This textbook provides an introduction to convex duality for optimization problems in Banach
spaces integration theory and their application to stochastic programming problems in a
static or dynamic setting. It introduces and analyses the main algorithms for stochastic
programs while the theoretical aspects are carefully dealt with. The reader is shown how these
tools can be applied to various fields including approximation theory semidefinite and
second-order cone programming and linear decision rules. This textbook is recommended for
students engineers and researchers who are willing to take a rigorous approach to the
mathematics involved in the application of duality theory to optimization with uncertainty.
