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Based on recent results on randomized min-max convex problems, we present a technique for building probability boxes that envelop the probability distribution of the costs incurred by a sample-based solution. The proposed technique is data-based in that the probability boxes are built based on the same data that are used to obtain the solution. The construction is distribution-free, and no specific...
In this paper, we present a theoretical result that applies to convex optimization problems in the presence of an uncertain stochastic parameter. We consider the min-max sample-based solution, i.e. the min-max solution computed over a finite sample of instances of the uncertain stochastic parameter, and the costs incurred by this solution in correspondence of the sampled parameter instances. Our goal...
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