Well-posedness of convex maximization problems on Stiefel manifolds and orthogonal tensor product approximations. - André Uschmajew (Numerische Mathematik, 2010)

Local Convergence of the Alternating Least Squares Algorithm for Canonical Tensor Approximation. - André Uschmajew (SIAM J. Matrix Anal. Appl., 2012)

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)

Approximation rates for the hierarchical tensor format in periodic Sobolev spaces. - Reinhold Schneider; André Uschmajew (J. Complex., 2014)

Low-Rank Tensor Methods with Subspace Correction for Symmetric Eigenvalue Problems. - Daniel Kressner; Michael Steinlechner; André Uschmajew (SIAM J. Sci. Comput., 2014)

On Convergence of the Maximum Block Improvement Method. - Zhening Li; André Uschmajew; Shuzhong Zhang (SIAM J. Optim., 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)

Tensor Networks for Latent Variable Analysis. Part I: Algorithms for Tensor Train Decomposition. - Anh Huy Phan 001; Andrzej Cichocki; André Uschmajew; Petr Tichavský; Gheorghe Luta; Danilo P. Mandic (CoRR, 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)

Parallel algorithms for tensor completion in the CP format. - Lars Karlsson; Daniel Kressner; André Uschmajew (Parallel Comput., 2016)

Finding a low-rank basis in a matrix subspace. - Yuji Nakatsukasa; Tasuku Soma; André Uschmajew (Math. Program., 2017)

On the interconnection between the higher-order singular values of real tensors. - Wolfgang Hackbusch; André Uschmajew (Numerische Mathematik, 2017)

Perturbation of Higher-Order Singular Values. - Wolfgang Hackbusch; Daniel Kressner; André Uschmajew (SIAM J. Appl. Algebra Geom., 2017)

A Riemannian Gradient Sampling Algorithm for Nonsmooth Optimization on Manifolds. - Seyedehsomayeh Hosseini; André Uschmajew (SIAM J. Optim., 2017)

On Orthogonal Tensors and Best Rank-One Approximation Ratio. - Zhening Li; Yuji Nakatsukasa; Tasuku Soma; André Uschmajew (SIAM J. Matrix Anal. Appl., 2018)

Alternating Least Squares as Moving Subspace Correction. - Ivan V. Oseledets; Maxim V. Rakhuba; André Uschmajew (SIAM J. Numer. Anal., 2018)

A Gradient Sampling Method on Algebraic Varieties and Application to Nonsmooth Low-Rank Optimization. - Seyedehsomayeh Hosseini; André Uschmajew (SIAM J. Optim., 2019)

Existence of dynamical low-rank approximations to parabolic problems. - Markus Bachmayr; Emil Kieri; André Uschmajew (CoRR, 2020)

Chebyshev Polynomials and Best Rank-one Approximation Ratio. - Andrei A. Agrachev; Khazhgali Kozhasov; André Uschmajew (SIAM J. Matrix Anal. Appl., 2020)

Tensor Networks for Latent Variable Analysis: Novel Algorithms for Tensor Train Approximation. - Anh-Huy Phan 001; Andrzej Cichocki; André Uschmajew; Petr Tichavský; George Luta; Danilo P. Mandic (IEEE Trans. Neural Networks Learn. Syst., 2020)

Existence of dynamical low-rank approximations to parabolic problems. - Markus Bachmayr; Henrik Eisenmann; Emil Kieri; André Uschmajew (Math. Comput., 2021)

Computing Eigenspaces With Low Rank Constraints. - Christian Krumnow; Max Pfeffer; André Uschmajew (SIAM J. Sci. Comput., 2021)

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)

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)

A note on overrelaxation in the Sinkhorn algorithm. - Tobias Lehmann; Max-K. von Renesse; Alexander Sambale; André Uschmajew (Optim. Lett., 2022)

Dynamical low-rank approximation of the Vlasov-Poisson equation with piecewise linear spatial boundary. - André Uschmajew; Andreas Zeiser (CoRR, 2023)

On the approximation of vector-valued functions by samples. - Daniel Kressner; Tingting Ni; André Uschmajew (CoRR, 2023)

Local convergence of alternating low-rank optimization methods with overrelaxation. - Ivan V. Oseledets; Maxim V. Rakhuba; André Uschmajew (Numer. Linear Algebra Appl., 2023)

Riemannian thresholding methods for row-sparse and low-rank matrix recovery. - Henrik Eisenmann; Felix Krahmer; Max Pfeffer; André Uschmajew (Numer. Algorithms, 2023)

Kronecker Product Approximation of Operators in Spectral Norm via Alternating SDP. - Mareike Dressler; André Uschmajew; Venkat Chandrasekaran (SIAM J. Matrix Anal. Appl., 2023)

Accelerating operator Sinkhorn iteration with overrelaxation. - Tasuku Soma; André Uschmajew (CoRR, 2024)

Time-Varying Semidefinite Programming: Path Following a Burer-Monteiro Factorization. - Antonio Bellon; Mareike Dressler; Vyacheslav Kungurtsev; Jakub Marecek; André Uschmajew (SIAM J. Optim., 2024)

On the approximation of vector-valued functions by volume sampling. - Daniel Kressner; Tingting Ni; André Uschmajew (J. Complex., 2025)

Discontinuous Galerkin discretization of conservative dynamical low-rank approximation schemes for the Vlasov-Poisson equation. - André Uschmajew; Andreas Zeiser (CoRR, 2025)

Identifiability through special linear measurements. - Fulvio Gesmundo; Alexandros Grosdos; André Uschmajew (CoRR, 2025)

On the randomized SVD in infinite dimensions. - Daniel Kressner; David Persson; André Uschmajew (CoRR, 2025)

Dynamical low-rank tensor approximations to high-dimensional parabolic problems: existence and convergence of spatial discretizations. - Markus Bachmayr; Henrik Eisenmann; André Uschmajew (Numerische Mathematik, 2025)

Gauss-Southwell Type Descent Methods for Low-Rank Matrix Optimization. - Guillaume Olikier; André Uschmajew; Bart Vandereycken (J. Optim. Theory Appl., 2025)