Agenda

Speaker:
Alice Cortinovis, Department of Computer Science, University of Pisa

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Meeting ID: 856 4636 0001         Passcode:  PizQy1

Abstract:
We consider the problem of finding a low-rank approximation of a given matrix in an efficient way. We focus on approximations that are built from rows and columns of the matrix, starting with the column subset selection problem. We propose a randomized strategy to select suitable rows and columns, called Adaptive Randomized Pivoting. The algorithm is simple and it guarantees, in expectation, an approximation error that matches the optimal existence result in the Frobenius norm. To show the versatility of Adaptive Randomized Pivoting, we apply it to select indices in cross approximation of general matrices and in the Nyström approximation of symmetric positive semidefinite matrices.

Bio sketch:
Alice Cortinovis is an Italian mathematician and researcher in numerical analysis at the University of Pisa. Her research interests include numerical linear algebra, randomized algorithms, low-rank matrix approximation, matrix functions, and large-scale scientific computing. She studied Mathematics at the University of Pisa and the Scuola Normale Superiore, and received her PhD from EPFL in 2022 under the supervision of Daniel Kressner. She was subsequently Szegö Assistant Professor at Stanford University before joining the University of Pisa. Her work has been recognized through several international awards. In 2023, she received a First Prize in the Leslie Fox Prize for Numerical Analysis, awarded to early-career researchers in the field. In 2025, she was awarded the Householder Prize, which recognizes an outstanding recent PhD thesis in numerical linear algebra.