伟德国际_伟德国际1946$娱乐app游戏

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Prof. Dr. André Uschmajew

Chair
Mathematical Data Science
Phone: +49 821 598 2033
Email:
Room: 3038 (L1)
Open hours: by appointment
Address: Universit?tsstra?e 14, 86159 Augsburg

Curriculum Vitae

2022 -????????????? Chair of Mathematical Data Science, 伟德国际_伟德国际1946$娱乐app游戏 of Augsburg

2019, 2020????? Visiting professor, Leipzig 伟德国际_伟德国际1946$娱乐app游戏

2017 - 2022???? Research group leader, Max Planck Institute MiS Leipzig

2014 - 2017???? Bonn Junior Fellow professorship, 伟德国际_伟德国际1946$娱乐app游戏 of Bonn

2013 - 2014???? Research associate, EPF Lausanne

2013??????????????? Dissertation in Mathematics, TU Berlin

2008 - 2013???? Research associate, TU Berlin

2008??????????????? Diploma in Mathematics, TU Berlin

Research Topics

  • 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

Publications

Publication list at Google Scholar
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Preprints

Daniel Kressner, David Persson, André Uschmajew
On the randomized SVD in infinite dimensions
arXiv:2506.06882 (2025)

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Fulvio Gesmundo, Alexandros Grosdos, André Uschmajew
Identifiability through special linear measurements
arXiv:2505.24328 (2025)

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Guillaume Olikier, Petar Mlinari?, P. -A. Absil, André Uschmajew
The tangent cone to the real determinantal variety: various expressions and a proof
arXiv:2504.11382 (2025)

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André Uschmajew and Andreas Zeiser
Discontinuous Galerkin discretization of conservative dynamical low-rank approximation schemes for the Vlasov-Poisson equation
arXiv:2503.10562 (2025)

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Tasuku Soma and André Uschmajew
Accelerating operator Sinkhorn iteration with overrelaxation
arXiv:2410.14104 (2024)

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Journal articles, book chapters, proceedings

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

BibTeX | RIS | DOI

Guillaume Olikier, André Uschmajew and Bart Vandereycken
Gauss–Southwell type descent methods for low-rank matrix optimization

PDF | BibTeX | RIS | DOI

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

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André Uschmajew and Andreas Zeiser
Dynamical low-rank approximation of the Vlasov–Poisson equation with piecewise linear spatial boundary

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 | DOI

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

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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

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

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

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

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

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

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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

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

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

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

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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

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