André Uschmajew

According to our database, André Uschmajew authored at least 34 papers between 2010 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

On the approximation of vector-valued functions by volume sampling.

Daniel Kressner, Tingting Ni, André Uschmajew

J. Complex., 2025

2024

Time-Varying Semidefinite Programming: Path Following a Burer-Monteiro Factorization.

Antonio Bellon, Mareike Dressler, Vyacheslav Kungurtsev, Jakub Marecek, André Uschmajew

SIAM J. Optim., March, 2024

2023

Kronecker Product Approximation of Operators in Spectral Norm via Alternating SDP.

Mareike Dressler, André Uschmajew, Venkat Chandrasekaran

SIAM J. Matrix Anal. Appl., December, 2023

Riemannian thresholding methods for row-sparse and low-rank matrix recovery.

Henrik Eisenmann, Felix Krahmer, Max Pfeffer, André Uschmajew

Numer. Algorithms, June, 2023

Local convergence of alternating low-rank optimization methods with overrelaxation.

Ivan V. Oseledets, Maxim V. Rakhuba, André Uschmajew

Numer. Linear Algebra Appl., May, 2023

Dynamical low-rank tensor approximations to high-dimensional parabolic problems: existence and convergence of spatial discretizations.

Markus Bachmayr, Henrik Eisenmann, André Uschmajew

CoRR, 2023

Gauss-Southwell type descent methods for low-rank matrix optimization.

Guillaume Olikier, André Uschmajew, Bart Vandereycken

CoRR, 2023

On the approximation of vector-valued functions by samples.

Daniel Kressner, Tingting Ni, André Uschmajew

CoRR, 2023

Dynamical low-rank approximation of the Vlasov-Poisson equation with piecewise linear spatial boundary.

André Uschmajew, Andreas Zeiser

CoRR, 2023

2022

A note on overrelaxation in the Sinkhorn algorithm.

Tobias Lehmann, Max-K. von Renesse, Alexander Sambale, André Uschmajew

Optim. Lett., 2022

A Note on the Optimal Convergence Rate of Descent Methods with Fixed Step Sizes for Smooth Strongly Convex Functions.

André Uschmajew, Bart Vandereycken

J. Optim. Theory Appl., 2022

Editorial: High-performance tensor computations in scientific computing and data science.

Edoardo Di Napoli, Paolo Bientinesi, Jiajia Li, André Uschmajew

Frontiers Appl. Math. Stat., 2022

2021

Computing Eigenspaces With Low Rank Constraints.

Christian Krumnow, Max Pfeffer, André Uschmajew

SIAM J. Sci. Comput., 2021

Existence of dynamical low-rank approximations to parabolic problems.

Markus Bachmayr, Henrik Eisenmann, Emil Kieri, André Uschmajew

Math. Comput., 2021

2020

Tensor Networks for Latent Variable Analysis: Novel Algorithms for Tensor Train Approximation.

Anh-Huy Phan, Andrzej Cichocki, André Uschmajew, Petr Tichavský, George Luta, Danilo P. Mandic

IEEE Trans. Neural Networks Learn. Syst., 2020

Chebyshev Polynomials and Best Rank-one Approximation Ratio.

Andrei A. Agrachev, Khazhgali Kozhasov, André Uschmajew

SIAM J. Matrix Anal. Appl., 2020

Existence of dynamical low-rank approximations to parabolic problems.

Markus Bachmayr, Emil Kieri, André Uschmajew

CoRR, 2020

2019

A Gradient Sampling Method on Algebraic Varieties and Application to Nonsmooth Low-Rank Optimization.

Seyedehsomayeh Hosseini, André Uschmajew

SIAM J. Optim., 2019

2018

Alternating Least Squares as Moving Subspace Correction.

Ivan V. Oseledets, Maxim V. Rakhuba, André Uschmajew

SIAM J. Numer. Anal., 2018

On Orthogonal Tensors and Best Rank-One Approximation Ratio.

Zhening Li, Yuji Nakatsukasa, Tasuku Soma, André Uschmajew

SIAM J. Matrix Anal. Appl., 2018

2017

A Riemannian Gradient Sampling Algorithm for Nonsmooth Optimization on Manifolds.

Seyedehsomayeh Hosseini, André Uschmajew

SIAM J. Optim., 2017

Perturbation of Higher-Order Singular Values.

Wolfgang Hackbusch, Daniel Kressner, André Uschmajew

SIAM J. Appl. Algebra Geom., 2017

On the interconnection between the higher-order singular values of real tensors.

Wolfgang Hackbusch, André Uschmajew

Numerische Mathematik, 2017

Finding a low-rank basis in a matrix subspace.

Yuji Nakatsukasa, Tasuku Soma, André Uschmajew

Math. Program., 2017

2016

Parallel algorithms for tensor completion in the CP format.

Lars Karlsson, Daniel Kressner, André Uschmajew

Parallel Comput., 2016

Tensor Networks and Hierarchical Tensors for the Solution of High-Dimensional Partial Differential Equations.

Markus Bachmayr, Reinhold Schneider, André Uschmajew

Found. Comput. Math., 2016

Tensor Networks for Latent Variable Analysis. Part I: Algorithms for Tensor Train Decomposition.

Anh Huy Phan, Andrzej Cichocki, André Uschmajew, Petr Tichavský, Gheorghe Luta, Danilo P. Mandic

CoRR, 2016

2015

Convergence Results for Projected Line-Search Methods on Varieties of Low-Rank Matrices Via Łojasiewicz Inequality.

Reinhold Schneider, André Uschmajew

SIAM J. Optim., 2015

On Convergence of the Maximum Block Improvement Method.

Zhening Li, André Uschmajew, Shuzhong Zhang

SIAM J. Optim., 2015

2014

Low-Rank Tensor Methods with Subspace Correction for Symmetric Eigenvalue Problems.

Daniel Kressner, Michael Steinlechner, André Uschmajew

SIAM J. Sci. Comput., 2014

Approximation rates for the hierarchical tensor format in periodic Sobolev spaces.

Reinhold Schneider, André Uschmajew

J. Complex., 2014

2013

On Local Convergence of Alternating Schemes for Optimization of Convex Problems in the Tensor Train Format.

Thorsten Rohwedder, André Uschmajew

SIAM J. Numer. Anal., 2013

2012

Local Convergence of the Alternating Least Squares Algorithm for Canonical Tensor Approximation.

André Uschmajew

SIAM J. Matrix Anal. Appl., 2012

2010

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

André Uschmajew

Numerische Mathematik, 2010