E-LIB Suche - Ergebnisse für: Uschmajew, André

1

Dynamical low-rank approximation of the Vlasov–Poisson equa..:

Uschmajew, André ; Zeiser, Andreas
BIT Numerical Mathematics. 64 (2024) 2 - p. , 2024

2

Time-Varying Semidefinite Programming: Path Following a Bur..:

Bellon, Antonio ; Dressler, Mareike ; Kungurtsev, Vyacheslav..
SIAM Journal on Optimization. 34 (2024) 1 - p. 1-26 , 2024

Link: https://doi.org/10.1137/..

3

Kronecker Product Approximation of Operators in Spectral No..:

Dressler, Mareike ; Uschmajew, André ; Chandrasekaran, Venkat
SIAM Journal on Matrix Analysis and Applications. 44 (2023) 4 - p. 1693-1708 , 2023

Link: https://doi.org/10.1137/..

4

Riemannian thresholding methods for row-sparse and low-rank..:

Eisenmann, Henrik ; Krahmer, Felix ; Pfeffer, Max.
Numerical Algorithms. 93 (2022) 2 - p. 669-693 , 2022

Link: https://doi.org/10.1007/..

5

Local convergence of alternating low‐rank optimization meth..:

Oseledets, Ivan V. ; Rakhuba, Maxim V. ; Uschmajew, André
Numerical Linear Algebra with Applications. 30 (2022) 3 - p. , 2022

Link: https://doi.org/10.1002/..

6

Maximum relative distance between real rank-two and rank-on..:

Eisenmann, Henrik ; Uschmajew, André
Annali di Matematica Pura ed Applicata (1923 -). 202 (2022) 2 - p. 993-1009 , 2022

Link: https://doi.org/10.1007/..

7

A Note on the Optimal Convergence Rate of Descent Methods w..:

Uschmajew, André ; Vandereycken, Bart
Journal of Optimization Theory and Applications. 194 (2022) 1 - p. 364-373 , 2022

Link: https://doi.org/10.1007/..

8

Editorial: High-performance tensor computations in scientif..:

Di Napoli, Edoardo ; Bientinesi, Paolo ; Li, Jiajia.
Frontiers in Applied Mathematics and Statistics. 8 (2022) - p. , 2022

Link: https://doi.org/10.3389/..

9

Computing Eigenspaces With Low Rank Constraints:

Krumnow, Christian ; Pfeffer, Max ; Uschmajew, André
SIAM Journal on Scientific Computing. 43 (2021) 1 - p. A586-A608 , 2021

Link: https://doi.org/10.1137/..

10

Modified Iterations for Data-Sparse Solution of Linear Syst..:

Hackbusch, Wolfgang ; Uschmajew, André
Vietnam Journal of Mathematics. , 2021

Link: https://doi.org/10.1007/..

11

A note on overrelaxation in the Sinkhorn algorithm:

Lehmann, Tobias ; von Renesse, Max-K. ; Sambale, Alexander.
Optimization Letters. 16 (2021) 8 - p. 2209-2220 , 2021

Link: https://doi.org/10.1007/..

12

On critical points of quadratic low-rank matrix optimizatio..:

Uschmajew, André ; Vandereycken, Bart
IMA Journal of Numerical Analysis. 40 (2020) 4 - p. 2626-2651 , 2020

Link: https://doi.org/10.1093/..

13

Geometric Methods on Low-Rank Matrix and Tensor Manifolds:

, In: Handbook of Variational Methods for Nonlinear Geometric Data,

Uschmajew, André ; Vandereycken, Bart - p. 261-313 , 2020

Link: https://doi.org/10.1007/..

14

Chebyshev Polynomials and Best Rank-one Approximation Ratio:

Agrachev, Andrei ; Kozhasov, Khazhgali ; Uschmajew, André
SIAM Journal on Matrix Analysis and Applications. 41 (2020) 1 - p. 308-331 , 2020

Link: https://doi.org/10.1137/..

15

Convergence of gradient methods on hierarchical tensor vari..:

Kutschan, Benjamin , 2019

Link: https://nbn-resolving.de..