𝔖 Bobbio Scriptorium
✦   LIBER   ✦

The effects of inexact solvers in algorithms for symmetric eigenvalue problems

✍ Scribed by P. Smit; M.H.C. Paardekooper


Publisher
Elsevier Science
Year
1999
Tongue
English
Weight
797 KB
Volume
287
Category
Article
ISSN
0024-3795

No coin nor oath required. For personal study only.

✦ Synopsis


This paper analyses the effects of inaccurate linear solvers on the behaviour of inverse iteration and Rayleigh quotient iteration. We derive an expression for the worst-case perturbation of the convergence factor of the exact iteration, due to the inexact solution.

A necessary and sufficient condition on the approximate eigenvector for the improvement of the next iterate follows from that formula. Preceding this, several new inequalities describe the relation between the errors in the approximate eigenvector, the approximate eigenvalue and the corresponding residual.


πŸ“œ SIMILAR VOLUMES


An inexact Krylov–Schur algorithm for th
✍ Roden J.A. David; David S. Watkins πŸ“‚ Article πŸ“… 2008 πŸ› Elsevier Science 🌐 English βš– 173 KB

We present an efficient inexact implicitly restarted Arnoldi algorithm to find a few eigenpairs of large unitary matrices. The approximating Krylov spaces are built using short-term recurrences derived from Gragg's isometric Arnoldi process. The implicit restarts are done by the Krylov-Schur methodo

On the use of inexact subdomain solvers
✍ Jing Li; Olof B. Widlund πŸ“‚ Article πŸ“… 2007 πŸ› Elsevier Science 🌐 English βš– 322 KB

The standard BDDC (balancing domain decomposition by constraints) preconditioner is shown to be equivalent to a preconditioner built from a partially subassembled finite element model. This results in a system of linear algebraic equations which is much easier to solve in parallel than the fully ass

A Parallel QR Algorithm for the Symmetri
✍ L. Kaufman πŸ“‚ Article πŸ“… 1994 πŸ› Elsevier Science 🌐 English βš– 478 KB

The implicit QR algorithm is a serial iterative algorithm for determining all the eigenvalues of an \(n \times n\) symmetric tridiagonal matrix \(A\). About \(3 n\) iterations, each requiring the serial application of about \(n\) similarity planar transformations, are required to reduce \(A\) to dia