Loading...
research article
A Regularized Robust Design Criterion for Uncertain Data
This paper formulates and solves a robust criterion for least-squares designs in the presence of uncertain data. Compared with earlier studies, the proposed criterion incorporates simultaneously both regularization and weighting and applies to a large class of uncertainties. The solution method is based on reducing a vector optimization problem to an equivalent scalar minimization problem of a provably unimodal cost function, thus achieving considerable reduction in computational complexity.
Type
research article
Authors
Publication date
2002
Published in
Volume
23
Issue
4
Start page
1120
End page
1142
Peer reviewed
REVIEWED
EPFL units
Available on Infoscience
December 19, 2017
Use this identifier to reference this record