SDDP for multistage stochastic linear programs based on spectral risk measures
Please always quote using this URN:urn:nbn:de:0296-matheon-11391
- We consider risk-averse formulations of multistage stochastic linear programs. For these formulations, based on convex combinations of spectral risk measures, risk-averse dynamic programming equations can be written. As a result, the Stochastic Dual Dynamic Programming (SDDP) algorithm can be used to obtain approximations of the corresponding risk-averse recourse functions. This allows us to define a risk-averse nonanticipative feasible policy for thestochastic linear program. Formulas for the cuts that approximate the recourse functions are given.
Author: | Vincent Guigues, Werner Roemisch |
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URN: | urn:nbn:de:0296-matheon-11391 |
Referee: | Fredi Tröltzsch |
Document Type: | Preprint, Research Center Matheon |
Language: | English |
Date of first Publication: | 2012/06/06 |
Release Date: | 2012/06/06 |
Tag: | decomposition; multistage; risk measure; stochastic programming |
Institute: | Humboldt-Universität zu Berlin |
MSC-Classification: | 00-XX GENERAL / 00-02 Research exposition (monographs, survey articles) |
Preprint Number: | 963 |