Scenario Reduction Techniques in Stochastic Programming
(invited lecture for SAGA 2009)
Author: Werner Römisch
Affiliation:
Institut für Mathematik
Humboldt University, Berlin, Germany
Abstract.
Two and multistage stochastic programming problems appear as
mathematical models for optimization problems under stochastic
uncertainty. Most computational approaches for solving such models
are based on approximating the underlying probability distribution
by a probability measure with finite support. Since the computational
complexity for solving stochastic programs gets worse with increasing
the number of atoms (or scenarios), it is sometimes necessary to reduce
their number. Another motivation for scenario reduction goes back to
the need of generating scenario trees to model decision processes
based on recursive observation and decision. Scenario reduction techniques
often require fast heuristics for solving combinatorial subproblems.
Available techniques are reviewed and open problems are discussed.
©Copyright 2009 Author
