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Regularized total least squares based on the nonlinear Arnoldi method
Author(s) -
Lampe Jörg,
Voss Heinrich
Publication year - 2007
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.200700665
Subject(s) - least squares function approximation , eigenvalues and eigenvectors , quadratic equation , mathematics , non linear least squares , total least squares , nonlinear system , mathematical optimization , arnoldi iteration , sequence (biology) , algorithm , preconditioner , iterative method , estimation theory , statistics , chemistry , physics , biochemistry , geometry , quantum mechanics , estimator , singular value decomposition
Abstract A computational approach for solving regularized total least squares problems via a sequence of quadratic eigenvalue problems has recently been introduced by Sima, Van Huffel, and Golub. Combining this approach with the nonlinear Arnoldi method and reusing information from all previous quadratic eigenvalue problems, together with an early update of search spaces we arrive at a very efficient method for large regularized total least squares problems. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)