Stochastic programming, complexity and risk analysis<BR>

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Alexander Shapiro<BR>
Georgia Tech<BR>

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Abstract: <BR>

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In this talk we discuss computational complexity and risk averse approaches to two and multistage stochastic
programming problems. We argue that two stage (say linear) stochastic programming problems can be solved with
a reasonable accuracy by Monte Carlo sampling techniques while there are indications that complexity of
multistage programs grows fast with increase of the number of stages.
We discuss an extension of coherent risk measures to a multistage setting and, in particular, dynamic
programming equations for such problems.