On mixed and componentwise condition numbers for moore-penrose inverse and linear least squares problems
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
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Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 947-963 |
| Journal / Publication | Mathematics of Computation |
| Volume | 76 |
| Issue number | 258 |
| Publication status | Published - Apr 2007 |
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Abstract
Classical condition numbers are normwise: they measure the size of both input perturbations and output errors using some norms. To take into account the relative of each data component, and, in particular, a possible data sparseness, componentwise condition numbers have been increasingly considered. These are mostly of two kinds: mixed and componentwise. In this paper, we give explicit expressions, computable from the data, for the mixed and componentwise condition numbers for the computation of the Moore-Penrose inverse as well as for the computation of solutions and residues of linear least squares problems. In both cases the data matrices have full column (row) rank. ©2006 American Mathematical Society.
Research Area(s)
- Componentwise analysis, Condition numbers, Least squares
Citation Format(s)
On mixed and componentwise condition numbers for moore-penrose inverse and linear least squares problems. / Cucker, Felipe; Diao, Huaian; Wei, Yimin.
In: Mathematics of Computation, Vol. 76, No. 258, 04.2007, p. 947-963.
In: Mathematics of Computation, Vol. 76, No. 258, 04.2007, p. 947-963.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
