David Persson

Research Interests

My research focuses on the theoretical foundations of numerical linear algebra algorithms. In particular, I am interested in randomized algorithms, hierarchical matrices, matrix functions, low-rank approximation, trace estimation, and applications of linear algebra in machine learning.

Publications

D. Kressner, D. Persson, and A. Uschmajew, On the randomized SVD in infinite dimensions arXiv preprint arXiv:2506.06882, (2025)
N. Amsel, T. Chen, F.D. Keles, D. Halikias, C. Musco, C. Musco, and D. Persson, Quasi-optimal hierarchically semi-separable matrix approximation, arXiv preprint arXiv:2505.16937, (2025).
N. Amsel, D. Persson, C. Musco, and R.M. Gower, The Polar Express: Optimal matrix-sign methods and their application within the Muon method, arXiv preprint arXiv:2505.16932, (2025).
D. Persson, N. Boullé, and D. Kressner, Randomized Nyström approximation of non-negative self-adjoint operators, SIAM J. Data Sci. 7 (2025), pp. 670-698.
D. Persson, T. Chen, and C. Musco, Randomized block-Krylov subspace methods for low-rank approximation of matrix functions, arXiv preprint arXiv:2502.01888, (2025).
T. Chen, F.D. Keles, D. Halikias, C. Musco, C. Musco, and D. Persson, Near-optimal hierarchical matrix approximation from matrix-vector products, in Proceedings of the 2025 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2025, pp. 2656–2692.
D. Persson, R.A. Meyer, and C. Musco, Algorithm-agnostic low-rank approximation of operator monotone matrix functions, SIAM J. Matrix Anal. Appl., 46 (2025), pp. 1–21.
D. Persson and D. Kressner, Randomized low-rank approximation of monotone matrix functions, SIAM J. Matrix Anal. Appl., 44 (2023), pp. 894–918.
D. Persson, A. Cortinovis, and D. Kressner, Improved variants of the Hutch++ algorithm for trace estimation, SIAM J. Matrix Anal. Appl., 43 (2022), pp. 1162–1185.

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Curriculum Vitae

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