Berthold, HolgerHolgerBertholdHeitsch, H.H.HeitschHenrion, R.R.HenrionSchwientek, JanJanSchwientek2022-05-062022-05-062022https://publica.fraunhofer.de/handle/publica/41540910.1007/s00186-021-00764-8We present an adaptive grid refinement algorithm to solve probabilistic optimization problems with infinitely many random constraints. Using a bilevel approach, we iteratively aggregate inequalities that provide most information not in a geometric but in a probabilistic sense. This conceptual idea, for which a convergence proof is provided, is then adapted to an implementable algorithm. The efficiency of our approach when compared to naive methods based on uniform grid refinement is illustrated for a numerical test example as well as for a water reservoir problem with joint probabilistic filling level constraints.en003300006519journal article