Optimization problems involving stochastic models occur in many areas of
of science and engineering. These lectures are aimed at presenting
theoretical foundations and recent advances in theory and applications
of the stochastic programming approach to such problems. In particular
we discuss modeling questions, computational complexity of solving
static and multistage stochastic problems, risk averse and
distributionally robust approaches and some popular numerical algorithms.
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