Abstract
Given a square matrix A, a decomposition A = QR, where Q is unitary and R is upper triangular, is said to be the QR decomposition of A. In contrast to the LU decomposition, one does not fear a growth of entries in the case of the QR decomposition. (Why?)
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
W. Hoffmann. Iterative algorithms for Gram-Schmidt orthogonalization. Computing 41: 335–348 (1989).
A. Bjorck and C. C. Paige. Loss and recapture of orthogonality in the modified Gram-Schmidt algorithm. SIAM J. Matrix Anal. Appl. 13(1): 176–190 (1992).
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1997 Springer Science+Business Media New York
About this chapter
Cite this chapter
Tyrtyshnikov, E.E. (1997). Lecture 8. In: A Brief Introduction to Numerical Analysis. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-0-8176-8136-4_8
Download citation
DOI: https://doi.org/10.1007/978-0-8176-8136-4_8
Publisher Name: Birkhäuser, Boston, MA
Print ISBN: 978-1-4612-6413-2
Online ISBN: 978-0-8176-8136-4
eBook Packages: Springer Book Archive
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.