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2020 – today
- 2024
[j19]Foivos Alimisis
, Bart Vandereycken:
Geodesic Convexity of the Symmetric Eigenvalue Problem and Convergence of Steepest Descent. J. Optim. Theory Appl. 203(1): 920-959 (2024)- [j18]Marco Sutti, Bart Vandereycken:
Implicit Low-Rank Riemannian Schemes for the Time Integration of Stiff Partial Differential Equations. J. Sci. Comput. 101(1): 3 (2024) - [j17]Stefan Güttel, Daniel Kressner, Bart Vandereycken:
Randomized Sketching of Nonlinear Eigenvalue Problems. SIAM J. Sci. Comput. 46(5): 3022- (2024) - 2023
- [j16]Daniel Kressner, Bart Vandereycken, Rik Voorhaar:
Streaming Tensor Train Approximation. SIAM J. Sci. Comput. 45(5): 2610- (2023) - [i12]Marco Sutti, Bart Vandereycken:
Implicit low-rank Riemannian schemes for the time integration of stiff partial differential equations. CoRR abs/2305.11532 (2023) - [i11]Guillaume Olikier, André Uschmajew, Bart Vandereycken:
Gauss-Southwell type descent methods for low-rank matrix optimization. CoRR abs/2306.00897 (2023) - [i10]Foivos Alimisis, Yousef Saad, Bart Vandereycken:
Gradient-type subspace iteration methods for the symmetric eigenvalue problem. CoRR abs/2306.10379 (2023) - [i9]Benjamin Carrel, Bart Vandereycken:
Projected exponential methods for stiff dynamical low-rank approximation problems. CoRR abs/2312.00172 (2023) - 2022
- [j15]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) - [i8]Bart Vandereycken, Rik Voorhaar:
TTML: tensor trains for general supervised machine learning. CoRR abs/2203.04352 (2022) - [i7]Benjamin Carrel, Martin J. Gander, Bart Vandereycken:
Low-rank Parareal: a low-rank parallel-in-time integrator. CoRR abs/2203.08455 (2022) - [i6]Shixin Zheng, Wen Huang, Bart Vandereycken, Xiangxiong Zhang:
Riemannian optimization using three different metrics for Hermitian PSD fixed-rank constraints: an extended version. CoRR abs/2204.07830 (2022) - [i5]Daniel Kressner, Bart Vandereycken, Rik Voorhaar:
Streaming Tensor Train Approximation. CoRR abs/2208.02600 (2022) - [i4]Stefan Güttel, Daniel Kressner, Bart Vandereycken:
Randomized sketching of nonlinear eigenvalue problems. CoRR abs/2211.12175 (2022) - 2021
- [j14]Marco Sutti, Bart Vandereycken:
Riemannian Multigrid Line Search for Low-Rank Problems. SIAM J. Sci. Comput. 43(3): A1803-A1831 (2021) - [c2]Foivos Alimisis, Peter Davies, Bart Vandereycken, Dan Alistarh:
Distributed Principal Component Analysis with Limited Communication. NeurIPS 2021: 2823-2834 - [i3]Foivos Alimisis, Peter Davies, Bart Vandereycken, Dan Alistarh:
Distributed Principal Component Analysis with Limited Communication. CoRR abs/2110.14391 (2021) - 2020
- [i2]Marco Sutti, Bart Vandereycken:
Multilevel Riemannian optimization for low-rank problems. CoRR abs/2005.06976 (2020) - [i1]Marco Sutti, Bart Vandereycken:
The leapfrog algorithm as nonlinear Gauss-Seidel. CoRR abs/2010.14137 (2020)
2010 – 2019
- 2019
- [j13]Emil Kieri, Bart Vandereycken:
Projection Methods for Dynamical Low-Rank Approximation of High-Dimensional Problems. Comput. Methods Appl. Math. 19(1): 73-92 (2019) - [j12]Francesco Borgioli, Wim Michiels, Ding Lu, Bart Vandereycken:
A globally convergent method to compute the real stability radius for time-delay systems. Syst. Control. Lett. 127: 44-51 (2019) - 2018
- [j11]Daniel Kressner, Ding Lu, Bart Vandereycken:
Subspace Acceleration for the Crawford Number and Related Eigenvalue Optimization Problems. SIAM J. Matrix Anal. Appl. 39(2): 961-982 (2018) - [j10]Christian Lubich, Bart Vandereycken, Hanna Walach:
Time Integration of Rank-Constrained Tucker Tensors. SIAM J. Numer. Anal. 56(3): 1273-1290 (2018) - [j9]Zhaojun Bai, Ding Lu, Bart Vandereycken:
Robust Rayleigh Quotient Minimization and Nonlinear Eigenvalue Problems. SIAM J. Sci. Comput. 40(5): A3495-A3522 (2018) - 2017
- [j8]Ding Lu, Bart Vandereycken:
Criss-Cross Type Algorithms for Computing the Real Pseudospectral Abscissa. SIAM J. Matrix Anal. Appl. 38(3): 891-923 (2017) - 2016
- [j7]Daniel Kressner, Michael Steinlechner, Bart Vandereycken:
Preconditioned Low-rank Riemannian Optimization for Linear Systems with Tensor Product Structure. SIAM J. Sci. Comput. 38(4) (2016) - 2015
- [j6]Christian Lubich, Ivan V. Oseledets, Bart Vandereycken:
Time Integration of Tensor Trains. SIAM J. Numer. Anal. 53(2): 917-941 (2015) - 2014
- [j5]Daniel Kressner, Bart Vandereycken:
Subspace Methods for Computing the Pseudospectral Abscissa and the Stability Radius. SIAM J. Matrix Anal. Appl. 35(1): 292-313 (2014) - [c1]Mingkui Tan, Ivor W. Tsang, Li Wang, Bart Vandereycken, Sinno Jialin Pan:
Riemannian Pursuit for Big Matrix Recovery. ICML 2014: 1539-1547 - 2013
- [j4]Bart Vandereycken:
Low-Rank Matrix Completion by Riemannian Optimization. SIAM J. Optim. 23(2): 1214-1236 (2013) - [j3]Christian Lubich, Thorsten Rohwedder, Reinhold Schneider, Bart Vandereycken:
Dynamical Approximation by Hierarchical Tucker and Tensor-Train Tensors. SIAM J. Matrix Anal. Appl. 34(2): 470-494 (2013) - 2010
- [j2]Bart Vandereycken, Stefan Vandewalle:
A Riemannian Optimization Approach for Computing Low-Rank Solutions of Lyapunov Equations. SIAM J. Matrix Anal. Appl. 31(5): 2553-2579 (2010)
2000 – 2009
- 2009
- [j1]Joris Vanbiervliet, Bart Vandereycken, Wim Michiels, Stefan Vandewalle, Moritz Diehl:
The Smoothed Spectral Abscissa for Robust Stability Optimization. SIAM J. Optim. 20(1): 156-171 (2009)
Coauthor Index
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