This talk elaborates an approximation scheme for convex multistage stochastic programs (MSP) with expected value constraints. The considered problem class thus comprises models with integrated chance constraints and CVaR constraints. We develop two computationally tractable approximate problems that provide bounds on the (untractable) original problem, and we show that the gap between the bounds can be made small. The solutions of the approximate MSPs give rise to a feasible policy for the original MSP, and this policy's optimality gap is shown to be smaller than the difference of the bounds. Furthermore, we propose a threshold accepting algorithm that attempts to find the most accurate discretization among all discretizations of a given complexity. Our approach is illustrated on a portfolio optimization problem.
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