A. Uschmajew

发表

Greedy rank updates combined with Riemannian descent methods for low-rank optimization

Bart Vandereycken, A. Uschmajew, André Uschmajew, 2015, 2015 International Conference on Sampling Theory and Applications (SampTA).

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

A. Uschmajew, André Uschmajew, 2014, 1407.4586.

On low-rank approximability of solutions to high-dimensional operator equations and eigenvalue problems

A. Uschmajew, D. Kressner, André Uschmajew, 2014, 1406.7026.

The geometry of algorithms using hierarchical tensors

Bart Vandereycken, A. Uschmajew, André Uschmajew, 2013, Linear Algebra and its Applications.

On critical points of quadratic low-rank matrix optimization problems

André Uschmajew, Bart Vandereycken, Bart Vandereycken, 2020, IMA Journal of Numerical Analysis.

LOCAL CONVERGENCE OF ALTERNATING SCHEMES FOR OPTIMIZATION OF CONVEX PROBLEMS IN THE TT FORMAT

A. Uschmajew, T. Rohwedder, T. Rohwedder, 2011 .

On orthogonal tensors and best rank-one approximation ratio pdf

André Uschmajew, Zhening Li, Yuji Nakatsukasa, 2017, SIAM J. Matrix Anal. Appl..

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

A. Uschmajew, André Uschmajew, 2015, 2015 International Conference on Sampling Theory and Applications (SampTA).

Kronecker Product Approximation of Operators in Spectral Norm via Alternating SDP

V. Chandrasekaran, A. Uschmajew, Mareike Dressler, 2022, ArXiv.

Maximum relative distance between real rank-two and rank-one tensors

A. Uschmajew, Henrik Eisenmann, André Uschmajew, 2021, Annali di Matematica Pura ed Applicata (1923 -).

Maximum relative distance between symmetric rank-two and rank-one tensors

A. Uschmajew, Henrik Eisenmann, André Uschmajew, 2021 .

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

Jakub Marecek, A. Uschmajew, V. Kungurtsev, 2022, ArXiv.

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

F. Krahmer, A. Uschmajew, Max Pfeffer, 2021, Numerical Algorithms.

Finding a low-rank basis in a matrix subspace

Y. Nakatsukasa, A. Uschmajew, Tasuku Soma, 2016, Mathematical Programming.

Local convergence of alternating low‐rank optimization methods with overrelaxation

A. Uschmajew, I. Oseledets, M. Rakhuba, 2021, Numer. Linear Algebra Appl..

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

A. Uschmajew, M. Bachmayr, R. Schneider, 2016, Foundations of Computational Mathematics.

Modified Iterations for Data-Sparse Solution of Linear Systems

W. Hackbusch, A. Uschmajew, 2021, Vietnam Journal of Mathematics.

Bridging the gap between quantum Monte Carlo and F12-methods

André Uschmajew, Wolfgang Hackbusch, Heinz-Jürgen Flad, 2012 .

Finding a low-rank basis in a matrix subspace

André Uschmajew, Yuji Nakatsukasa, Tasuku Soma, 2015, Math. Program..

Tangent and Normal Cones for Low-Rank Matrices

André Uschmajew, D. Russell Luke, Seyedehsomayeh Hosseini, 2019, Nonsmooth Optimization and Its Applications.

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

A. Uschmajew, A. Zeiser, 2023, ArXiv.

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

Reinhold Schneider, André Uschmajew, Markus Bachmayr, 2016, Foundations of Computational Mathematics.

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

André Uschmajew, A. Uschmajew, 2012, SIAM J. Matrix Anal. Appl..

Perturbation of Higher-Order Singular Values

André Uschmajew, Wolfgang Hackbusch, Daniel Kressner, 2017, SIAM J. Appl. Algebra Geom..

A Riemannian Gradient Sampling Algorithm for Nonsmooth Optimization on Manifolds

André Uschmajew, Seyedehsomayeh Hosseini, A. Uschmajew, 2017, SIAM J. Optim..

Geometric Methods on Low-Rank Matrix and Tensor Manifolds

André Uschmajew, Bart Vandereycken, Bart Vandereycken, 2020 .

Existence of dynamical low-rank approximations to parabolic problems

A. Uschmajew, M. Bachmayr, Emil Kieri, 2020, Math. Comput..

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

Daniel Kressner, André Uschmajew, Michael Steinlechner, 2014, SIAM J. Sci. Comput..

Parallel algorithms for tensor completion in the CP format

Lars Karlsson, Daniel Kressner, André Uschmajew, 2016, Parallel Comput..

Approximation rates for the hierarchical tensor format in periodic Sobolev spaces

Reinhold Schneider, André Uschmajew, A. Uschmajew, 2014, J. Complex..

Modified Iterations for Data-Sparse Solution of Linear Systems

André Uschmajew, Wolfgang Hackbusch, W. Hackbusch, 2021, Vietnam Journal of Mathematics.

On Convergence of the Maximum Block Improvement Method

André Uschmajew, Shuzhong Zhang, Zhening Li, 2015, SIAM J. Optim..

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

Reinhold Schneider, André Uschmajew, A. Uschmajew, 2014, SIAM J. Optim..

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

André Uschmajew, Thorsten Rohwedder, A. Uschmajew, 2013, SIAM J. Numer. Anal..

The geometry of algorithms using hierarchical

André Uschmajew, Bart Vandereycken, Bart Vandereycken, 2013 .

Data Fusion Techniques for the Integration of Multi-Domain Genomic Data from Uveal Melanoma

André Uschmajew, Max Pfeffer, Adriana Amaro, 2019, Cancers.

Computing Eigenspaces With Low Rank Constraints

Max Pfeffer, Christian Krumnow, André Uschmajew, 2019, SIAM J. Sci. Comput..

Chebyshev Polynomials and Best Rank-one Approximation Ratio

Khazhgali Kozhasov, Andrei Agrachev, Andr'e Uschmajew, 2019, SIAM J. Matrix Anal. Appl..

A note on overrelaxation in the Sinkhorn algorithm

A. Uschmajew, M. Renesse, Tobias Lehmann, 2020, Optimization Letters.

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

Danilo P Mandic, Andrzej Cichocki, George Luta, 2020, IEEE Transactions on Neural Networks and Learning Systems.

Data Fusion Techniques for the Integration of Multi-Domain Genomic Data from Uveal Melanoma

A. Uschmajew, A. Amaro, Max Pfeffer, 2019, Cancers.

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

Andrzej Cichocki, Danilo P. Mandic, André Uschmajew, 2016, ArXiv.

Regularity of Tensor Product Approximations to Square Integrable Functions

André Uschmajew, A. Uschmajew, 2011 .

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

André Uschmajew, A. Uschmajew, 2010, Numerische Mathematik.

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

A. Uschmajew, André Uschmajew, 2012, SIAM J. Matrix Anal. Appl..

On the approximation of vector-valued functions by samples

A. Uschmajew, D. Kressner, Tingting Ni, 2023, ArXiv.