Alternative format: Google Scholar Page
(*authors are ordered alphabetically)
J. Kane, L. Onisk, and L. Reichel, Numerical considerations when sketching Krylov methods for linear discrete ill-posed problems (2026)
J. Kane, L. Onisk, L. Reichel, and G. Rodriguez, Approximating matrix functions by randomized block Krylov methods (2026)
C. Drum, J. G. Nagy, and L. Onisk, Projected iterated Tikhonov regularization in low precision (arXiv link - 2025)
[student advised paper] M. Hu and L. Onisk, On the choice of subspace for the quasi-minimal residual method for linear inverse problems (arXiv link - 2025)
L. Onisk and M. Sabaté Landman, Iterative refinement and flexible iteratively reweighted solvers for linear inverse problems with sparse solutions (arXiv link - 2025)
J. Kane, L. Onisk, and L. Reichel, Solution of large linear discrete ill-posed problems by randomized block Krylov methods, BIT Numerical Mathematics, 66, 3 (2026), https://doi.org/10.1007/s10543-025-01097-2
J. G. Nagy and L. Onisk, Mixed precision iterative refinement for linear inverse problems, SIAM Journal on Matrix Analysis and Applications (accepted - 2025)
J. Chung, L. Onisk, and Y. Wang, Iterative reconstruction methods for cosmological x-ray tomography, SIAM Journal on Imaging Sciences, 18, 1653--1680 (2025), https://epubs.siam.org/doi/10.1137/24M1656724
A. Buccini, S. Gazzola, L. Onisk, M. Pasha, and L. Reichel, Projected iterated Tikhonov in general form with adaptive choice of the regularization parameter, Numer. Algor. (2025), https://doi.org/10.1007/s11075-025-02072-2
A. Buccini, M. Donatelli, L. Onisk, and L. Reichel, Flexible iterative methods for linear systems of equations with multiple right-hand sides, Numer. Algor. (2025), https://doi.org/10.1007/s11075-025-02007-x
L. Onisk, L. Reichel, and H. Sadok, Numerical considerations of block GMRES methods when applied to linear discrete ill-posed problems, J. Comp. and App. Math. 430 (2023), 115262
A. Buccini, L. Onisk, and L. Reichel, Range restricted iterative methods for the solution of discrete linear ill-posed problems. Electron. Trans. Numer. Anal. 58, 348--377 (2023)
A. Buccini, L. Onisk, and L. Reichel, An Arnoldi-based preconditioner for iterated Tikhonov regularization. Numer. Algor. 92, 223--245 (2023)
L. Onisk, Arnoldi-type methods for the solution of linear discrete ill-posed problems (Ph.D. Dissertation, 2022)