Hochreiter, Ronald. 2005. Scenario Optimization for Multi-Stage Stochastic Programming Problems. In Algorithms for Optimization with Incomplete Information, Hrsg. Susanne Albers and Rolf H. Möhring and Georg Ch. Pflug and Rüdiger Schultz, 61-63. Volume 05031 of Dagstuhl Seminar Proceedings: IBFI, Schloss Dagstuhl, Germany.

RIS

@INPROCEEDINGS{Hochreiter2005,
title = {Scenario Optimization for Multi-Stage Stochastic Programming Problems},
author = {Ronald Hochreiter},
publisher = {IBFI, Schloss Dagstuhl, Germany},
year = {2005},
address = {Volume 05031 of Dagstuhl Seminar Proceedings},
url = {http://drops.dagstuhl.de/volltexte/2005/61/pdf/05031.HochreiterRonald.ExtAbstract.61.pdf},
publisher = {IBFI, Schloss Dagstuhl, Germany},
volume = {05031},
language = {EN},
pages = {61-63},
series = {Algorithms for Optimization with Incomplete Information},
abstract = {The ¯eld of multi-stage stochastic programming provides a rich modelling framework to tackle a broad range of real-world decision problems. In order to numerically solve such programs - once they get reasonably large - the in¯nite-dimensional optimization problem has to be discretized. The stochastic optimization program generally consists of an optimization model and a stochastic model. In the multi-stage case the stochastic model is most commonly represented as a multi-variate stochastic process. The most common technique to calculate an useable discretization is to generate a scenario tree from the underlying sto- chastic process. Scenario tree generation is exampli¯ed by reviewing one speci¯c algorithm based on multi-dimensional facility location applying backward stagewise clustering.},
}