L Auslander, E Feig, et al.
Advances in Applied Mathematics
Inverse iteration is widely used to compute the eigenvectors of a matrix once accurate eigenvalues are known. We discuss various issues involved in any implementation of inverse iteration for real, symmetric matrices. Current implementations resort to reorthogonalization when eigenvalues agree to more than three digits relative to the norm. Such reorthogonalization can have unexpected consequences. Indeed, as we show in this paper, the implementations in EISPACK and LAPACK may fail. We illustrate with both theoretical and empirical failures.
L Auslander, E Feig, et al.
Advances in Applied Mathematics
Hans Becker, Frank Schmidt, et al.
Photomask and Next-Generation Lithography Mask Technology 2004
Ehud Altman, Kenneth R. Brown, et al.
PRX Quantum
R.A. Brualdi, A.J. Hoffman
Linear Algebra and Its Applications