Uncertainty Feature Optimization: an implicit paradigm for problems with noisy data

Optimization problems with noisy data solved using stochastic programming or robust optimization approaches require the explicit characterization of an uncertainty set U that models the nature of the noise. Such approaches depend on the modeling of the uncertainty set and suffer from an erroneous estimation of the noise.


Publié dans:
Networks, 57, 3, 270-284
Année
2011
ISSN:
0028-3045
Mots-clefs:
Laboratoires:




 Notice créée le 2010-09-30, modifiée le 2018-12-03


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