Remarks on choosing a regularization parameter using the quasioptimality and ratio criterion
USSR Computational Mathematics and Mathematical Physics
The use of the L-curve in the regularization of discrete ill-posed problems
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
A Technique for the Numerical Solution of Certain Integral Equations of the First Kind
Journal of the ACM (JACM)
The triangle method for finding the corner of the L-curve
Applied Numerical Mathematics
Iterative methods for ill-posed problems and semiconvergent sequences
Journal of Computational and Applied Mathematics
An adaptive pruning algorithm for the discrete L-curve criterion
Journal of Computational and Applied Mathematics - Special issue: Applied computational inverse problems
A new variant of L-curve for Tikhonov regularization
Journal of Computational and Applied Mathematics
Sparse Bayesian learning for the Laplace transform inversion in dynamic light scattering
Journal of Computational and Applied Mathematics
Mathematics and Computers in Simulation
Old and new parameter choice rules for discrete ill-posed problems
Numerical Algorithms
FGMRES for linear discrete ill-posed problems
Applied Numerical Mathematics
Hi-index | 7.29 |
The truncated singular value decomposition is a popular method for the solution of linear ill-posed problems. The method requires the choice of a truncation index, which affects the quality of the computed approximate solution. This paper proposes that an L-curve, which is determined by how well the given data (right-hand side) can be approximated by a linear combination of the first (few) left singular vectors (or functions), be used as an aid for determining the truncation index.