André Uschmajew

发表

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

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

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..

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

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

Chebyshev Polynomials and Best Rank-one Approximation Ratio

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

The geometry of algorithms using hierarchical

André Uschmajew, Bart Vandereycken, Bart Vandereycken, 2013 .

Computing Eigenspaces With Low Rank Constraints

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

Approximation rates for the hierarchical tensor format in periodic Sobolev spaces

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

The geometry of algorithms using hierarchical tensors

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

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.

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

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

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..

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 .

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).

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

SEYEDEHSOMAYEH HOSSEINI, ANDRÉ USCHMAJEW, S. Hosseini, 2019, SIAM J. Optim..

Parallel algorithms for tensor completion in the CP format

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

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

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

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

A. Uschmajew, André Uschmajew, 2014, 1407.4586.

Bridging the gap between quantum Monte Carlo and F12-methods

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

On the approximation of vector-valued functions by samples

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

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

Bart Vandereycken, André Uschmajew, 2021, Journal of Optimization Theory and Applications.

A note on overrelaxation in the Sinkhorn algorithm pdf

Max-K. von Renesse, Alexander Sambale, Andr'e Uschmajew, 2020, Optimization Letters.

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

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

Existence of dynamical low-rank approximations to parabolic problems

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

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.

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

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

A Riemannian Gradient Sampling Algorithm for Nonsmooth Optimization on Manifolds

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