We study robust stochastic optimization problems in the quasi-sure setting in discrete-time. The strategies are restricted to those taking values in a discrete set. The optimization problems under consideration are not concave. We provide conditions under which a maximizer exists. The class of problems covered by our robust optimization problem includes optimal stopping and semi-static trading under Knightian uncertainty. Moreover, it also can be considered as an approximation of general nonconcave robust stochastic optimization problems.
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