3 citations · 3 across the 3 of their papers we have counts for
4 papers
A parameter-dependent smoother for the multigrid method
Lars Grasedyck, Maren Klever, Christian Löbbert +1
The solution of parameter-dependent linear systems, by classical methods, leads to an arithmetic effort that grows exponentially in the number of parameters. This renders the multi…
Low-rank tensor methods for Markov chains with applications to tumor progression models
Peter Georg, Lars Grasedyck, Maren Klever +3
Continuous-time Markov chains describing interacting processes exhibit a state space that grows exponentially in the number of processes. This state-space explosion renders the com…
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format
Paul B. Rohrbach, Sergey Dolgov, Lars Grasedyck +1
Low-rank tensor approximations have shown great potential for uncertainty quantification in high dimensions, for example, to build surrogate models that can be used to speed up lar…
Finding entries of maximum absolute value in low-rank tensors
Lars Grasedyck, Lukas Juschka, Christian Löbbert
We present an iterative method for the search of extreme entries in low-rank tensors which is based on a power iteration combined with a binary search. In this work we use the HT-f…