Publications de André Uschmajew

On low-rank approximability of solutions to high-dimensional operator equations and eigenvalue problems

Low-rank tensor approximation techniques attempt to mitigate the overwhelming complexity of linear algebra tasks arising from high-dimensional applications. In this work, we study the low-rank approximability of solutions to linear systems and eigenvalue p ...

Elsevier Science Inc2016

Parallel algorithms for tensor completion in the CP format

Daniel Kressner, André Uschmajew

Low-rank tensor completion addresses the task of filling in missing entries in multidimensional data. It has proven its versatility in numerous applications, including context aware recommender systems and multivariate function learning. To handle large-sc ...

Elsevier Science Bv2016

On Convergence Of The Maximum Block Improvement Method

André Uschmajew

The MBI (maximum block improvement) method is a greedy approach to solving optimization problems where the decision variables can be grouped into a finite number of blocks. Assuming that optimizing over one block of variables while fixing all others is rel ...

Siam Publications2015

Convergence Results For Projected Line-Search Methods On Varieties Of Low-Rank Matrices Via Lojasiewicz Inequality

André Uschmajew

The aim of this paper is to derive convergence results for projected line-search methods on the real-algebraic variety M-

Siam Publications2015

A New Convergence Proof For The Higher-Order Power Method And Generalizations

André Uschmajew

A proof for the point-wise convergence of the factors in the higher-order power method for tensors towards a critical point is given. It is obtained by applying established results from the theory of Lojasiewicz inequalities to the equivalent, unconstraine ...

Yokohama Publ2015

Approximation rates for the hierarchical tensor format in periodic Sobolev spaces

André Uschmajew

In this note we estimate the asymptotic rates for the L-2-error decay and the storage cost when approximating 2 pi-periodic, d-variate functions from isotropic and mixed Sobolev classes by the recent hierarchical tensor format as introduced by Hackbusch an ...

Academic Press Inc Elsevier Science2014

Low-Rank Tensor Methods With Subspace Correction For Symmetric Eigenvalue Problems

Daniel Kressner, André Uschmajew, Michael Maximilian Steinlechner

We consider the solution of large-scale symmetric eigenvalue problems for which it is known that the eigenvectors admit a low-rank tensor approximation. Such problems arise, for example, from the discretization of high-dimensional elliptic PDE eigenvalue p ...

Siam Publications2014