|edoc-Server der Humboldt-Universität zu Berlin|
Steftcho P. Dokov, The University of Texas at Austin|
David P. Morton, The University of Texas at Austin
|Title:||Higher-Order Upper Bounds on the Expectation of a Convex Function|
|Date of Acceptance:||03.05.2002|
Stochastic Programming E-Print Series |
|Editors:||Julie L. Higle; Werner Römisch; Surrajeet Sen|
|Complete Preprint:||pdf (urn:nbn:de:kobv:11-10058406)|
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|We develop a decreasing sequence of upper bounds on the expectation of a convex function. The n-th term in the sequence uses moments and cross-moments of up to degree n from the underlying random vector. Our work has application to a class of two-stage stochastic programs with recourse. The objective function of such a model can defy computation when: (i) the underlying distribution is assumed to be known only through a limited number of moments or (ii) the function is computationally intractable, even though the distribution is known. A tractable approximating model arises by replacing the objective function by one of our bounding elements. We justify this approach by showing that as n grows, solutions of the order-n approximation solve the true stochastic program.|
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