威尼斯赌博游戏_威尼斯赌博app-【官网】

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威尼斯赌博游戏_威尼斯赌博app-【官网】

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Prof. Dr. André Uschmajew

Lehrstuhlinhaber
Mathematical Data Science
Telefon: +49 821 598 2033
E-Mail:
Raum: 3038 (L1)
Sprechzeiten: nach Vereinbarung
Adresse: Universit?tsstra?e 14, 86159 Augsburg

Lebenslauf

2022 -????????????? Professor für Mathematical Data Science, Universit?t Augsburg

2019, 2020????? Vertretungsprofessur, Universit?t Leipzig

2017 - 2022???? Forschungsgruppenleiter, Max-Planck-Institut MiS Leipzig

2014 - 2017???? Bonn Junior Fellow Professor, Universit?t Bonn

2013 - 2014???? Wissenschaftlicher Mitarbeiter, EPF Lausanne

2013??????????????? Promotion, TU Berlin

2008 - 2013???? Wissenschaftlicher Mitarbeiter, TU Berlin

2008??????????????? Diplom Mathematik, TU Berlin

Forschungsschwerpunkte

  • Tensors: geometry of low-rank varieties and tensor networks tensor product operators
  • Low-rank approximation: functional analytic foundations, approximation rates, spectral and nuclear norm
  • Optimization: block coordinate methods, Riemannian optimization, optimization landscape of multilinear models
  • Applications: high-dimensional problems, low-rank models in data science, signal processing, dynamical low-rank approximation

Publikationen

Publication list at Google Scholar
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Preprints

Markus Bachmayr, Henrik Eisenmann and André Uschmajew
Dynamical low-rank tensor approximations to high-dimensional parabolic problems: existence and convergence of spatial discretizations
arXiv:2308.16720 (2023)

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Guillaume Olikier, André Uschmajew and Bart Vandereycken
Gauss-Southwell type descent methods for low-rank matrix optimization
arXiv:2306.00897 (2023)

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Daniel Kressner, Tingting Ni and André Uschmajew
On the approximation of vector-valued functions by samples
arXiv:2304.03212 (2023)

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André Uschmajew and Andreas Zeiser
Dynamical low-rank approximation of the Vlasov-Poisson equation with piecewise linear spatial boundary
arXiv:2303.01856 (2023)

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Journal articles, book chapters, proceedings

Daniel Kressner, Tingting Ni and André Uschmajew
On the approximation of vector-valued functions by volume sampling

PDF | BibTeX | RIS | DOI

Antonio Bellon, Mareike Dressler, Vyacheslav Kungurtsev, Jakub Marecek and André Uschmajew
Time-varying semidefinite programming: path following a Burer-Monteiro factorization

BibTeX | RIS | URL

Mareike Dressler, André Uschmajew and Venkat Chandrasekaran
Kronecker product approximation of operators in spectral norm via alternating SDP

BibTeX | RIS | DOI

Ivan V. Oseledets, Maxim V. Rakhuba and André Uschmajew
Local convergence of alternating low‐rank optimization methods with overrelaxation

PDF | BibTeX | RIS | DOI

Henrik Eisenmann and André Uschmajew
Maximum relative distance between real rank-two and rank-one tensors

PDF | BibTeX | RIS | DOI

Henrik Eisenmann, Felix Krahmer, Max Pfeffer and André Uschmajew
Riemannian thresholding methods for row-sparse and low-rank matrix recovery

PDF | BibTeX | RIS | DOI

Tobias Lehmann, Max-K. von Renesse, Alexander Sambale and André Uschmajew
A note on overrelaxation in the Sinkhorn algorithm

PDF | BibTeX | RIS | DOI

André Uschmajew and Bart Vandereycken
A note on the optimal convergence rate of descent methods with fixed step sizes for smooth strongly convex functions

PDF | BibTeX | RIS | DOI

Edoardo Di Napoli, Paolo Bientinesi, Jiajia Li and André Uschmajew
Editorial: high-performance tensor computations in scientific computing and data science

PDF | BibTeX | RIS | DOI

Christian Krumnow, Max Pfeffer and André Uschmajew
Computing eigenspaces with low rank constraints

BibTeX | RIS | DOI

André Uschmajew, M. Bachmayr, H. Eisenmann and E. Kieri
Dynamical low-rank approximation for parabolic problems

BibTeX | RIS | DOI

In: Mini-Workshop: Computational Optimization on Manifolds

Markus Bachmayr, Henrik Eisenmann, Emil Kieri and André Uschmajew
Existence of dynamical low-rank approximations to parabolic problems

BibTeX | RIS | DOI

Wolfgang Hackbusch and André Uschmajew
Modified iterations for data-sparse solution of linear systems

PDF | BibTeX | RIS | DOI

Andrei Agrachev, Khazhgali Kozhasov and André Uschmajew
Chebyshev polynomials and best rank-one approximation ratio

BibTeX | RIS | DOI

André Uschmajew and Bart Vandereycken
Geometric methods on low-rank matrix and tensor manifolds

PDF | BibTeX | RIS | DOI

André Uschmajew and Bart Vandereycken
On critical points of quadratic low-rank matrix optimization problems

PDF | BibTeX | RIS | DOI

Anh-Huy Phan, Andrzej Cichocki, André Uschmajew, Petr Tichavsky, George Luta and Danilo P. Mandic
Tensor networks for latent variable analysis: novel algorithms for tensor train approximation

BibTeX | RIS | DOI

Seyedehsomayeh Hosseini and André Uschmajew
A gradient sampling method on algebraic varieties and application to nonsmooth low-rank optimization

BibTeX | RIS | DOI

Max Pfeffer, André Uschmajew, Adriana Amaro and Ulrich Pfeffer
Data fusion techniques for the integration of multi-domain genomic data from uveal melanoma

PDF | BibTeX | RIS | DOI

Seyedehsomayeh Hosseini, D. Russell Luke and André Uschmajew
Tangent and normal cones for low-rank matrices

BibTeX | RIS | DOI

Ivan V. Oseledets, Maxim V. Rakhuba and André Uschmajew
Alternating least squares as moving subspace correction

BibTeX | RIS | DOI

Zhening Li, Yuji Nakatsukasa, Tasuku Soma and André Uschmajew
On orthogonal tensors and best rank-one approximation ratio

BibTeX | RIS | DOI

Seyedehsomayeh Hosseini and André Uschmajew
A Riemannian gradient sampling algorithm for nonsmooth optimization on manifolds

BibTeX | RIS | DOI

Yuji Nakatsukasa, Tasuku Soma and André Uschmajew
Finding a low-rank basis in a matrix subspace

BibTeX | RIS | DOI

Wolfgang Hackbusch and André Uschmajew
On the interconnection between the higher-order singular values of real tensors

PDF | BibTeX | RIS | DOI

Wolfgang Hackbusch, Daniel Kressner and André Uschmajew
Perturbation of higher-order singular values

BibTeX | RIS | DOI

Daniel Kressner and André Uschmajew
On low-rank approximability of solutions to high-dimensional operator equations and eigenvalue problems

BibTeX | RIS | DOI

Lars Karlsson, Daniel Kressner and André Uschmajew
Parallel algorithms for tensor completion in the CP format

BibTeX | RIS | DOI

Markus Bachmayr, Reinhold Schneider and André Uschmajew
Tensor networks and hierarchical tensors for the solution of high-dimensional partial differential equations

BibTeX | RIS | DOI

André Uschmajew
A new convergence proof for the higher-order power method and generalizations

BibTeX | RIS | URL

Reinhold Schneider and André Uschmajew
Convergence results for projected line-search methods on varieties of low-rank matrices via ?ojasiewicz inequality

BibTeX | RIS | DOI

André Uschmajew and Bart Vandereycken
Greedy rank updates combined with Riemannian descent methods for low-rank optimization

BibTeX | RIS | DOI

Zhening Li, André Uschmajew and Shuzhong Zhang
On convergence of the maximum block improvement method

BibTeX | RIS | DOI

André Uschmajew
Some results concerning rank-one truncated steepest descent directions in tensor spaces

BibTeX | RIS | DOI

Reinhold Schneider and André Uschmajew
Approximation rates for the hierarchical tensor format in periodic Sobolev spaces

BibTeX | RIS | DOI

André Uschmajew and Bart Vandereycken
Line-search methods and rank increase on low-rank matrix varieties

BibTeX | RIS | DOI

Daniel Kressner, Michael Steinlechner and André Uschmajew
Low-rank tensor methods with subspace correction for symmetric eigenvalue problems

BibTeX | RIS | DOI

André Uschmajew, D. Kressner and M. Steinlechner
Low-rank tensor methods with subspace correction for symmetric eigenvalue problems

BibTeX | RIS | DOI

In: Numerical solution of PDE eigenvalue problems, 17 November - 23 November 2013; report no. 56/2013

Thorsten Rohwedder and André Uschmajew
On local convergence of alternating schemes for optimization of convex problems in the tensor train format

BibTeX | RIS | DOI

André Uschmajew and Bart Vandereycken
The geometry of algorithms using hierarchical tensors

BibTeX | RIS | DOI

Sambasiva Rao Chinnamsetty, Hongjun Luo, Wolfgang Hackbusch, Heinz-Jürgen Flad and André Uschmajew
Bridging the gap between quantum Monte Carlo and F12-methods

BibTeX | RIS | DOI

André Uschmajew
Local convergence of the alternating least squares algorithm for canonical tensor approximation

BibTeX | RIS | DOI

André Uschmajew
Regularity of tensor product approximations to?square integrable functions

BibTeX | RIS | DOI

André Uschmajew
The regularity of tensor product approximations in L2 in dependence of the target function

BibTeX | RIS | DOI

In: Mathematical methods in quantum chemistry, June 26th - July 2nd, 2011, report no. 32/2011

André Uschmajew
Well-posedness of convex maximization problems on Stiefel manifolds and orthogonal tensor product approximations

BibTeX | RIS | DOI

Dissertation

André Uschmajew
Zur Theorie der Niedrigrangapproximation in Tensorprodukten von Hilbertr?umen

BibTeX | RIS | DOI

Dissertation, TU Berlin, 2013

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