On the Glivenko–Cantelli problem in stochastic programming: Linear recourse and extensions

On the Glivenko–Cantelli problem in stochastic programming: Linear recourse and extensions

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Article ID: iaor2004774
Country: United States
Volume: 23
Issue: 1
Start Page Number: 204
End Page Number: 220
Publication Date: Feb 1998
Journal: Mathematics of Operations Research
Authors: , ,
Abstract:

Integrals of optimal values of random optimization problems depending on a finite dimensional parameter are approximated by using empirical distributions instead of the original measure. Under fairly broad conditions, it is proved that uniform convergence of empirical approximations of the right hand sides of the constraints implies uniform convergence of the optimal values in the linear and convex case.

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