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2020 – today
- 2024
[j34]Rudolf Schill
, Maren Klever, Andreas Lösch, Y. Linda Hu, Stefan Vocht, Kevin Rupp, Lars Grasedyck, Rainer Spang, Niko Beerenwinkel:
Correcting for Observation Bias in Cancer Progression Modeling. J. Comput. Biol. 31(10): 927-945 (2024)- [j33]Simon Pfahler, Peter Georg, Rudolf Schill, Maren Klever, Lars Grasedyck, Rainer Spang, Tilo Wettig:
Taming numerical imprecision by adapting the KL divergence to negative probabilities. Stat. Comput. 34(5): 168 (2024) - [c1]Rudolf Schill, Maren Klever, Andreas Lösch, Y. Linda Hu, Stefan Vocht, Kevin Rupp, Lars Grasedyck, Rainer Spang, Niko Beerenwinkel:
Overcoming Observation Bias for Cancer Progression Modeling. RECOMB 2024: 217-234 - 2022
- [j32]Paul B. Rohrbach, Sergey Dolgov, Lars Grasedyck, Robert Scheichl:
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format. SIAM/ASA J. Uncertain. Quantification 10(1): 1191-1224 (2022) - 2021
- [i6]Kevin Rupp, Rudolf Schill, Jonas Süskind, Peter Georg, Maren Klever, Andreas Lösch, Lars Grasedyck, Tilo Wettig, Rainer Spang:
Differentiated uniformization: A new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models. CoRR abs/2112.10971 (2021) - 2020
- [i5]Paul B. Rohrbach, Sergey Dolgov, Lars Grasedyck, Robert Scheichl:
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format. CoRR abs/2001.08187 (2020) - [i4]Peter Georg, Lars Grasedyck, Maren Klever, Rudolf Schill, Rainer Spang, Tilo Wettig:
Low-rank tensor methods for Markov chains with applications to tumor progression models. CoRR abs/2006.08135 (2020) - [i3]Lars Grasedyck, Maren Klever, Christian Löbbert, Tim A. Werthmann:
A parameter-dependent smoother for the multigrid method. CoRR abs/2008.00927 (2020) - [i2]Anna Rörich, Tim A. Werthmann, Dominik Göddeke, Lars Grasedyck:
Bayesian inversion for electromyography using low-rank tensor formats. CoRR abs/2009.02772 (2020)
2010 – 2019
- 2019
- [j31]Johannes Vorwerk, Anne Hanrath, Carsten H. Wolters, Lars Grasedyck:
The multipole approach for EEG forward modeling using the finite element method. NeuroImage 201 (2019) - [j30]Lars Grasedyck, Sebastian Krämer:
Stable ALS approximation in the TT-format for rank-adaptive tensor completion. Numerische Mathematik 143(4): 855-904 (2019) - [i1]Lars Grasedyck, Lukas Juschka, Christian Löbbert:
Finding entries of maximum absolute value in low-rank tensors. CoRR abs/1912.02072 (2019) - 2018
- [j29]Peter Benner, Heike Faßbender, Lars Grasedyck, Daniel Kressner, Beatrice Meini, Valeria Simoncini:
7th Workshop on Matrix Equations and Tensor Techniques. Numer. Linear Algebra Appl. 25(6) (2018) - [j28]Lars Grasedyck, Christian Löbbert:
Distributed hierarchical SVD in the Hierarchical Tucker format. Numer. Linear Algebra Appl. 25(6) (2018) - 2016
- [j27]Wolfgang Dahmen, Ronald A. DeVore, Lars Grasedyck, Endre Süli:
Tensor-Sparsity of Solutions to High-Dimensional Elliptic Partial Differential Equations. Found. Comput. Math. 16(4): 813-874 (2016) - [j26]Lars Grasedyck, Lu Wang, Jinchao Xu:
A nearly optimal multigrid method for general unstructured grids. Numerische Mathematik 134(3): 637-666 (2016) - [p1]Lars Grasedyck, Christian Löbbert, Gabriel Wittum, Arne Nägel, Volker Schulz, Martin Siebenborn, Rolf Krause, Pietro Benedusi, Uwe Küster, Björn Dick:
Space and Time Parallel Multigrid for Optimization and Uncertainty Quantification in PDE Simulations. Software for Exascale Computing 2016: 507-523 - 2015
- [j25]Lars Grasedyck, Ronald Kriemann, Christian Löbbert, Arne Nägel, Gabriel Wittum, Konstantinos Xylouris:
Parallel tensor sampling in the hierarchical Tucker format. Comput. Vis. Sci. 17(2): 67-78 (2015) - [j24]Jonas Ballani, Lars Grasedyck:
Hierarchical Tensor Approximation of Output Quantities of Parameter-Dependent PDEs. SIAM/ASA J. Uncertain. Quantification 3(1): 852-872 (2015) - [j23]Lars Grasedyck, Melanie Kluge, Sebastian Krämer:
Variants of Alternating Least Squares Tensor Completion in the Tensor Train Format. SIAM J. Sci. Comput. 37(5) (2015) - 2014
- [j22]Jonas Ballani, Lars Grasedyck:
Tree Adaptive Approximation in the Hierarchical Tensor Format. SIAM J. Sci. Comput. 36(4) (2014) - 2013
- [j21]Peter Gerds, Lars Grasedyck:
Solving an elliptic PDE eigenvalue problem via automated multi-level substructuring and hierarchical matrices. Comput. Vis. Sci. 16(6): 283-302 (2013) - [j20]Jonas Ballani, Lars Grasedyck:
A projection method to solve linear systems in tensor format. Numer. Linear Algebra Appl. 20(1): 27-43 (2013) - 2012
- [j19]Lars Grasedyck, Isabelle Greff, Stefan A. Sauter:
The AL Basis for the Solution of Elliptic Problems in Heterogeneous Media. Multiscale Model. Simul. 10(1): 245-258 (2012) - 2011
- [j18]Lars Grasedyck, Wolfgang Hackbusch:
An Introduction to Hierarchical (H-) Rank and TT-Rank of Tensors with Examples. Comput. Methods Appl. Math. 11(3): 291-304 (2011) - 2010
- [j17]Lars Grasedyck:
Hierarchical Singular Value Decomposition of Tensors. SIAM J. Matrix Anal. Appl. 31(4): 2029-2054 (2010)
2000 – 2009
- 2009
- [j16]Florian Drechsler, Carsten H. Wolters, Thomas Dierkes, Hang Si, Lars Grasedyck:
A full subtraction approach for finite element method based source analysis using constrained Delaunay tetrahedralisation. NeuroImage 46(4): 1055-1065 (2009) - [j15]Lars Grasedyck, Ronald Kriemann, Sabine Le Borne:
Domain decomposition based ℋ-LU preconditioning. Numerische Mathematik 112(4): 565-600 (2009) - 2008
- [j14]Lars Grasedyck:
Nonlinear multigrid for the solution of large-scale Riccati equations in low-rank and ℋ-matrix format. Numer. Linear Algebra Appl. 15(9): 779-807 (2008) - 2007
- [j13]Maxim V. Fedorov, Heinz-Jürgen Flad, Gennady N. Chuev, Lars Grasedyck, Boris N. Khoromskij:
A structured low-rank wavelet solver for the Ornstein-Zernike integral equation. Computing 80(1): 47-73 (2007) - [j12]Lars Grasedyck, Wolfgang Hackbusch:
A Multigrid Method to Solve Large Scale Sylvester Equations. SIAM J. Matrix Anal. Appl. 29(3): 870-894 (2007) - [j11]Ivan G. Graham, Lars Grasedyck, Wolfgang Hackbusch, Stefan A. Sauter:
Optimal Panel-Clustering in the Presence of Anisotropic Mesh Refinement. SIAM J. Numer. Anal. 46(1): 517-543 (2007) - [j10]Carsten H. Wolters, Harald Köstler, Christian Möller, Jochen Härdtlein, Lars Grasedyck, Wolfgang Hackbusch:
Numerical Mathematics of the Subtraction Method for the Modeling of a Current Dipole in EEG Source Reconstruction Using Finite Element Head Models. SIAM J. Sci. Comput. 30(1): 24-45 (2007) - 2006
- [j9]Sabine Le Borne, Lars Grasedyck:
H-matrix Preconditioners in Convection-Dominated Problems. SIAM J. Matrix Anal. Appl. 27(4): 1172-1183 (2006) - 2005
- [j8]Lars Grasedyck:
Adaptive Recompression of H -Matrices for BEM. Computing 74(3): 205-223 (2005) - [j7]Steffen Börm, Lars Grasedyck:
Hybrid cross approximation of integral operators. Numerische Mathematik 101(2): 221-249 (2005) - 2004
- [j6]Lars Grasedyck:
Existence and Computation of Low Kronecker-Rank Approximations for Large Linear Systems of Tensor Product Structure. Computing 72(3-4): 247-265 (2004) - [j5]Steffen Börm, Lars Grasedyck:
Low-Rank Approximation of Integral Operators by Interpolation. Computing 72(3-4): 325-332 (2004) - [j4]Lars Grasedyck, Wolfgang Hackbusch, Sabine Le Borne:
Adaptive Geometrically Balanced Clustering of H-Matrices. Computing 73(1): 1-23 (2004) - [j3]Lars Grasedyck:
Existence of a low rank or ℋ-matrix approximant to the solution of a Sylvester equation. Numer. Linear Algebra Appl. 11(4): 371-389 (2004) - 2003
- [j2]Lars Grasedyck, Wolfgang Hackbusch, Boris N. Khoromskij:
Solution of Large Scale Algebraic Matrix Riccati Equations by Use of Hierarchical Matrices. Computing 70(2): 121-165 (2003) - [j1]Lars Grasedyck, Wolfgang Hackbusch:
Construction and Arithmetics of H-Matrices. Computing 70(4): 295-334 (2003)
Coauthor Index
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last updated on 2024-12-10 20:52 CET by the dblp team
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