Publications

Alternative format: Google Scholar Page

(*authors are ordered alphabetically)

Submitted*

• [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)

• J. Kane, L. Onisk, and L. Reichel, Solution of large linear discrete ill-posed problems by randomized block Krylov methods (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. G. Nagy and L. Onisk, Mixed precision iterative refinement for linear inverse problems (arXiv link - 2024)

Published and Accepted*

• 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)