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SPEPS Preprint

Author(s): 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
Series Title: Stochastic Programming E-Print Series
(SPEPS)
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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Abstract (eng):
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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