Tikhonov regularization and the L-curve for large discrete ill-posed problems

By Daniela Calvetti, S. Morigi, Lothar Reichel, Fiorella Sgallari. 2000

528 citations recorded by the source corpus.

Subfields: Applied Mathematics, Computer Vision and Pattern Recognition, Mathematical Physics

View Paper: https://www.semanticscholar.org/paper/b06d61aa797c88afde67337067f16c12a8fadb2e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Mathematics

1. Lothar Reichel
2. Bryan Lewis
3. Daniela Calvetti
4. Martin Stoll

In Computer Vision and Pattern Recognition

1. Bryan Lewis
2. Lothar Reichel
3. Daniela Calvetti

In Mathematical Physics

1. Lothar Reichel
2. Bryan Lewis
3. Daniela Calvetti

Citations in the corpus

• Arnoldi-Tikhonov regularization methods (2009, methodology): “Table 3 compares RRAT, RRGMRES, the AT method in [ 8 ], and the LBDT method in [9].”
• Tikhonov Regularization of Large Linear Problems (2003, methodology): “The derivation of these bounds requires the columns of U� and V� to be orthogonal, and computed examples reported in [ 7 ] show that the accuracy of the bounds is affected by loss 268”