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Stochastic programming is one of the fundamental
approaches that can be used to model
decision-making under uncertainty. It is concerned with the
mathematical programming problems, where the uncertain
problem parameters are represented by random variables,
and it extends deterministic optimization by explicitly
accounting for the uncertainty already in the modeling
age. This course will provide a broad overview
of the main themes and methods of the subject.
This course covers various optimization models
(chance-constrained optimization, two-stage
stochastic programming models, optimization with risk
measures, etc.), as well as their mathematical
programming-based solution methods and applications to
practical problems. Since stochastic programs are
computationally challenging, there is a particular
emphasis in this course on algorithmic tools (especially,
on decomposition-based algorithms) for
solving large-scale instances.
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