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André Uschmajew
André Uschmajew
University of Augsburg
Verified email at uni-a.de
Title
Cited by
Cited by
Year
Local convergence of the alternating least squares algorithm for canonical tensor approximation
A Uschmajew
SIAM Journal on Matrix Analysis and Applications 33 (2), 639-652, 2012
2462012
Tensor networks and hierarchical tensors for the solution of high-dimensional partial differential equations
M Bachmayr, R Schneider, A Uschmajew
Foundations of Computational Mathematics 16 (6), 1423-1472, 2016
1782016
On local convergence of alternating schemes for optimization of convex problems in the tensor train format
T Rohwedder, A Uschmajew
SIAM Journal on Numerical Analysis 51 (2), 1134-1162, 2013
158*2013
The geometry of algorithms using hierarchical tensors
A Uschmajew, B Vandereycken
Linear Algebra and its Applications 439 (1), 133-166, 2013
1502013
Convergence results for projected line-search methods on varieties of low-rank matrices via Łojasiewicz inequality
R Schneider, A Uschmajew
SIAM Journal on Optimization 25 (1), 622-646, 2015
1442015
Parallel algorithms for tensor completion in the CP format
L Karlsson, D Kressner, A Uschmajew
Parallel Computing 57, 222-234, 2016
1092016
Approximation rates for the hierarchical tensor format in periodic Sobolev spaces
R Schneider, A Uschmajew
Journal of Complexity 30 (2), 56-71, 2014
1012014
Low-rank tensor methods with subspace correction for symmetric eigenvalue problems
D Kressner, M Steinlechner, A Uschmajew
SIAM Journal on Scientific Computing 36 (5), A2346-A2368, 2014
912014
A Riemannian gradient sampling algorithm for nonsmooth optimization on manifolds
S Hosseini, A Uschmajew
SIAM Journal on Optimization 27 (1), 173-189, 2017
902017
A new convergence proof for the higher-order power method and generalizations
A Uschmajew
Pacific Journal of Optimization 11 (2), 309-321, 2015
522015
Tensor networks for latent variable analysis: Novel algorithms for tensor train approximation
AH Phan, A Cichocki, A Uschmajew, P Tichavský, G Luta, DP Mandic
IEEE Transactions on Neural Networks and Learning Systems 31 (11), 4622-4636, 2020
51*2020
Geometric methods on low-rank matrix and tensor manifolds
A Uschmajew, B Vandereycken
Handbook of variational methods for nonlinear geometric data, 261-313, 2020
502020
On convergence of the maximum block improvement method
Z Li, A Uschmajew, S Zhang
SIAM Journal on Optimization 25 (1), 210-233, 2015
492015
On low-rank approximability of solutions to high-dimensional operator equations and eigenvalue problems
D Kressner, A Uschmajew
Linear Algebra and its Applications 493, 556-572, 2016
392016
Greedy rank updates combined with Riemannian descent methods for low-rank optimization
A Uschmajew, B Vandereycken
2015 International Conference on Sampling Theory and Applications (SampTA …, 2015
362015
On orthogonal tensors and best rank-one approximation ratio
Z Li, Y Nakatsukasa, T Soma, A Uschmajew
SIAM Journal on Matrix Analysis and Applications 39 (1), 400-425, 2018
352018
Well-posedness of convex maximization problems on Stiefel manifolds and orthogonal tensor product approximations
A Uschmajew
Numerische Mathematik 115 (2), 309-331, 2010
32*2010
Finding a low-rank basis in a matrix subspace
Y Nakatsukasa, T Soma, A Uschmajew
Mathematical Programming 162 (1-2), 325-361, 2017
292017
Line-search methods and rank increase on low-rank matrix varieties
A Uschmajew, B Vandereycken
Proceedings of the 2014 International Symposium on Nonlinear Theory and its …, 2014
262014
On critical points of quadratic low-rank matrix optimization problems
A Uschmajew, B Vandereycken
IMA Journal of Numerical Analysis 40 (4), 2626-2651, 2020
212020
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Articles 1–20