Volume 53, pp. 541-561, 2020.

Krylov type methods for linear systems exploiting properties of the quadratic numerical range

Andreas Frommer, Brigit Jacob, Kartsen Kahl, Christian Wyss, and Ian Zwaan

Abstract

The quadratic numerical range $W^2(A)$ is a subset of the standard numerical range of a linear operator, which still contains its spectrum. It arises naturally in operators that have a $2\times 2$ block structure, and it consists of at most two connected components, none of which necessarily convex. The quadratic numerical range can thus reveal spectral gaps, and it can in particular indicate that the spectrum of an operator is bounded away from $0$. We exploit this property in the finite-dimensional setting to derive Krylov subspace-type methods to solve the system $Ax=b$, in which the iterates arise as solutions of low-dimensional models of the operator whose quadratic numerical range is contained in $W^2(A)$. This implies that the iterates are always well-defined and that, as opposed to standard FOM, large variations in the approximation quality of consecutive iterates are avoided, although $0$ lies within the convex hull of the spectrum. We also consider GMRES variants that are obtained in a similar spirit. We derive theoretical results on basic properties of these methods, review methods on how to compute the required bases in a stable manner, and present results of several numerical experiments illustrating improvements over standard FOM and GMRES.

Full Text (PDF) [386 KB], BibTeX , DOI: 10.1553/etna_vol53s541

Key words

quadratic numerical range, full orthogonalization method (FOM), generalized minimal residual method (GMRES), linear systems, projection methods, two-level orthogonal Arnoldi method

AMS subject classifications

15A60, 47A12, 65F10, 65N55