5 papers
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…
Interpolatory Dynamical Low-Rank Approximation: Theoretical Foundations and Algorithms
Benjamin Carrel, Daniel Kressner, Hei Yin Lam +1
Dynamical low-rank approximation (DLRA) is a widely used paradigm for solving large-scale matrix differential equations, as they arise, for example, from the discretization of time…
Randomized low-rank Runge-Kutta methods
Hei Yin Lam, Gianluca Ceruti, Daniel Kressner
This work proposes and analyzes a new class of numerical integrators for computing low-rank approximations to solutions of matrix differential equation. We combine an explicit Rung…
Subspace embedding with random Khatri-Rao products and its application to eigensolvers
Zvonimir BujanoviÄ, Luka GrubiÅ¡iÄ, Daniel Kressner +1
Various iterative eigenvalue solvers have been developed to compute parts of the spectrum for a large sparse matrix, including the power method, Krylov subspace methods, contour in…
Randomized low-rank approximation of parameter-dependent matrices
Daniel Kressner, Hei Yin Lam
This work considers the low-rank approximation of a matrix depending on a parameter in a compact set . Application areas that give rise to such p…