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Vincent Guigues (vguiguesimpa.br) Abstract: We consider interstage dependent stochastic linear programs where both the random righthand side and the model of the underlying stochastic process have a special structure. Namely, for stage $t$, the righthand side of the equality constraints (resp. the inequality constraints) is an affine function (resp. a given function $b_t$) of the process value for this stage. As for $m$th component of the process at stage $t$, it depends on previous values of the process through a function $h_{t m}$. For this type of problem, to obtain an approximate policy under some assumptions for functions $b_t$ and $h_{t m}$, we detail a stochastic dual dynamic programming algorithm. Our analysis includes some enhancements of this algorithm such as the definition of a state vector of minimal size, the computation of feasibility cuts without the assumption of relatively complete recourse, as well as efficient formulas for sharing cuts between nodes of the same stage. The algorithm is given for both a nonrisk averse and a risk averse model. We finally provide preliminary results comparing the performances of the recourse functions corresponding to these two models for a reallife application. Keywords: Stochastic programming; Risk averse optimization; Decomposition algorithms; Interstage dependency; Monte Carlo sampling Category 1: Stochastic Programming Citation: Download: [PDF] Entry Submitted: 03/18/2011 Modify/Update this entry  
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