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@incollection{oai:export,
author = {Vincent Guiges and Werner Römisch},
title = {SDDP for multistage stochastic linear programs based on spectral risk measures},
series = {Stochastic Programming E-Print Series},
year = {2012},
publisher = {Institut für Mathematik},
editor =
{
Julie L.
Higle and Werner
Römisch and Surrajeet
Sen
},
WernerRömischSurrajeetSenabstract = {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. },
note = {published; Operations Research Letters; 2012; 40}
url = { \url{http://edoc.hu-berlin.de/docviews/abstract.php?id=39285} },
url = { \url{urn:nbn:de:kobv:11-100201028} },
number = {
4},
timestamp = {2017-06-22T18:32:41Z}
}