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.


Published in:
SIAM Journal on Matrix Analysis and Applications, 23, 4, 1120-1142
Year:
2002
Publisher:
Society for Industrial and Applied Mathematics
ISSN:
1095-7162
Laboratories:




 Record created 2017-12-19, last modified 2018-09-13


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