You are here: MIMS > EPrints
MIMS EPrints

## 2008.70: Analysis of the Cholesky Method with Iterative Refinement for Solving the Symmetric Definite Generalized Eigenproblem

2008.70: Philip I. Davies, Nicholas J. Higham and Françoise Tisseur (2001) Analysis of the Cholesky Method with Iterative Refinement for Solving the Symmetric Definite Generalized Eigenproblem. SIAM Journal on Matrix Analysis and Applications, 23 (2). pp. 472-493. ISSN 0895-4798

Full text available as:

 PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader235 Kb

## Abstract

A standard method for solving the symmetric definite generalized eigenvalue problem $Ax = \lambda Bx$, where $A$ is symmetric and $B$ is symmetric positive definite, is to compute a Cholesky factorization $B = LL^T$ (optionally with complete pivoting) and solve the equivalent standard symmetric eigenvalue problem $C y = \l y$ where $C = L^{-1} A L^{-T}$. Provided that a stable eigensolver is used, standard error analysis says that the computed eigenvalues are exact for $A+\dA$ and $B+\dB$ with $\max( \normt{\dA}/\normt{A}, \normt{\dB}/\normt{B} )$ bounded by a multiple of $\kappa_2(B)u$, where $u$ is the unit roundoff. We take the Jacobi method as the eigensolver and give a detailed error analysis that yields backward error bounds potentially much smaller than $\kappa_2(B)u$. To show the practical utility of our bounds we describe a vibration problem from structural engineering in which $B$ is ill conditioned yet the error bounds are small. We show how, in cases of instability, iterative refinement based on Newton's method can be used to produce eigenpairs with small backward errors. Our analysis and experiments also give insight into the popular Cholesky--QR method, in which the QR method is used as the eigensolver. We argue that it is desirable to augment current implementations of this method with pivoting in the Cholesky factorization.

Item Type: Article symmetric definite generalized eigenvalue problem, Cholesky method, Cholesky factorization with complete pivoting, Jacobi method, backward error analysis, rounding error analysis, iterative refinement, Newton's method, LAPACK, MATLAB. MSC 2000 > 15 Linear and multilinear algebra; matrix theoryMSC 2000 > 65 Numerical analysis 2008.70 Nick Higham 01 July 2008