The regularizing properties of the adjoint gradient method in ill-posed problems
USSR Computational Mathematics and Mathematical Physics
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)
Fast CG-Based Methods for Tikhonov--Phillips Regularization
SIAM Journal on Scientific Computing
LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
ACM Transactions on Mathematical Software (TOMS)
Algorithm 583: LSQR: Sparse Linear Equations and Least Squares Problems
ACM Transactions on Mathematical Software (TOMS)
Tikhonov regularization and the L-curve for large discrete ill-posed problems
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. III: linear algebra
Choosing Regularization Parameters in Iterative Methods for Ill-Posed Problems
SIAM Journal on Matrix Analysis and Applications
Invertible smoothing preconditioners for linear discrete ill-posed problems
Applied Numerical Mathematics - 6th IMACS International symposium on iterative methods in scientific computing
Iterative Methods for Toeplitz Systems (Numerical Mathematics and Scientific Computation)
Iterative Methods for Toeplitz Systems (Numerical Mathematics and Scientific Computation)
Iterative methods for ill-posed problems and semiconvergent sequences
Journal of Computational and Applied Mathematics
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Several numerical methods for the solution of large linear ill-posed problems combine Tikhonov regularization with an iterative method based on partial Lanczos bidiagonalization of the operator. This paper discusses the determination of the regularization parameter and the dimension of the Krylov subspace for this kind of method. A method that requires a Krylov subspace of minimal dimension is referred to as greedy.