Dynamic Generation of Scenario Trees

We present new algorithms for the dynamic generation of scenario trees for multistage stochastic optimization. The different methods described are based on random vectors, which are drawn from conditional distributions given the past and on sample trajectories. The structure of the tree is not determined beforehand, but dynamically adapted to meet a distance criterion, which insures the quality of the approximation. The criterion is built on transportation theory, which is extended to stochastic processes.



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