Tikhonov Regularization with a Solution Constraint

Authors:
Daniela Calvetti;Lothar Reichel
Affiliations:
-;-
Venue:
SIAM Journal on Scientific Computing
Year:
2005

Citing 0
Cited 6

Partial Lanczos extreme learning machine for single-output regression problems

Neurocomputing
An interior-point method for large constrained discrete ill-posed problems

Journal of Computational and Applied Mathematics
An iterative Lagrange method for the regularization of discrete ill-posed inverse problems

Computers & Mathematics with Applications
Accelerating the LSTRS Algorithm

SIAM Journal on Scientific Computing
Combining approximate solutions for linear discrete ill-posed problems

Journal of Computational and Applied Mathematics
Moving the suspended load of an overhead crane along a pre-specified path: A non-time based approach

Robotics and Computer-Integrated Manufacturing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Many numerical methods for the solution of linear ill-posed problems apply Tikhonov regularization. This paper presents a modification of a numerical method proposed by Golub and von Matt for quadratically constrained least-squares problems and applies it to Tikhonov regularization of large-scale linear discrete ill-posed problems. The method is based on partial Lanczos bidiagonalization and Gauss quadrature. Computed examples illustrating its performance are presented.