Preprints:
A.C., Daniel Kressner, Adaptive randomized pivoting for column subset selection, DEIM, and low-rank approximation (arXiv preprint).
Journal publications:
Ya-Chi Chu, A.C, Improved bounds for randomized Schatten norm estimation of numerically low-rank matrices (Linear Algebra and its Applications, 2025). Code.
A.C., Lexing Ying, A sublinear-time randomized algorithm for column and row subset selection based on strong rank-revealing QR factorizations (SIAM Journal on Matrix Analysis and Applications, 2024). Code.
A.C., Lexing Ying, Computing free convolutions via contour integrals (Random Matrices: Theory and Applications, 2024). Code.
A.C., Daniel Kressner, Yuji Nakatsukasa, Speeding up Krylov subspace methods for f(A)b via randomization (SIAM Journal on Matrix Analysis and Applications 2024). Code.
David Persson, A.C., Daniel Kressner, Improved variants of the Hutch++ algorithm for trace estimation (SIAM Journal on Matrix Analysis and Applications 2022). Code.
A.C., Daniel Kressner, Stefano Massei, Divide and conquer methods for functions of matrices with banded or hierarchical low-rank structure (SIAM Journal on Matrix Analysis and Applications 2022). Code.
A.C., Daniel Kressner, On randomized trace estimates for indefinite matrices with an application to determinants (Foundations of Computational Mathematics 2021).
Bernhard Beckermann, A.C., Daniel Kressner, Marcel Schweitzer, Low-rank updates of matrix functions II: Rational Krylov methods (SIAM Journal on Numerical Analysis 2021).
A.C., Daniel Kressner, Low-rank approximation in the Frobenius norm by column and row subset selection (SIAM Journal on Matrix Analysis and Applications 2020). Code.
A.C., Daniel Kressner, Stefano Massei, On maximum volume submatrices and cross approximation for symmetric semidefinite and diagonally dominant matrices (Linear Algebra and Applications 2020).
Conference proceedings:
A.C., Daniel Kressner, Stefano Massei, Benjamin Peherstorfer, Quasi-optimal sampling to learn basis updates for online adaptive model reduction with adaptive empirical interpolation (American Control Conference 2020).
Theses:
PhD thesis: Fast deterministic and randomized algorithms for low-rank approximation, matrix functions, and trace estimation (2022). Advisor: Prof. Daniel Kressner.
Master's thesis: Minimizing the optimality residual for algebraic Riccati equations (2018). Advisor: Prof. Federico Poloni.