Regularization by Truncated Total Least Squares
SIAM Journal on Scientific Computing
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Tikhonov Regularization and Total Least Squares
SIAM Journal on Matrix Analysis and Applications
Efficient Algorithms for Solution of Regularized Total Least Squares
SIAM Journal on Matrix Analysis and Applications
On the Solution of the Tikhonov Regularization of the Total Least Squares Problem
SIAM Journal on Optimization
SIAM Journal on Matrix Analysis and Applications
Overview of total least-squares methods
Signal Processing
On a quadratic eigenproblem occurring in regularized total least squares
Computational Statistics & Data Analysis
Regularized Total Least Squares: Computational Aspects and Error Bounds
SIAM Journal on Matrix Analysis and Applications
Accelerating the LSTRS Algorithm
SIAM Journal on Scientific Computing
Newton Methods for Nonlinear Problems: Affine Invariance and Adaptive Algorithms
Newton Methods for Nonlinear Problems: Affine Invariance and Adaptive Algorithms
Efficient determination of the hyperparameter in regularized total least squares problems
Applied Numerical Mathematics
| Hi-index | 7.29 |
The total least squares (TLS) method is a successful approach for linear problems when not only the right-hand side but the system matrix is also contaminated by some noise. For ill-posed TLS problems regularization is necessary to stabilize the computed solution. In this paper we present a new approach for computing an approximate solution of the Tikhonov-regularized large-scale total least-squares problem. An iterative method is proposed which solves a convergent sequence of projected linear systems and thereby builds up a highly suitable search space. The focus is on efficient implementation with particular emphasis on the reuse of information.