Scenario reduction in stochastic programming |
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Authors: | J Dupačová N Gröwe-Kuska W Römisch |
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Institution: | (1) Charles University Prague, Department of Probability and Mathematical Statistics, 18675 Prague 8, Czech Republic, e-mail: dupacova@karlin.mff.cuni.cz, CZ;(2) Humboldt-University Berlin, Institute of Mathematics, 10099 Berlin, Germany, e-mail: nicole@mathematik.hu-berlin.de, DE;(3) Humboldt-University Berlin, Institute of Mathematics, 10099 Berlin, Germany, e-mail: romisch@mathematik.hu-berlin.de, DE |
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Abstract: | Given a convex stochastic programming problem with a discrete initial probability distribution, the problem of optimal scenario
reduction is stated as follows: Determine a scenario subset of prescribed cardinality and a probability measure based on this
set that is the closest to the initial distribution in terms of a natural (or canonical) probability metric. Arguments from
stability analysis indicate that Fortet-Mourier type probability metrics may serve as such canonical metrics. Efficient algorithms
are developed that determine optimal reduced measures approximately. Numerical experience is reported for reductions of electrical
load scenario trees for power management under uncertainty. For instance, it turns out that after 50% reduction of the scenario
tree the optimal reduced tree still has about 90% relative accuracy.
Received: July 2000 / Accepted: May 2002 Published online: February 14, 2003
Key words. stochastic programming – quantitative stability – Fortet-Mourier metrics – scenario reduction – transportation problem –
electrical load scenario tree
Mathematics Subject Classification (1991): 90C15, 90C31 |
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Keywords: | |
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