|edoc-Server der Humboldt-Universität zu Berlin|
Vincent Guiges, IMPA|
Werner Römisch, Humboldt-University
|Title:||SDDP for multistage stochastic linear programs based on spectral risk measures|
|Date of Acceptance:||09.04.2012|
Stochastic Programming E-Print Series |
|Editors:||Julie L. Higle; Werner Römisch; Surrajeet Sen|
|Complete Preprint:||pdf (urn:nbn:de:kobv:11-100201028)|
|Keywords (eng):||stochastic programming, Monte-Carlo sampling, spectral risk measure, risk-averse optimization, decomposition algorithms|
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|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 the stochastic linear program. Formulas for the cuts that approximate the recourse functions are given.|
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