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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.

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Metadaten
Author:Vincent Guigues, Werner Roemisch
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
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