Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
418103 | Computational Statistics & Data Analysis | 2007 | 13 Pages |
Abstract
A computational approach for solving regularized total least squares problems via a sequence of quadratic eigenvalue problems has recently been proposed. Taking advantage of a variational characterization of real eigenvalues of nonlinear eigenproblems the existence of a real right-most eigenvalue for each quadratic eigenvalue problem in the sequence is proven. For large problems the approach is improved considerably utilizing information from the previous quadratic problems and early updates in a nonlinear Arnoldi method.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
J. Lampe, H. Voss,