17 papers
Stochastic trace estimation with tensor train random vectors
Zvonimir BujanoviÄ, Daniel Kressner, Hrvoje OliÄ
Stochastic trace estimation is a standard tool for approximating the trace of a large-scale matrix available only through matrix-vector products. However, in tensor-structured sett…
Kernel-based linear system identification using augmented Krylov subspaces
Fabio Matti, Martin Skovgaard Andersen, Tianshi Chen +1
We propose a novel Krylov subspace method for estimating the finite impulse response (FIR) of a one-dimensional linear time-invariant systems. The method approximates the system's…
Linear Systems and Eigenvalue Problems: Open Questions from a Simons Workshop
Noah Amsel, Yves Baumann, Paul Beckman +36
This document presents a series of open questions arising in matrix computations, i.e., the numerical solution of linear algebra problems. It is a result of working groups at the w…
Stochastic trace estimation for parameter-dependent matrices applied to spectral density approximation
Fabio Matti, Haoze He, Daniel Kressner +1
Stochastic trace estimation is a well-established tool for approximating the trace of a large symmetric matrix . Several applications involve a matrix that depends…
On the randomized SVD in infinite dimensions
Daniel Kressner, David Persson, André Uschmajew
Randomized methods, such as the randomized SVD (singular value decomposition) and Nyström approximation, are an effective way to compute low-rank approximations of large matrices.…
A novel Krylov subspace method for approximating Fréchet derivatives of large-scale matrix functions
Daniel Kressner, Peter Oehme
We present a novel Krylov subspace method for approximating $L_f(A, E) \vc{b}$, the matrix-vector product of the Fréchet derivative of a large-scale matrix function $f…