Policy with guaranteed risk-adjusted performance for multistage stochastic linear problems
Published in Computational Management Science, 2024
Risk-averse multistage problems and their applications are gaining interest in various fields of applications. Under convexity assumptions, the resolution of these problems can be done with trajectory following dynamic programming algorithms like Stochastic Dual Dynamic Programming (SDDP) to access a deterministic lower bound, and dual SDDP for deterministic upper bounds. In this paper, we leverage the dual SDDP algorithm to compute a policy with guaranteed risk-adjusted performance for multistage stochastic linear problems.
Recommended citation: L Merabet, BFP da Costa, V Leclere. "Policy with guaranteed risk-adjusted performance for multistage stochastic linear problems". Computational Management Science 21 (2024), no. 2, p. 43.
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