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Zbl 0891.65039
El Ghaoui, Laurent; Lebret, Hervé
Robust solutions to least-squares problems with uncertain data. (English)
[J] SIAM J. Matrix Anal. Appl. 18, No.4, 1035-1064 (1997). ISSN 0895-4798; ISSN 1095-7162/e

Least-squares problems are studied where the coefficient matrices are unknown but bounded. The worse-case residual error is minimized. The exact value of the optimal worst-case residuals is computed using convex, second-order cone programming or semidefinite programming. A consequence is that the minimizing vector can be computed in polynomial time.
[H.Hollatz (Magdeburg)]

MSC 2000:: *65F20 Overdetermined systems (numerical linear algebra)
65Y20 Complexity and performance of numerical algorithms

Keywords: least-squares problems; uncertainty; robustness; semidefinite programming; ill-conditioned problem; regularization; robust interpolation

Cited in: Zbl 1091.93524

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