Local epi-continuity and local optimization
Mathematical Programming: Series A and B
On concepts of directional differentiability
Journal of Optimization Theory and Applications
Normalized convergence in stochastic optimization
Annals of Operations Research
Asymptotic theory for solutions in statistical estimation and stochastic programming
Mathematics of Operations Research
Asymptotic stochastic programs
Mathematics of Operations Research
Mathematical Programming: Series A and B
The Sample Average Approximation Method for Stochastic Discrete Optimization
SIAM Journal on Optimization
On the Rate of Convergence of Optimal Solutions of Monte Carlo Approximations of Stochastic Programs
SIAM Journal on Optimization
Rates of Convergence in Stochastic Programs with Complete Integer Recourse
SIAM Journal on Optimization
Quantitative Stability in Stochastic Programming: The Method of Probability Metrics
Mathematics of Operations Research
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We consider empirical approximations (sample average approximations) of two-stage stochastic mixed-integer linear 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 resampling methods (bootstrap, subsampling).