Stochastic integer programming

Andreas Eichhorn; Werner Römisch

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SPEPS Preprint

Author(s):	Andreas Eichhorn, Humboldt-University Berlin Werner Römisch, Humboldt-University Berlin
Title:	Stochastic integer programming – Limit theorems and confidence intervals
Date of Acceptance:	25.02.2005
Submission Date:	07.02.2005
Series Title:	Stochastic Programming E-Print Series (SPEPS)
Editors:	Julie L. Higle; Werner Römisch; Surrajeet Sen
Complete Preprint:	pdf (urn:nbn:de:kobv:11-10057192)
Keywords (eng):	stability, Stochastic programming, mixed-integer optimiziation, Hadamard directional differentiability, Donsker class, delta method, bootstap
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Abstract (eng):

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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