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Approximating matrix functions by block Krylov methods with randomized vectors
Josh Kane, Lucas Onisk, Lothar Reichel +1
The need to evaluate expressions of the form , where is a square matrix, is a function, and is a vector, arises in several areas of applied mat…
Iterated Tikhonov regularization of large linear problems
Davide Furchì, Lothar Reichel
Many solution methods for linear discrete ill-posed problems with error-contaminated data (right-hand side) apply Tikhonov regularization to compute a meaningful approximate soluti…
The iterated Golub-Kahan-Tikhonov method
Davide Bianchi, Marco Donatelli, Davide Furchì +1
The Golub-Kahan-Tikhonov method is a popular solution technique for large linear discrete ill-posed problems. This method first applies partial Golub-Kahan bidiagonalization to red…
Convergence analysis and parameter estimation for the iterated Arnoldi-Tikhonov method
Davide Bianchi, Marco Donatelli, Davide Furchì +1
The Arnoldi-Tikhonov method is a well-established regularization technique for solving large-scale ill-posed linear inverse problems. This method leverages the Arnoldi decompositio…