@article {15823,
	title = {Tikhonov Regularization and Total Least Squares},
	journal = {SIAM Journal on Matrix Analysis and Applications},
	volume = {21},
	year = {1999},
	month = {1999///},
	pages = {185 - 194},
	abstract = {Discretizations of inverse problems lead to systems of linear equations with a highly ill-conditioned coefficient matrix, and in order to computestable solutions to these systems it is necessary to apply regularization methods. We show how Tikhonov{\textquoteright}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.},
	keywords = {bidiagonalization, discrete ill-posed problems, regularization, total least squares},
	doi = {10.1137/S0895479897326432},
	url = {http://link.aip.org/link/?SML/21/185/1},
	author = {Golub, Gene H. and Hansen,Per Christian and O{\textquoteright}Leary, Dianne P.}
}
