Abstract
If \( f(x) = \tfrac{1} {2}(Ax,x) - \operatorname{Re} (b,x), A = A^* \in \mathbb{C}^{n \times n} \), then the boundedness of f from below is equivalent to the nonnegative definiteness of A (prove this). Let us assume that A > 0. In this case, a linear system Ax = b has a unique solution z, and, for any x,
z is the single minimum point for f (x). ⇒ A minimization method for f can equally serve as a method of solving a linear system with the Hermitian positively definite coefficient matrix.
Preview
Unable to display preview. Download preview PDF.
References
A.W. Chou. On the optimality of Krylov information. J. of Complexity 3: 26–40 (1987).
G. I. Marchuk and Yu. A. Kuznetsov. Iterative methods and quadratic functional. Methods of Numerical Mathematics. Novosibirsk, 1975, pp. 4–143.
Y. Saad and M.H. Schultz. GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems. SIAM J. Scientific and Stat. Comp. 7: 856–869 (1986).
I first heard about this striking relation from S. A. Goreinov.
L. Zhou and H. F. Walker. Residual smoothing techniques for iterative methods. SIAM 3. on Sci. Comput. 15(2): 297–312 (1994).
R. W. Freund and N. M. Nachtigal. QMR: a quasi-minimal residual method for non-Hermitian linear systems. Numer. Math. 60: 315–339 (1991).
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 19. In: A Brief Introduction to Numerical Analysis. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-0-8176-8136-4_19
Download citation
DOI: https://doi.org/10.1007/978-0-8176-8136-4_19
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.