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.
Author: | Werner Römisch |
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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 |