  We introduce DASC, a decomposition method akin to Stochastic Dual Dynamic
Programming (SDDP) which solves some multistage stochastic optimization
problems having strongly convex cost functions. Similarly to SDDP, DASC
approximates cost-to-go functions by a maximum of lower bounding functions
called cuts. However, contrary to SDDP, the cuts computed with DASC are
quadratic functions. We also prove the convergence of DASC.
