André Uschmajew
Max-Planck-Institut für Mathematik in den Naturwissenschaften
Inselstr. 22
04103 Leipzig
Germany
Office: G3 08
Contact:
Email
Phone:
+49 341 9959 824
Fax:
+49 341 9959 658
André Uschmajew
News & Activities
- From October 2020 to March 2021 I am a visiting professor at Leipzig University.
- I am a co-organizer of the workshop Mathematical Foundations and Algorithms for Tensor Computations, May 3 -7, 2021, at IPAM. It is part of the 2021 IPAM Long Program Tensor Methods and Emerging Applications to the Physical and Data Sciences.
- The 2020 Annual GAMM AG Workshop Computational and Mathematical Methods in Data Science (COMinDS 2020), September 10 - 11, has been co-organized by Max von Renesse (Leipzig University) and myself.
- Past international workshops:
Low-rank Optimization and Applications 2019, April 01 - 05, 2019, Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
Nonsmooth Optimization and its Applications, May 15 - 19, 2017, Hausdorff Center for Mathematics, University of Bonn, Germany
Low-rank Optimization and Applications 2015, June 08 - 12, 2015, Hausdorff Center for Mathematics, University of Bonn, Germany
About
I am Max Planck Research Group Leader for the group Tensors and Optimization at MPI MiS in Leipzig.
My research is on low-rank approximation of matrices and tensors, multivariate functions, and high-dimensional equations. I am particularly interested in the algebraic and geometric structures that underlie multilinear representations of low-rank matrix and tensor manifolds, and their implications on the applicability and convergence of numerical optimization methods.
Academic CV:
- since Oct. 2017: Max Planck Research Group Leader at MPI MiS, Leipzig, Germany
- 2014 - 2017 Bonn Junior Fellow Professor at University of Bonn, Germany
- 2013 - 2014 Postdoc at EPF Lausanne, Switzerland
- 2008 - 2012 PhD at TU Berlin
I am an associate editor of Linear Algebra and Its Applications (LAA) and of Numerical Algebra, Control and Optimization (NACO).
I am a co-initiator of the Mathematics of Data Initiative at MPI MiS.
Publications
Preprints
H. Eisenmann ; F. Krahmer ; M. Pfeffer and A. Uschmajew:
Riemannian thresholding methods for row-sparse and low-rank matrix recovery.
Bibtex MIS-Preprint: 4/2021[ARXIV] Repository Open Access
M. Bachmayr ; H. Eisenmann ; E. Kieri and A. Uschmajew:
Existence of dynamical low-rank approximations to parabolic problems.
(Mathematics of computation)
Bibtex MIS-Preprint: 33/2020[ARXIV] Repository Open Access
W. Hackbusch and A. Uschmajew:
Modified iterations for data-sparse solution of linear systems.
Bibtex MIS-Preprint: 58/2020 Repository Open Access
T. Lehmann ; M. v.
Renesse ; A. Sambale and A. Uschmajew:
A note on overrelaxation in the Sinkhorn algorithm.
Bibtex MIS-Preprint: 110/2020[ARXIV] Repository Open Access
P. Gelß ; S. Matera ; R. Schneider and A. Uschmajew:
Low-rank approximability of nearest neighbor interaction systems.
Bibtex MIS-Preprint: 82/2018 Repository Open Access
Books
S. Hosseini ; B. S. Mordukhovich and A. Uschmajew (eds.):
Nonsmooth optimization and its applications : based on the workshop 'Nonsmooth optimization
and its Applications', Bonn, Germany, May 15-19, 2017.
Springer Birkhäuser, 2019. - VII, 149 p.
(International series of numerical mathematics ; 170)
ISBN 978-3-030-11369-8 Bibtex [DOI]
Journal articles and proceedings
C. Krumnow ; M. Pfeffer and A. Uschmajew:
Computing eigenspaces with low rank constraints.
SIAM journal on scientific computing,
43 (2021) 1, p. A586-A608 Bibtex MIS-Preprint: 102/2019[DOI] Repository Open Access
A. Agrachev ; K. Kozhasov and A. Uschmajew:
Chebyshev polynomials and best rank-one approximation ratio.
SIAM journal on matrix analysis and applications,
41 (2020) 1, p. 308-331 Bibtex MIS-Preprint: 34/2019[DOI][ARXIV] Repository Open Access
A.-H. Phan ; A. Cichocki ; A. Uschmajew ; P. Tichavsky ; G. Luta and D. Mandic:
Tensor networks for latent variable analysis : novel algorithms for tensor train approximation.
IEEE transactions on neural networks and learning systems,
31 (2020) 11, p. 4622-4636 Bibtex [DOI][ARXIV] Repository Open Access
A. Uschmajew and B. Vandereycken:
On critical points of quadratic low-rank matrix optimization problems.
IMA journal of numerical analysis,
40 (2020) 4, p. 2626-2651 Bibtex MIS-Preprint: 58/2018[DOI] Journal Open Access
A. Uschmajew and B. Vandereycken:
Geometric methods on low-rank matrix and tensor manifolds.
Handbook of variational methods for nonlinear geometric data / P.
Grohs... (eds.).Springer, 2020. - P. 261-313 Bibtex [DOI][PDF] Journal Open Access
S. Hosseini ; D. R.
Luke and A. Uschmajew:
Tangent and normal cones for low-rank matrices.
Nonsmooth optimization and its applications : based on the workshop 'Nonsmooth optimization
and its Applications', Bonn, Germany, May 15-19, 2017 / S.
Hosseini... (eds.).Springer Birkhäuser, 2019. - P. 45-53
(International series of numerical mathematics ; 170)
Bibtex [DOI][FREELINK] Repository Open Access
S. Hosseini and A. Uschmajew:
A gradient sampling method on algebraic varieties and application to nonsmooth low-rank
optimization.
SIAM journal on optimization,
29 (2019) 4, p. 2853-2880 Bibtex [DOI][PDF] Repository Open Access
M. Pfeffer ; A. Uschmajew ; A. Amaro and U. Pfeffer:
Data fusion techniques for the integration of multi-domain genomic data from uveal
melanoma.
Cancers,
11 (2019) 10, 1434 Bibtex MIS-Preprint: 42/2019[DOI] Journal Open Access
Z. Li ; Y. Nakatsukasa ; T. Soma and A. Uschmajew:
On orthogonal tensors and best rank-one approximation ratio.
SIAM journal on matrix analysis and applications,
39 (2018) 1, p. 400-425 Bibtex [DOI][ARXIV][PDF] Repository Open Access
I. V. Oseledets ; M. Rakhuba and A. Uschmajew:
Alternating least squares as moving subspace correction.
SIAM journal on numerical analysis,
56 (2018) 6, p. 3459-3479 Bibtex [DOI][ARXIV][PDF] Repository Open Access
W. Hackbusch ; D. Kressner and A. Uschmajew:
Perturbation of higher-order singular values.
SIAM journal on applied algebra and geometry,
1 (2017) 1, p. 374-387 Bibtex MIS-Preprint: 51/2016[DOI][PDF] Journal Open Access
W. Hackbusch and A. Uschmajew:
On the interconnection between the higher-order singular values of real tensors.
Numerische Mathematik,
135 (2017) 3, p. 875-894 Bibtex MIS-Preprint: 62/2015[DOI] Journal Open Access
S. Hosseini and A. Uschmajew:
A Riemannian gradient sampling algorithm for nonsmooth optimization on manifolds.
SIAM journal on optimization,
27 (2017) 1, p. 173-189 Bibtex [DOI][PDF] Repository Open Access
Y. Nakatsukasa ; T. Soma and A. Uschmajew:
Finding a low-rank basis in a matrix subspace.
Mathematical programming,
162 (2017) 1-2, p. 325-361 Bibtex [DOI][ARXIV] Repository Open Access
M. Bachmayr ; R. Schneider and A. Uschmajew:
Tensor networks and hierarchical tensors for the solution of high-dimensional partial
differential equations.
Foundations of computational mathematics,
16 (2016) 6, p. 1423-1472 Bibtex [DOI][PDF] Repository Open Access
L. Karlsson ; D. Kressner and A. Uschmajew:
Parallel algorithms for tensor completion in the CP format.
Parallel computing,
57 (2016), p. 222-234 Bibtex [DOI][PDF] Repository Open Access
D. Kressner and A. Uschmajew:
On low-rank approximability of solutions to high-dimensional operator equations and
eigenvalue problems.
Linear algebra and its applications,
493 (2016), p. 556-572 Bibtex [DOI][ARXIV][PDF] Repository Open Access
R. Schneider and A. Uschmajew:
Convergence results for projected line-search methods on varieties of low-rank matrices
via Łojasiewicz inequality.
SIAM journal on optimization,
25 (2016) 1, p. 622-646 Bibtex [DOI][ARXIV][PDF] Repository Open Access
Z. Li ; A. Uschmajew and S. Zhang:
On convergence of the maximum block improvement method.
SIAM journal on optimization,
25 (2015) 1, p. 210-233 Bibtex [DOI][PDF] Repository Open Access
A. Uschmajew:
A new convergence proof for the higher-order power method and generalizations.
Pacific journal of optimization : an international journal,
11 (2015) 2, p. 309-321 Bibtex [ARXIV][FREELINK][PDF] Repository Open Access
A. Uschmajew:
Some results concerning rank-one truncated steepest descent directions in tensor spaces.
2015 International conference on sampling theory and applications (SampTA) took place
May 25-29, 2015 in Washington, DC, USAIEEE, 2015. - P. 415-419 Bibtex [DOI][PDF] Repository Open Access
A. Uschmajew and B. Vandereycken:
Greedy rank updates combined with Riemannian descent methods for low-rank optimization.
2015 International conference on sampling theory and applications (SampTA) took place
May 25-29, 2015 in Washington, DC, USAIEEE, 2015. - P. 420-424 Bibtex [DOI][PDF] Repository Open Access
D. Kressner ; M. Steinlechner and A. Uschmajew:
Low-rank tensor methods with subspace correction for symmetric eigenvalue problems.
SIAM journal on scientific computing,
36 (2014) 5, p. A2346-A2368 Bibtex [DOI][PDF] Repository Open Access
R. Schneider and A. Uschmajew:
Approximation rates for the hierarchical tensor format in periodic Sobolev spaces.
Journal of complexity,
30 (2014) 2, p. 56-71 Bibtex [DOI][PDF] Repository Open Access
A. Uschmajew and B. Vandereycken:
Line-search methods and rank increase on low-rank matrix varieties.
2014 International symposium on nonlinear theory and its applications : NOLTA2014,
Luzern, Switzerland, September 14-18, 2014IEICE, 2014. - P. 52-55 Bibtex [FREELINK][PDF] Repository Open Access
T. Rohwedder and A. Uschmajew:
On local convergence of alternating schemes for optimization of convex problems in
the tensor train format.
SIAM journal on numerical analysis,
51 (2013) 2, p. 1134-1162 Bibtex [DOI][PDF] Repository Open Access
A. Uschmajew ; D. Kressner and M. Steinlechner:
Low-rank tensor methods with subspace correction for symmetric eigenvalue problems
[In: Numerical solution of PDE eigenvalue problems ; 17 November - 23 November 2013
; report no. 56/2013].
Oberwolfach reports,
10 (2013) 4, p. 3296-3298 Bibtex [DOI][FREELINK] Repository Open Access
A. Uschmajew and B. Vandereycken:
The geometry of algorithms using hierarchical tensors.
Linear algebra and its applications,
439 (2013) 1, p. 133-166 Bibtex [DOI][PDF] Repository Open Access
S. R.
Chinnamsetty ; H. Luo ; W. Hackbusch ; H.-J. Flad and A. Uschmajew:
Bridging the gap between quantum Monte Carlo and F12-methods.
Chemical physics,
401 (2012), p. 36-44 Bibtex MIS-Preprint: 68/2011[DOI][PDF] Repository Open Access
A. Uschmajew:
Local convergence of the alternating least squares algorithm for canonical tensor
approximation.
SIAM journal on matrix analysis and applications,
33 (2012) 2, p. 639-652 Bibtex [DOI][PDF] Repository Open Access
A. Uschmajew:
Regularity of tensor product approximations to square integrable functions.
Constructive approximation,
34 (2011) 3, p. 371-391 Bibtex [DOI][PDF] Repository Open Access
A. Uschmajew:
The regularity of tensor product approximations in \(L^2\) in dependence of the target
function [In: Mathematical methods in quantum chemistry ; June 26 th - July 2nd, 2011
; report no. 32/2011].
Oberwolfach reports,
8 (2011) 2, p. 1802-1804 Bibtex [DOI][FREELINK] Repository Open Access
A. Uschmajew:
Well-posedness of convex maximization problems on Stiefel manifolds and orthogonal
tensor product approximations.
Numerische Mathematik,
115 (2010) 2, p. 309-331 Bibtex [DOI][PDF] Repository Open Access