| A Sparse ℋ-Matrix Arithmetic. W Hackbusch, BN Khoromskij Computing 64 (1), 21-47, 2000 | 1857* | 2000 |
| O(dlog N)-Quantics Approximation of N-d Tensors in High-Dimensional Numerical Modeling BN Khoromskij Constructive Approximation 34 (2), 257-280, 2011 | 416 | 2011 |
| Tensors-structured numerical methods in scientific computing: Survey on recent advances BN Khoromskij Chemometrics and intelligent laboratory systems 110 (1), 1-19, 2012 | 267 | 2012 |
| Tensor-structured Galerkin approximation of parametric and stochastic elliptic PDEs BN Khoromskij, C Schwab SIAM journal on scientific computing 33 (1), 364-385, 2011 | 235 | 2011 |
| Fast solution of parabolic problems in the tensor train/quantized tensor train format with initial application to the Fokker--Planck equation SV Dolgov, BN Khoromskij, IV Oseledets SIAM Journal on Scientific Computing 34 (6), A3016-A3038, 2012 | 190* | 2012 |
| Low-rank explicit QTT representation of the Laplace operator and its inverse VA Kazeev, BN Khoromskij SIAM journal on matrix analysis and applications 33 (3), 742-758, 2012 | 186 | 2012 |
| Hierarchical Kronecker Tensor Product Approximations W Hackbusch, BN Khoromskij, EE Tyrtyshnikov J. Numer. Math. 13 (2), 119-156, 2005 | 178 | 2005 |
| Low-rank Kronecker-product approximation to multi-dimensional nonlocal operators. Part I. Separable approximation of multi-variate functions W Hackbusch, BN Khoromskij Computing 76 (3), 177-202, 2006 | 175 | 2006 |
| Multigrid accelerated tensor approximation of function related multidimensional arrays BN Khoromskij, V Khoromskaia SIAM Journal on Scientific Computing 31 (4), 3002-3026, 2009 | 154 | 2009 |
| Hierarchical matrices based on a weak admissibility criterion W Hackbusch, BN Khoromskij, R Kriemann Computing 73 (3), 207-243, 2004 | 153 | 2004 |
| Computation of extreme eigenvalues in higher dimensions using block tensor train format SV Dolgov, BN Khoromskij, IV Oseledets, DV Savostyanov Computer Physics Communications 185 (4), 1207-1216, 2014 | 141 | 2014 |
| Tensor Numerical Methods in Scientific Computing BN Khoromskij Walter de Gruyter, 2018 | 140 | 2018 |
| Solution of large scale algebraic matrix Riccati equations by use of hierarchical matrices L Grasedyck, W Hackbusch, BN Khoromskij Computing 70 (2), 121-165, 2003 | 135 | 2003 |
| Approximate iterations for structured matrices W Hackbusch, BN Khoromskij, EE Tyrtyshnikov Numerische Mathematik 109 (3), 365-383, 2008 | 134 | 2008 |
| Simultaneous state‐time approximation of the chemical master equation using tensor product formats S Dolgov, B Khoromskij Numerical Linear Algebra with Applications 22 (2), 197-219, 2015 | 131* | 2015 |
| Multilevel Toeplitz matrices generated by tensor-structured vectors and convolution with logarithmic complexity VA Kazeev, BN Khoromskij, EE Tyrtyshnikov SIAM Journal on Scientific Computing 35 (3), A1511-A1536, 2013 | 131 | 2013 |
| A sparse H-matrix arithmetic: general complexity estimates W Hackbusch, BN Khoromskij Journal of computational and applied mathematics 125 (1-2), 479-501, 2000 | 130 | 2000 |
| Low rank Tucker-type tensor approximation to classical potentials B Khoromskij, V Khoromskaia Open Mathematics 5 (3), 523-550, 2007 | 126 | 2007 |
| Polynomial chaos expansion of random coefficients and the solution of stochastic partial differential equations in the tensor train format S Dolgov, BN Khoromskij, A Litvinenko, HG Matthies SIAM/ASA Journal on Uncertainty Quantification 3 (1), 1109-1135, 2015 | 124* | 2015 |
| Application of hierarchical matrices for computing the Karhunen–Loève expansion BN Khoromskij, A Litvinenko, HG Matthies Computing 84 (1), 49-67, 2009 | 123 | 2009 |
Venera KhoromskaiaSenior Scientist, Max-Planck Institute for Mathematics in the SciencesVerified email at mis.mpg.de
