Generalized cross validation is a popular approach to determining the regularization
parameter in Tikhonov regularization. The regularization parameter is chosen by minimizing …

Tikhonov regularization is one of the most popular methods for computing an approximate
solution of linear discrete ill-posed problems with error-contaminated data. A regularization …

For linear inverse problems Y=A\mu+ξ, it is classical to recover the unknown signal μ by
iterative regularization methods (\widehatμ^(m),m=0,1,...) and halt at a data-dependent …

This work discusses four algorithms for the solution of linear discrete ill-posed problems with
several right-hand side vectors. These algorithms can be applied, for instance, to multi …

Discrete ill-posed problems arise in many areas of science and engineering. Their solutions,
if they exist, are very sensitive to perturbations in the data. Regularization aims to reduce this …

This paper is concerned with the solution of large-scale linear discrete ill-posed problems
with error-contaminated data. Tikhonov regularization is a popular approach to determine …

Truncated singular value decomposition is a popular method for solving linear discrete ill‐
posed problems with a small to moderately sized matrix A. Regularization is achieved by …

The generalized singular value decomposition (GSVD) of a pair of matrices expresses each
matrix as a product of an orthogonal, a diagonal, and a nonsingular matrix. The nonsingular …

The numerical solution of linear discrete ill-posed problems typically requires regularization,
ie, replacement of the available ill-conditioned problem by a nearby better conditioned one …

Large linear discrete ill-posed problems are commonly solved by first reducing them to small
size by application of a few steps of a Krylov subspace method, and then applying Tikhonov …