| 2017 | 2016 | 2015 | 2014 | 2013 |

2017

1. Andrzej Cichocki, Anh-Huy Phan, Qibin Zhao, Namgil Lee, Ivan Oseledets, Masashi Sugiyama, and Danilo Mandic. Tensor networks for dimensionality reduction and large-scale optimization: part 2 applications and future perspectives. Foundations and Trends in Machine Learning, 9(6):431–673, 2017. [ bib ]
2. Grigory Drozdov, Igor Ostanin, and Ivan Oseledets. Time-and memory-efficient representation of complex mesoscale potentials. J. Comp. Phys., 343:110–114, 2017. [ bib ]
3. Alexander Fonarev, Oleksii Hrinchuk, Gleb Gusev, Pavel Serdyukov, and Ivan Oseledets. Riemannian optimization for skip-Gram negative sampling. arXiv preprint 1704.08059, 2017. URL: http://arxiv.org/abs/1704.8059. [ bib ]
4. Evgeny Frolov and Ivan Oseledets. Tensor methods and recommender systems. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2017. [ bib ]
5. Valentin Khrulkov and Ivan Oseledets. Art of singular vectors and universal adversarial perturbations. arXiv preprint 1709.03582, 2017. URL: http://arxiv.org/abs/1709.03582. [ bib ]
6. Valentin Khrulkov, Maxim Rakhuba, and Ivan Oseledets. Vico-Greengard-Ferrando quadratures in the tensor solver for integral equations. arXiv preprint 1704.01669, 2017. URL: http://arxiv.org/abs/1704.01669. [ bib ]
7. I. V. Oseledets, G. V. Ovchinnikov, and A. M. Katrutsa. Fast, memory efficient low-rank approximation of SimRank. Journal of Complex Networks, 5(1):111–126, 2017. URL: http://arxiv.org/abs/1410.0717, doi:10.1093/comnet/cnw008. [ bib ]
8. Ivan Oseledets, Maxim Rakhuba, and André Uschmajew. Alternating least squares as moving subspace correction. arXiv preprint 1709.07286, 2017. URL: http://arxiv.org/abs/1709.07286. [ bib ]
9. Igor Ostanin, Ivan Tsybulin, Mikhail Litsarev, Ivan Oseledets, and Denis Zorin. Scalable topology optimization with the kernel-independent fast multipole method. Engineering Analysis with Boundary Elements, 83:123–132, 2017. [ bib ]
10. Igor Ostanin, Denis Zorin, and Ivan Oseledets. Fast topological-shape optimization with boundary elements in two dimensions. Russian J. Numer. Anal. Math. Modell., 32(2):127–133, 2017. [ bib ]
11. Igor Ostanin, Denis Zorin, and Ivan Oseledets. Parallel optimization with boundary elements and kernel independent fast multipole method. International Journal of Computational Methods and Experimental Measurements, 5(2):154–162, 2017. [ bib ]
12. G. Ovchinnikov, D. Zorin, and I. Oseledets. Robust regularization of topology optimization problems with a posteriori error estimators. arXiv preprint 1705.07316, 2017. URL: http://arxiv.org/abs/1705.07316. [ bib ]
13. A. Pavlov, G. Ovchinnikov, D. Derbyshev, D. Tsetserukou, and I. Oseledets. AA-ICP: iterative closest point with Anderson acceleration. arXiv preprint 1709.05479, 2017. URL: http://arxiv.org/abs/1709.05479. [ bib ]
14. Vladislav Pimanov and Ivan Oseledets. Regularization of topology optimization problem by the FEM a posteriori error estimator. arXiv preprint 1706.03516, 2017. URL: http://arxiv.org/abs/1706.03516. [ bib ]
15. Maxim Rakhuba and Ivan Oseledets. Jacobi-Davidson method on low-rank matrix manifolds. arXiv preprint 1605.03795, 2017. URL: http://arxiv.org/abs/1703.0906. [ bib ]
16. Daria Sushnikova and Ivan Oseledets. Simple non-extensive sparsification of the hierarchical matrices. arXiv preprint 1705.04601, 2017. URL: http://arxiv.org/abs/1705.04601. [ bib ]

2016

1. L. Bozyk, F. Chill, M. S. Litsarev, I. Yu. Tolstikhina, and V. P. Shevelko. Multiple-electron losses in uranium ion beams in heavy ion synchrotrons. Nuclear Instruments and Methods in Physics Research B, 372:102–108, 2016. doi:10.1016/j.nimb.2016.01.047. [ bib ]
2. A. V. Chertkov, I. V Oseledets, and M. V. Rakhuba. Robust discretization in quantized tensor train format for elliptic problems in two dimensions. arXiv preprint 1612.01166, 2016. URL: http://arxiv.org/abs/1612.01166. [ bib ]
3. A. Cichocki, N. Lee, I. V. Oseledets, A. H. Phan, Q. Zhao, and D. Mandic. Low-rank tensor networks for dimensionality reduction and large-scale optimization problems: perspectives and challenges part 1. arXiv preprint 1609.00893, 2016. accepted at Trends and Foundations in Machine Learning. URL: http://arxiv.org/abs/1609.00893. [ bib ]
4. Andrzej Cichocki, Namgil Lee, Ivan Oseledets, Anh-Huy Phan, Qibin Zhao, Danilo P Mandic, and others. Tensor networks for dimensionality reduction and large-scale optimization: part 1 low-rank tensor decompositions. Foundations and Trends in Machine Learning, 9(4-5):249–429, 2016. [ bib ]
5. Alexander Fonarev, Alexander Mikhalev, Pavel Serdyukov, Gleb Gusev, and Ivan Oseledets. Efficient rectangular maximal-volume algorithm for rating elicitation in collaborative filtering. arXiv preprint 1610.04850, 2016. accepted at ICDM 2016. URL: http://arxiv.org/abs/1610.04850. [ bib ]
6. Evgeny Frolov and Ivan Oseledets. Fifty shades of ratings: how to benefit from a negative feedback in top-n recommendations tasks. In Proceedings of the 10th ACM Conference on Recommender Systems, RecSys '16, 91–98. 2016. URL: http://arxiv.org/abs/1607.04228, doi:10.1145/2959100.2959170. [ bib ]
7. Jutho Haegeman, Christian Lubich, Ivan Oseledets, Bart Vandereycken, and Frank Verstraete. Unifying time evolution and optimization with matrix product states. Phys. Rev. B, 94(16):165116, 2016. URL: http://arxiv.org/abs/1408.5056, doi:10.1103/PhysRevB.94.165116. [ bib ]
8. Vladimir Kazeev, Ivan Oseledets, Maxim Rakhuba, and Christoph Schwab. QTT-finite-element approximation for multiscale problems I: model problems in one dimension. Adv. Comp. Math., 2016. URL: http://www.sam.math.ethz.ch/reports/2016/06, doi:10.1007/s10444-016-9491-y. [ bib ]
9. Valentin Khrulkov and Ivan Oseledets. Desingularization of bounded-rank matrix sets. arXiv preprint 1612.03973, 2016. URL: http://arxiv.org/abs/1612.03973. [ bib ]
10. Denis Kolesnikov and Ivan Oseledets. Convergence analysis of projected fixed-point iteration on a low-rank matrix manifold. arXiv preprint 1604.02111, 2016. URL: http://arxiv.org/abs/1604.02111. [ bib ]
11. M. S. Litsarev and I. V. Oseledets. Low-rank approach to the computation of path integrals. J. Comp. Phys., 305:557–574, 2016. URL: http://arxiv.org/abs/1504.06149, doi:10.1016/j.jcp.2015.11.009. [ bib ]
12. A. Yu. Mikhalev and I. V. Oseledets. Iterative representing set selection fo nested cross approximation. Numer. Linear Algebra Appl., 23(2):230–248, 2016. URL: http://arxiv.org/abs/1309.1773, doi:10.1002/nla.2021. [ bib ]
13. D. V. Nazarenko, P. V. Kharyuk, I. V. Oseledets, I. A. Rodin, and O. A. Shpigun. Machine learning for LC-MS medicinal plants identification. Chemometrics and Intelligent Laboratory Systems, 156:174–180, 2016. URL: http://www.sciencedirect.com/science/article/pii/S0169743916301368, doi:10.1016/j.chemolab.2016.06.003. [ bib ]
14. Alexander Novikov, Mikhail Trofimov, and Ivan Oseledets. Tensor Train polynomial models via Riemannian optimization. arXiv preprint 1605.03795, 2016. URL: http://arxiv.org/abs/1605.03795. [ bib ]
15. Ivan V. Oseledets, Maxim V. Rakhuba, and Andrei V. Chertkov. Black-box solver for multiscale modelling using the QTT format. In Proc. ECCOMAS. Crete Island, Greece, 2016. URL: https://www.eccomas2016.org/proceedings/pdf/10906.pdf. [ bib ]
16. Igor Ostanin, Ivan Tsybulin, Mikhail Litsarev, Ivan Oseledets, and Denis Zorin. What lies beneath the surface: topological-shape optimization with the kernel-independent fast multipole method. arXiv preprint 1612.04082, 2016. URL: http://arxiv.org/abs/1612.04082. [ bib ]
17. M. V. Rakhuba and I. V. Oseledets. Grid-based electronic structure calculations: the tensor decomposition approach. J. Comp. Phys., 2016. URL: http://arxiv.org/abs/1508.07632, doi:10.1016/j.jcp.2016.02.023. [ bib ]
18. Maxim Rakhuba and Ivan Oseledets. Calculating vibrational spectra of molecules using tensor train decomposition. J. Chem. Phys., 145:124101, 2016. doi:10.1063/1.4962420. [ bib ]
19. Daria A. Sushnikova and Ivan V. Oseledets. "compress and eliminate" solver for symmetric positive definite sparse matrices. arXiv preprint 1603.09133, 2016. URL: http://arxiv.org/abs/1603.09133. [ bib ]
20. Daria A. Sushnikova and Ivan V. Oseledets. Preconditioners for hierarchical matrices based on their extended sparse form. Russ. J. Numer. Anal. Math. Modelling, 31(1):29–40, 2016. URL: http://arxiv.org/abs/1412.1253, doi:10.1515/rnam-2016-0003. [ bib ]

2015

1. V. Baranov and I. Oseledets. Fitting high-dimensional potential energy surface using active subspace and tensor train (AS+TT) method. J. Chem. Phys., pages 17107, 2015. doi:10.1063/1.4935017. [ bib ]
2. Sergey I. Kabanikhin, Nikita S. Novikov, Ivan V. Oseledets, and Maxim A. Shishlenin. Fast Toeplitz linear system inversion for solving two-dimensional acoustic inverse problem. Inverse Problems, 23(6):687–700, 2015. doi:10.1515/jiip-2015-0083. [ bib ]
3. D. A. Kolesnikov and I. V. Oseledets. From low-rank approximation to arational Krylov subspace method for the Lyapunov equation. SIAM J. Matrix Anal. Appl., 36(4):1622–1637, 2015. URL: http://arxiv.org/abs/1410.3335, doi:10.1137/140992266. [ bib ]
4. M. S. Litsarev and I. V. Oseledets. Fast low-rank approximations of multidimensional integrals in ion-atomic collisions modelling. Numer. Linear Algebra Appl., 22(6):1147–1160, 2015. URL: http://arxiv.org/abs/1403.4068, doi:10.1002/nla.2008. [ bib ]
5. Christian Lubich, Ivan Oseledets, and Bart Vandereycken. Time integration of tensor trains. SIAM J. Numer. Anal., 53(2):917–941, 2015. URL: http://arxiv.org/abs/1407.2042, doi:10.1137/140976546. [ bib ]
6. A. Yu. Mikhalev and I. V. Oseledets. Rectangular maximum-volume submatrices and their applications. arXiv preprint 1502.07838, 2015. URL: http://arxiv.org/abs/1502.07838. [ bib ]
7. I. V. Oseledets, G. V. Ovchinnikov, and A. M. Katrutsa. Linear complexity SimRank using iterative diagonal estimation. arXiv preprint 1502.07167, 2015. URL: http://arxiv.org/abs/1502.07167. [ bib ]
8. I. Ostanin, A. Mikhalev, D. Zorin, and I. Oseledets. Engineering optimization with the fast boundary element method. WIT Transactions on Modelling and Simulation, 61:7, 2015. doi:10.2495/BEM380141. [ bib ]
9. Igor Ostanin, Roberto Ballarini, and Traian Dumitrica. Distinct element method for multiscale modeling of cross-linked carbon nanotube bundles: from soft to strong nanomaterials. Journal of Materials Research, xxx(xx):–, 2015. Accepted manuscript. To appear in 2015 Focus Issue on Soft Nanomaterials. [ bib ]
10. Igor Ostanin, Yuxiang Ni, Yuezhou Wang, and Traian Dumitrica. Nanomechanics of polycrystalline nanoparticles with the distinct element method. Engineering Materials and Technology, xxx(xx):–, 2015. Accepted manuscript. [ bib ]
11. Igor Ostanin, Denis Zorin, and Ivan Oseledets. Toward fast topological-shape optimization. arXiv preprint 1503.02383, 2015. URL: http://arxiv.org/abs/1503.02383. [ bib ]
12. M. V. Rakhuba and I. V. Oseledets. Fast multidimensional convolution in low-rank tensor formats via cross approximation. SIAM J. Sci. Comput., 37(2):A565–A582, 2015. doi:10.1137/140958529. [ bib ]
13. G.V. Ryzhakov, A.Yu. Mikhalev, D.A. Sushnikova, and I.V. Oseledets. Numerical solution of diffraction problems using large matrix compression. In Antennas and Propagation (EuCAP), 2015 9th European Conference on, 1–3. April 2015. URL: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7228667&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7228667. [ bib ]
14. Ben Usman and Ivan Oseledets. Tensor SimRank for heterogeneous information networks. arXiv preprint 1502.06818, 2015. URL: http://arxiv.org/abs/1502.06818. [ bib ]
15. Wang Youezhou, Igor Ostanin, Christian Gaidau, and Traian Dumitrica. Twisting carbon nanotube ropes with the mesoscopic distinct element method: geometry, packing, and nanomechanics. Langmuir, 31(45):12323–12327, 2015. doi:10.1021/acs.langmuir.5b03208. [ bib ]
16. Zhang Zheng, Xiu Yang, Ivan V. Oseledets, George Em Karniadakis, and Luca Daniel. Enabling high-dimensional hierarchical uncertainty quantification by ANOVA and Tensor-Train decomposition. IEEE Trans. Comput-aided Des. Integr. Circuits Syst., 34(1):63–76, 2015. URL: http://arxiv.org/abs/1407.3023, doi:10.1109/TCAD.2014.2369505. [ bib ]

2014

1. P. -A. Absil and I. V. Oseledets. Low-rank retractions: a survey and new results. Comput. Optim. Appl., 2014. URL: http://sites.uclouvain.be/absil/2013.04, doi:10.1007/s10589-014-9714-4. [ bib ]
2. M. A. Botchev, I. V. Oseledets, and E. E. Tyrtyshnikov. Iterative across-time solution of linear differential equations: Krylov subspace versus waveform relaxation. Comput. Math. Appl., 67(2):2088–2098, 2014. doi:10.1016/j.camwa.2014.03.002. [ bib ]
3. Anwesha Chaudhury, Ivan Oseledets, and Rohit Ramachandran. A computationally efficient technique for the solution of multi-dimensional PBMs of granulation. Comput. Chem. Eng., 61(11):234–244, 2014. doi:10.1016/j.compchemeng.2013.10.020. [ bib ]
4. S. V. Dolgov, B. N. Khoromskij, I. V. Oseledets, and D. V. Savostyanov. Computation of extreme eigenvalues in higher dimensions using block tensor train format. Computer Phys. Comm., 185(4):1207–1216, 2014. doi:10.1016/j.cpc.2013.12.017. [ bib ]
5. Vadim Lebedev, Yaroslav Ganin, Maxim Rakhuba, Ivan Oseledets, and Victor Lempitsky. Speeding up convolutional neural networks using fine-tuned CP-decomposition. arXiv preprint 1412.6553, 2014. URL: http://arxiv.org/abs/1412.6553. [ bib ]
6. Mikhail S. Litsarev and Ivan V. Oseledets. The DEPOSIT computer code based on the low rank approximations. Computer Phys. Comm., 185(10):2801–2082, 2014. doi:10.1016/j.cpc.2014.06.012. [ bib ]
7. Christian Lubich and Ivan V. Oseledets. A projector-splitting integrator for dynamical low-rank approximation. BIT, 54(1):171–188, 2014. doi:10.1007/s10543-013-0454-0. [ bib ]
8. Ivan Oseledets. Solving high-dimensional problems via stable and efficient tensor factorization techniques. In Abstracts of Papers of the American Chemical Society, volume 246, 244–Phys. 2014. [ bib ]
9. Igor Ostanin. Multiscale modeling of carbon nanotube materials with distinct element method. PhD thesis, University of Minnesota, 2014. [ bib ]
10. Igor Ostanin, Roberto Ballarini, and Traian Dumitrica. Distinct element method modeling of carbon nanotube bundles with intertube sliding and dissipation. Journal of Applied Mechanics, 81(6):06004, 2014. URL: http://dx.doi.org/10.1115/1.4026484, doi:10.1115/1.4026484. [ bib ]
11. G. V. Ovchinnikov, D. A. Kolesnikov, and I. V. Oseledets. Algebraic reputation model RepRank and its application to spambot detection. arXiv preprint 1411.5995, 2014. URL: http://arxiv.org/abs/1411.5995. [ bib ]
12. V.P. Shevelko, N. Winckler, and M.S. Litsarev. Influence of multi-electron charge-changing processes on the average charge states of heavy ions passing through a he-gas target. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 330:82–85, 2014. doi:10.1016/j.nimb.2014.04.002. [ bib ]
13. I. Yu. Tolstikhina, M. S. Litsarev, D. Kato, M.-Y. Song, Yoon J.-S., and V.P. Shevelko. Collisions of be, fe, mo and w atoms and ions with hydrogen isotopes: electron capture and electron loss cross sections. Journal of Physics B: Atomic, Molecular and Optical Physics, 47(3):035206, 2014. URL: http://stacks.iop.org/0953-4075/47/i=3/a=035206, doi:10.1088/0953-4075/47/3/035206. [ bib ]
14. Yuezhou Wang, Matthew R. Semler, Igor Ostanin, Erik K. Hobbie, and Traian Dumitrica. Rings and rackets from single-wall carbon nanotubes: manifestations of mesoscale mechanics. Soft Matter, 10:8635–8640, 2014. URL: http://dx.doi.org/10.1039/C4SM00865K, doi:10.1039/C4SM00865K. [ bib ]

2013

1. Traian Dumitrica, Roberto Ballarini, and Igor Ostanin. Multiscale modeling of carbon nanotube material using distinct element method. In DEM6 Conference, Colorado School of Mines, Golden, CO. 2013. [ bib ]
2. Vladimir A. Kazeev and Ivan V. Oseledets. The tensor structure of a class of adaptive algebraic wavelet transforms. Preprint 2013-28, ETH SAM, Zürich, 2013. URL: http://www.sam.math.ethz.ch/sam_reports/reports_final/reports2013/2013-28.pdf. [ bib ]
3. M. S. Litsarev. The deposit computer code: calculations of electron-loss cross-sections for complex ions colliding with neutral atoms. Computer Physics Communications, 184(2):432–439, 2013. URL: http://www.sciencedirect.com/science/article/pii/S0010465512003153, doi:10.1016/j.cpc.2012.09.028. [ bib ]
4. M. S. Litsarev and V. P. Shevelko. Multiple-electron losses of highly charged ions colliding with neutral atoms. Physica Scripta, 2013(T156):014037, 2013. URL: http://stacks.iop.org/1402-4896/2013/i=T156/a=014037, doi:10.1088/0031-8949/2013/T156/014037. [ bib ]
5. Vladimir Lyashev, Ivan Oseledets, and Delai Zheng. Tensor-based multiuser detection and intra-cell interference mitigation in LTE PUCCH. In Proc. TELFOR 2013, 385–388. 2013. doi:10.1109/TELFOR.2013.6716250. [ bib ]
6. E.A. Muravleva and I.V. Oseledets. Fast low-rank solution of the Poisson equation with application to the Stokes problem. arXiv preprint 1306.2150, 2013. URL: http://arxiv.org/abs/1306.2150. [ bib ]
7. I. V. Oseledets. Constructive representation of functions in low-rank tensor formats. Constr. Approx., 37(1):1–18, 2013. doi:10.1007/s00365-012-9175-x. [ bib ]
8. Igor Ostanin, Roberto Ballarini, David Potyondy, and Traian Dumitrica. A distinct element method for large scale simulations of carbon nanotube assemblies. Journal of the Mechanics and Physics of Solids, 61(3):762 – 782, 2013. URL: http://www.sciencedirect.com/science/article/pii/S0022509612002372, doi:http://dx.doi.org/10.1016/j.jmps.2012.10.016. [ bib ]
9. Yuezhou Wang, Cristian Gaidau, Igor Ostanin, and Traian Dumitrica. Ring windings from single-wall carbon nanotubes: a distinct element method study. Applied Physics Letters, 103(18):–, 2013. URL: http://scitation.aip.org/content/aip/journal/apl/103/18/10.1063/1.4827337, doi:http://dx.doi.org/10.1063/1.4827337. [ bib ]