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Vincent Guigues(vguiguesimpa.br) Abstract: We consider riskaverse formulations of multistage stochastic linear programs. For these formulations, based on convex combinations of spectral risk measures, riskaverse 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 riskaverse recourse functions. This allows us to define a riskaverse nonanticipative feasible policy for the stochastic linear program. Formulas for the cuts that approximate the recourse functions are given. In particular, we show that some of the cut coefficients have analytic formulas. Keywords: Spectral risk measure; Stochastic programming; Riskaverse optimization; Decomposition algorithms; MonteCarlo sampling Category 1: Stochastic Programming Citation: Download: [PDF] Entry Submitted: 08/21/2012 Modify/Update this entry  
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