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.


Published in:
Networks, 57, 3, 270-284
Year:
2011
ISSN:
0028-3045
Keywords:
Laboratories:




 Record created 2010-09-30, last modified 2018-03-17


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