research
∙
06/17/2023
Gradient-type subspace iteration methods for the symmetric eigenvalue problem
This paper explores variants of the subspace iteration algorithm for com...
share
research
∙
06/01/2023
Gauss-Southwell type descent methods for low-rank matrix optimization
We consider gradient-related methods for low-rank matrix optimization wi...
research
∙
05/19/2023
Implicit low-rank Riemannian schemes for the time integration of stiff partial differential equations
We propose two implicit numerical schemes for the low-rank time integrat...
research
∙
11/22/2022
Randomized sketching of nonlinear eigenvalue problems
Rational approximation is a powerful tool to obtain accurate surrogates ...
research
∙
08/04/2022
Streaming Tensor Train Approximation
Tensor trains are a versatile tool to compress and work with high-dimens...
research
∙
04/16/2022
Riemannian optimization using three different metrics for Hermitian PSD fixed-rank constraints: an extended version
We consider smooth optimization problems with a Hermitian positive semi-...
research
∙
03/16/2022
Low-rank Parareal: a low-rank parallel-in-time integrator
The Parareal algorithm of Lions, Maday, and Turinici is a well-known tim...
research
∙
03/08/2022
TTML: tensor trains for general supervised machine learning
This work proposes a novel general-purpose estimator for supervised mach...
research
∙
10/27/2021
Distributed Principal Component Analysis with Limited Communication
We study efficient distributed algorithms for the fundamental problem of...
research
∙
10/27/2020
The leapfrog algorithm as nonlinear Gauss-Seidel
Several applications in optimization, image and signal processing deal w...
research
∙
05/14/2020