We provide a tutorial-type review on stochastic dual dynamic programming (SDDP), as one of the state-of-the-art solution methods for large-scale multistage stochastic programs.
Since introduced about 30 years ago for solving large-scale multistage stochastic linear programming problems in energy planning, SDDP has been applied to practical problems from several fields and is enriched by various improvements and enhancements to broader problem classes. We begin with a detailed introduction to SDDP, with special focus on its motivation, its complexity and required assumptions. Then, we present and discuss in depth the existing enhancements as well as current research trends, allowing for an alleviation of those assumptions.
Citation
Preprint, Karlsruhe Institute of Technology, 2024
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