Scenario reduction in stochastic programming with respect to discrepancy distances
Computational Optimization and Applications
Scenario reduction techniques in stochastic programming
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Bounds for multistage stochastic programs using supervised learning strategies
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Computers and Industrial Engineering
Scenario tree generation approaches using K-means and LP moment matching methods
Journal of Computational and Applied Mathematics
A Distance For Multistage Stochastic Optimization Models
SIAM Journal on Optimization
Scenario Trees and Policy Selection for Multistage Stochastic Programming Using Machine Learning
INFORMS Journal on Computing
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An important issue for solving multistage stochastic programs consists in the approximate representation of the (multivariate) stochastic input process in the form of a scenario tree. In this paper, we develop (stability) theory-based heuristics for generating scenario trees out of an initial set of scenarios. They are based on forward or backward algorithms for tree generation consisting of recursive scenario reduction and bundling steps. Conditions are established implying closeness of optimal values of the original process and its tree approximation, respectively, by relying on a recent stability result in Heitsch, Römisch and Strugarek (SIAM J Optim 17:511–525, 2006) for multistage stochastic programs. Numerical experience is reported for constructing multivariate scenario trees in electricity portfolio management.