robust Stochastic Programming' (DRSP) and 'Computational Methods' provide readers with a solid understanding of emerging topics.
Chapter 8 presents new material on formulation and numerical approaches to solving periodical multistage stochastic programs.



This book is written for researchers and graduate students working on theory and applications of optimization.