  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.
