Asymptotic Consistency for Nonconvex Risk-Averse Stochastic Optimization with Infinite-Dimensional Decision Spaces
Johannes Milz,
Thomas M. Surowiec
Abstract:Optimal values and solutions of empirical approximations of stochastic optimization problems can be viewed as statistical estimators of their true values. From this perspective, it is important to understand the asymptotic behavior of these estimators as the sample size goes to infinity. This area of study has a long tradition in stochastic programming. However, the literature is lacking consistency analysis for problems in which the decision variables are taken from an infinite-dimensional space, which arise … Show more
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