Risk-averse multistage stochastic programs appear in multiple areas and are challenging to solve. Stochastic Dual Dynamic Programming (SDDP) is a well-known tool to address such problems under time-independence assumptions. We show how to derive a dual formulation for these problems and apply an SDDP algorithm, leading to converging and deterministic upper bounds for risk-averse problems.
Freitas Paulo da Costa, B., Leclère, V. Dual SDDP for risk-averse multistage stochastic programs. Operations Research Letters 51 (3), 332-337, https://doi.org/10.1016/j.orl.2023.04.001