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Scenario generation in stochastic programming

Please always quote using this URN:urn:nbn:de:0296-matheon-6657
  • Stability-based methods for scenario generation in stochastic programming are reviewed. In particular, we briefly discuss Monte Carlo sampling, Quasi-Monte Carlo methods, quadrature rules based on sparse grids and optimal quantization. In addition, we provide some convergence results based on recent developments in multivariate integration. The method of optimal scenario reduction and techniques for scenario trees generation are also reviewed.

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Metadaten
Author:Werner Römisch
URN:urn:nbn:de:0296-matheon-6657
Referee:Fredi Tröltzsch
Document Type:Preprint, Research Center Matheon
Language:English
Date of first Publication:2009/10/19
Release Date:2009/10/19
Institute:Humboldt-Universität zu Berlin
Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS)
Preprint Number:668
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