On the Glivenko-Cantelli Problem in Stochastic Programming: Linear Recourse and Extensions
AbstractIntegrals 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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Bibliographic InfoPaper provided by International Institute for Applied Systems Analysis in its series Working Papers with number wp96020.
Date of creation: Feb 1996
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- Lucchetti, R. & Salinetti, G. & Wets, R. J. B., 1994. "Uniform Convergence of Probability Measures: Topological Criteria," Journal of Multivariate Analysis, Elsevier, vol. 51(2), pages 252-264, November.
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