Tikhonov regularization and total least squares

Gene H. Golub, Per Christian Hansen, and Dianne P. O'Leary.

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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 formu- lation 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 regu- larizing 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.

IMM Technical Report 15/97

Last modified April 21, 1997

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