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André Uschmajew
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2020 – today
- 2025
- [j28]Daniel Kressner, Tingting Ni, André Uschmajew:
On the approximation of vector-valued functions by volume sampling. J. Complex. 86: 101887 (2025) - 2024
- [j27]Antonio Bellon, Mareike Dressler
, Vyacheslav Kungurtsev, Jakub Marecek, André Uschmajew:
Time-Varying Semidefinite Programming: Path Following a Burer-Monteiro Factorization. SIAM J. Optim. 34(1): 1-26 (2024) - [i15]Tasuku Soma, André Uschmajew:
Accelerating operator Sinkhorn iteration with overrelaxation. CoRR abs/2410.14104 (2024) - 2023
- [j26]Henrik Eisenmann
Riemannian thresholding methods for row-sparse and low-rank matrix recovery. Numer. Algorithms 93(2): 669-693 (2023) - [j25]Ivan V. Oseledets, Maxim V. Rakhuba, André Uschmajew:
Local convergence of alternating low-rank optimization methods with overrelaxation. Numer. Linear Algebra Appl. 30(3) (2023) - [j24]Mareike Dressler
Kronecker Product Approximation of Operators in Spectral Norm via Alternating SDP. SIAM J. Matrix Anal. Appl. 44(4): 1693-1708 (2023) - [i14]André Uschmajew, Andreas Zeiser:
Dynamical low-rank approximation of the Vlasov-Poisson equation with piecewise linear spatial boundary. CoRR abs/2303.01856 (2023) - [i13]Daniel Kressner, Tingting Ni, André Uschmajew:
On the approximation of vector-valued functions by samples. CoRR abs/2304.03212 (2023) - [i12]Guillaume Olikier, André Uschmajew, Bart Vandereycken:
Gauss-Southwell type descent methods for low-rank matrix optimization. CoRR abs/2306.00897 (2023) - [i11]Markus Bachmayr, Henrik Eisenmann, André Uschmajew:
Dynamical low-rank tensor approximations to high-dimensional parabolic problems: existence and convergence of spatial discretizations. CoRR abs/2308.16720 (2023) - 2022
- [j23]Edoardo Di Napoli, Paolo Bientinesi, Jiajia Li, André Uschmajew:
Editorial: High-performance tensor computations in scientific computing and data science. Frontiers Appl. Math. Stat. 8 (2022) - [j22]André Uschmajew, Bart Vandereycken
A Note on the Optimal Convergence Rate of Descent Methods with Fixed Step Sizes for Smooth Strongly Convex Functions. J. Optim. Theory Appl. 194(1): 364-373 (2022) - [j21]Tobias Lehmann, Max-K. von Renesse, Alexander Sambale, André Uschmajew:
A note on overrelaxation in the Sinkhorn algorithm. Optim. Lett. 16(8): 2209-2220 (2022) - [i10]Mareike Dressler, André Uschmajew, Venkat Chandrasekaran:
Kronecker Product Approximation of Operators in Spectral Norm via Alternating SDP. CoRR abs/2207.03186 (2022) - [i9]Antonio Bellon, Mareike Dressler, Vyacheslav Kungurtsev
Time-Varying Semidefinite Programming: Path Following a Burer-Monteiro Factorization. CoRR abs/2210.08387 (2022) - 2021
- [j20]Markus Bachmayr, Henrik Eisenmann, Emil Kieri, André Uschmajew:
Existence of dynamical low-rank approximations to parabolic problems. Math. Comput. 90(330): 1799-1830 (2021) - [j19]Christian Krumnow, Max Pfeffer
Computing Eigenspaces With Low Rank Constraints. SIAM J. Sci. Comput. 43(1): A586-A608 (2021) - [i8]Henrik Eisenmann, Felix Krahmer, Max Pfeffer
Riemannian thresholding methods for row-sparse and low-rank matrix recovery. CoRR abs/2103.02356 (2021) - [i7]Ivan V. Oseledets, Maxim V. Rakhuba, André Uschmajew:
Local convergence of alternating low-rank optimization methods with overrelaxation. CoRR abs/2111.14758 (2021) - 2020
- [j18]Andrei A. Agrachev, Khazhgali Kozhasov, André Uschmajew:
Chebyshev Polynomials and Best Rank-one Approximation Ratio. SIAM J. Matrix Anal. Appl. 41(1): 308-331 (2020) - [j17]Anh-Huy Phan
Tensor Networks for Latent Variable Analysis: Novel Algorithms for Tensor Train Approximation. IEEE Trans. Neural Networks Learn. Syst. 31(11): 4622-4636 (2020) - [i6]Markus Bachmayr, Emil Kieri, André Uschmajew:
Existence of dynamical low-rank approximations to parabolic problems. CoRR abs/2002.12197 (2020) - [i5]Tobias Lehmann, Max-K. von Renesse, Alexander Sambale, André Uschmajew:
A note on overrelaxation in the Sinkhorn algorithm. CoRR abs/2012.12562 (2020)
2010 – 2019
- 2019
- [j16]Seyedehsomayeh Hosseini, André Uschmajew:
A Gradient Sampling Method on Algebraic Varieties and Application to Nonsmooth Low-Rank Optimization. SIAM J. Optim. 29(4): 2853-2880 (2019) - 2018
- [j15]Zhening Li
On Orthogonal Tensors and Best Rank-One Approximation Ratio. SIAM J. Matrix Anal. Appl. 39(1): 400-425 (2018) - [j14]Ivan V. Oseledets, Maxim V. Rakhuba, André Uschmajew:
Alternating Least Squares as Moving Subspace Correction. SIAM J. Numer. Anal. 56(6): 3459-3479 (2018) - 2017
- [j13]Yuji Nakatsukasa
Finding a low-rank basis in a matrix subspace. Math. Program. 162(1-2): 325-361 (2017) - [j12]Wolfgang Hackbusch, André Uschmajew:
On the interconnection between the higher-order singular values of real tensors. Numerische Mathematik 135(3): 875-894 (2017) - [j11]Wolfgang Hackbusch, Daniel Kressner, André Uschmajew:
Perturbation of Higher-Order Singular Values. SIAM J. Appl. Algebra Geom. 1(1): 374-387 (2017) - [j10]Seyedehsomayeh Hosseini, André Uschmajew:
A Riemannian Gradient Sampling Algorithm for Nonsmooth Optimization on Manifolds. SIAM J. Optim. 27(1): 173-189 (2017) - [i4]Zhening Li, Yuji Nakatsukasa, Tasuku Soma, André Uschmajew:
On orthogonal tensors and best rank-one approximation ratio. CoRR abs/1707.02569 (2017) - 2016
- [j9]Markus Bachmayr, Reinhold Schneider, André Uschmajew:
Tensor Networks and Hierarchical Tensors for the Solution of High-Dimensional Partial Differential Equations. Found. Comput. Math. 16(6): 1423-1472 (2016) - [j8]Lars Karlsson, Daniel Kressner, André Uschmajew:
Parallel algorithms for tensor completion in the CP format. Parallel Comput. 57: 222-234 (2016) - [i3]Anh Huy Phan, Andrzej Cichocki, André Uschmajew, Petr Tichavský, Gheorghe Luta, Danilo P. Mandic:
Tensor Networks for Latent Variable Analysis. Part I: Algorithms for Tensor Train Decomposition. CoRR abs/1609.09230 (2016) - 2015
- [j7]Zhening Li
On Convergence of the Maximum Block Improvement Method. SIAM J. Optim. 25(1): 210-233 (2015) - [j6]Reinhold Schneider, André Uschmajew:
Convergence Results for Projected Line-Search Methods on Varieties of Low-Rank Matrices Via Łojasiewicz Inequality. SIAM J. Optim. 25(1): 622-646 (2015) - [i2]Yuji Nakatsukasa, Tasuku Soma, André Uschmajew:
Finding a low-rank basis in a matrix subspace. CoRR abs/1503.08601 (2015) - 2014
- [j5]Reinhold Schneider, André Uschmajew:
Approximation rates for the hierarchical tensor format in periodic Sobolev spaces. J. Complex. 30(2): 56-71 (2014) - [j4]Daniel Kressner, Michael Steinlechner, André Uschmajew:
Low-Rank Tensor Methods with Subspace Correction for Symmetric Eigenvalue Problems. SIAM J. Sci. Comput. 36(5) (2014) - [i1]Reinhold Schneider, André Uschmajew:
Convergence results for projected line-search methods on varieties of low-rank matrices via Łojasiewicz inequality. CoRR abs/1402.5284 (2014) - 2013
- [j3]Thorsten Rohwedder, André Uschmajew:
On Local Convergence of Alternating Schemes for Optimization of Convex Problems in the Tensor Train Format. SIAM J. Numer. Anal. 51(2): 1134-1162 (2013) - 2012
- [j2]André Uschmajew:
Local Convergence of the Alternating Least Squares Algorithm for Canonical Tensor Approximation. SIAM J. Matrix Anal. Appl. 33(2): 639-652 (2012) - 2010
- [j1]André Uschmajew:
Well-posedness of convex maximization problems on Stiefel manifolds and orthogonal tensor product approximations. Numerische Mathematik 115(2): 309-331 (2010)
Coauthor Index
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