Stochastic integer programming
Limit theorems and confidence intervals
We consider empirical approximations of two-stage stochastic mixed-integer programs and derive central limit theorems for the objectives and optimal values. The limit theorems are based on empirical process theory and the functional delta method. We also show how these limit theorems can be used to derive confidence intervals for optimal values via a certain modification of the bootstrapping method.
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