## Abstract Condition numbers play an important role in numerical analysis. Classical condition numbers are normwise: they measure the size of both input perturbations and output errors using norms. In this paper, we give explicit, computable expressions depending on the data, for the normwise cond
On level-2 condition number for the weighted Moore–Penrose inverse
✍ Scribed by Lijing Lin; Tzon-Tzer Lu; Yimin Wei
- Publisher
- Elsevier Science
- Year
- 2008
- Tongue
- English
- Weight
- 277 KB
- Volume
- 55
- Category
- Article
- ISSN
- 0898-1221
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✦ Synopsis
In this paper, we present characterizations for the level-2 condition number of the weighted Moore-Penrose inverse, i.e.,
where cond M N (A) is the condition number of the weighted Moore-Penrose inverse of a rectangular matrix and cond [2] M N (A) is the level-2 condition number of this problem. This paper extends the result by Cucker, Diao and Wei [F. Cucker, H. Diao, Y. Wei, On the level-2 condition number for Moore-Penrose inversion, 2005, Unpublished report] and improves the results by Wei and Wang [Y. Wei, D. Wang, Condition numbers and perturbation of weighted Moore-Penrose inverse and weighted linear least squares problem, Appl. Math. Comput. 145 (2003) 45-58].
📜 SIMILAR VOLUMES
An iterative algorithm for estimating the Moore-Penrose generalized inverse is developed. The main motive for the construction of the algorithm is simultaneous usage of Penrose equations ( 2) and ( 4). Convergence properties of the introduced method as well as their first-order and second-order erro