Dynamic generation of scenario trees

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