Policy with guaranteed risk-adjusted performance for multistage stochastic linear problems

Lucas Merabet (), Bernardo Freitas Paulo Costa () and Vincent Leclere ()
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Lucas Merabet: METRON
Bernardo Freitas Paulo Costa: Getulio Vargas Foundation
Vincent Leclere: Ecole des Ponts

Computational Management Science, 2024, vol. 21, issue 2, No 6, 25 pages

Abstract: Abstract 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.

Keywords: Duality; Inner approximation; SDDP; Primal-dual methods (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10287-024-00524-z

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