On the algorithmic solution of optimization problems subject to probabilistic/robust (probust) constraints

Abstract We 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 geometric but sense. This conceptual idea, for which convergence proof is provided, then adapted implementable algorithm. The efficiency of our approach when compared naive methods based on uniform illustrated numerical test example as well water reservoir problem joint filling level