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• W3094694511 startingPage "755" @default.
• W3094694511 abstract "We propose a new algorithm for solving multistage stochastic mixed integer linear programming (MILP) problems with complete continuous recourse. In a similar way to cutting plane methods, we construct nonlinear Lipschitz cuts to build lower approximations for the non-convex cost-to-go functions. An example of such a class of cuts are those derived using Augmented Lagrangian Duality for MILPs. The family of Lipschitz cuts we use is MILP representable, so that the introduction of these cuts does not change the class of the original stochastic optimization problem. We illustrate the application of this algorithm on two case studies, comparing our approach with the convex relaxation of the problems, for which we can apply SDDP, and for a discretized approximation, applying SDDiP." @default.
• W3094694511 created "2020-11-09" @default.
• W3094694511 creator A5025814071 @default.
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• W3094694511 date "2020-10-28" @default.
• W3094694511 modified "2023-10-12" @default.
• W3094694511 title "Stochastic Lipschitz dynamic programming" @default.
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• W3094694511 doi "https://doi.org/10.1007/s10107-020-01569-z" @default.
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