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- Publications
- Influence

Low-Rank Tensor Krylov Subspace Methods for Parametrized Linear Systems

- D. Kressner, C. Tobler
- Computer Science, Mathematics
- SIAM J. Matrix Anal. Appl.
- 1 November 2011

We consider linear systems $A(\alpha) x(\alpha) = b(\alpha)$ depending on possibly many parameters $\alpha = (\alpha_1,\ldots,\alpha_p)$. Solving these systems simultaneously for a standard… Expand

Low-rank tensor completion by Riemannian optimization

- D. Kressner, M. Steinlechner, B. Vandereycken
- Mathematics
- 1 June 2014

In tensor completion, the goal is to fill in missing entries of a partially known tensor under a low-rank constraint. We propose a new algorithm that performs Riemannian optimization techniques on… Expand

A literature survey of low‐rank tensor approximation techniques

- L. Grasedyck, D. Kressner, C. Tobler
- Mathematics, Physics
- 28 February 2013

During the last years, low-rank tensor approximation has been established as a new tool in scientific computing to address large-scale linear and multilinear algebra problems, which would be… Expand

Algorithm 941: htucker---A Matlab Toolbox for Tensors in Hierarchical Tucker Format

- D. Kressner, C. Tobler
- Computer Science
- TOMS
- 1 April 2014

The hierarchical Tucker format is a storage-efficient scheme to approximate and represent tensors of possibly high order. This article presents a Matlab toolbox, along with the underlying methodology… Expand

A block Newton method for nonlinear eigenvalue problems

- D. Kressner
- Computer Science, Mathematics
- Numerische Mathematik
- 13 November 2009

We consider matrix eigenvalue problems that are nonlinear in the eigenvalue parameter. One of the most fundamental differences from the linear case is that distinct eigenvalues may have linearly… Expand

Krylov Subspace Methods for Linear Systems with Tensor Product Structure

- D. Kressner, C. Tobler
- Computer Science, Mathematics
- SIAM J. Matrix Anal. Appl.
- 2010

The numerical solution of linear systems with certain tensor product structures is considered. Such structures arise, for example, from the finite element discretization of a linear PDE on a… Expand

Parallel algorithms for tensor completion in the CP format

- L. Karlsson, D. Kressner, A. Uschmajew
- Computer Science
- Parallel Comput.
- 1 September 2016

Novel parallel algorithms for tensor completion problems, with applications to recommender systems and function learning.Parallelization strategy offers greatly reduced memory requirements compared… Expand

Preconditioned Low-Rank Methods for High-Dimensional Elliptic PDE Eigenvalue Problems

- D. Kressner, C. Tobler
- Computer Science, Mathematics
- Comput. Methods Appl. Math.
- 2011

Abstract We consider elliptic PDE eigenvalue problems on a tensorized domain, discretized such that the resulting matrix eigenvalue problem Ax=λx exhibits Kronecker product structure. In particular,… Expand

Learning Heat Diffusion Graphs

- Dorina Thanou, X. Dong, D. Kressner, P. Frossard
- Computer Science, Mathematics
- IEEE Transactions on Signal and Information…
- 4 November 2016

Information analysis of data often boils down to properly identifying their hidden structure. In many cases, the data structure can be described by a graph representation that supports signals in the… Expand

Structured Hölder Condition Numbers for Multiple Eigenvalues

- D. Kressner, M. J. Peláez, J. Moro
- Mathematics, Computer Science
- SIAM J. Matrix Anal. Appl.
- 1 February 2009

The sensitivity of a multiple eigenvalue of a matrix under perturbations can be measured by its Holder condition number. Various extensions of this concept are considered. A meaningful notion of… Expand