On the perturbation of the Moore–Penrose inverse of a matrix

Xuefeng Xu

Applied Mathematics and Computation, 2020, vol. 374, issue C

Abstract: The Moore–Penrose inverse of a matrix has been extensively investigated and widely applied in many fields over the past decades. One reason for the interest is that the Moore–Penrose inverse can succinctly express some important geometric constructions in finite-dimensional spaces, such as the orthogonal projection onto a subspace and the linear least squares problem. In this paper, we establish new perturbation bounds for the Moore–Penrose inverse under the Frobenius norm, some of which are sharper than the existing ones.

Keywords: Moore–Penrose inverse; Perturbation; Singular value decomposition (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300319309129
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:374:y:2020:i:c:s0096300319309129

DOI: 10.1016/j.amc.2019.124920

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().