|edoc-Server der Humboldt-Universität zu Berlin|
Christian Küchler, Mathematik|
Stefan Vigerske, Mathematik
|Title:||Numerical Evaluation of Approximation Methods in Stochastic Programming|
|Date of Acceptance:||02.07.2008|
Stochastic Programming E-Print Series |
|Editors:||Julie L. Higle; Werner Römisch; Surrajeet Sen|
|Complete Preprint:||pdf (urn:nbn:de:kobv:11-10090091)|
|Keywords (eng):||scenario tree, multistage stochastic programming, out-of-sample evaluation|
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|We study an approach for the evaluation of approximation and solution methods for multistage linear stochastic programs by measuring the performance of the obtained solutions on a set of out-of-sample scenarios. The main point of the approach is to restore the feasibility of solutions to an approximated problem along the out-of-sample scenarios. For this purpose, we consider and compare different feasibility and optimality based projection methods. With this at hand, we study the quality of solutions to different test models based on classical as well as recombining scenario trees.|
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