TIKHONOV REGULARIZATION AND TOTAL LEAST SQUARES
TIKHONOV REGULARIZATION AND TOTAL LEAST SQUARES
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Date
1998-10-15
Authors
Golub, Gene H.
Hansen, Per Christian
O'Leary, Dianne P.
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Abstract
Discretizations of inverse problems lead to systems of linear equations
with a highly ill-conditioned coefficient matrix, and in order to
compute stable solutions to these systems it is necessary to apply
regularization methods.
We show how Tikhonov's regularization method, which in its original
formulation involves a least squares problem, can be recast in a
total least squares formulation, suited for problems in which both the
coefficient matrix and the right-hand side are known only approximately.
We analyze the regularizing properties of this method and demonstrate by a
numerical example that in certain cases with large perturbations, the new
method is superior to standard regularization methods.
(Also cross-referenced as UMIACS-TR-97-65)