Abstract
We propose a generic method for obtaining quickly good upper bounds on the minimal value of a multistage stochastic program. The method is based on the simulation of a feasible decision policy, synthesized by a strategy relying on any scenario tree approximation from stochastic programming and on supervised learning techniques from machine learning. © Springer-Verlag 2009.
Cite
CITATION STYLE
Defourny, B., Ernst, D., & Wehenkel, L. (2009). Bounds for multistage stochastic programs using supervised learning strategies. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5792 LNCS, pp. 61–73). https://doi.org/10.1007/978-3-642-04944-6_6
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