Ivan Oseledets

• Solution of multidimensional integral and differential equations discretized on fine grids
• Ab initio computations in quantum chemistry and computational material design
• Construction of reduced order models for multiparametric systems in engineering
• Uncertainty quantification in engineering sciences
• Data mining and compression

• 1. 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 ]
  2. 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 ]
  3. 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 ]
  4. 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 ]
  5. Evgeny Frolov and Ivan Oseledets. Tensor methods and recommender systems. arXiv preprint 1603.06038, 2016. URL: http://arxiv.org/abs/1603.06038. [ bib ]
  6. 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 ]
  7. 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 ]
  8. Valentin Khrulkov and Ivan Oseledets. Desingularization of bounded-rank matrix sets. arXiv preprint 1612.03973, 2016. URL: http://arxiv.org/abs/1612.03973. [ bib ]
  9. 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 ]
  10. 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 ]
  11. 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 ]
  12. 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 ]
  13. 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 ]
  14. I. V. Oseledets, G. V. Ovchinnikov, and A. M. Katrutsa. Fast, memory efficient low-rank approximation of SimRank. Journal of Complex Networks, 2016. URL: http://arxiv.org/abs/1410.0717, doi:10.1093/comnet/cnw008. [ 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., pages 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 ]
  21. 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 ]
  22. 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 ]
  23. 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 ]
  24. 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 ]
  25. 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 ]
  26. 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 ]
  27. 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 ]
  28. 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 ]
  29. 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 ]
  30. 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 ]
  31. 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 ]
  32. Ben Usman and Ivan Oseledets. Tensor SimRank for heterogeneous information networks. arXiv preprint 1502.06818, 2015. URL: http://arxiv.org/abs/1502.06818. [ bib ]
  33. 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 ]
  34. 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 ]
  35. 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 ]
  36. 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 ]
  37. 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 ]
  38. 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 ]
  39. 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 ]
  40. 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 ]
  41. 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 ]
  42. 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 ]
  43. Vladimir A. Kazeev and Ivan V. Oseledets. The tensor structure of a class of adaptive algebraic wavelet transforms. Preprint 2013-28, ETH SAM, Z\”urich, 2013. URL: http://www.sam.math.ethz.ch/sam_reports/reports_final/reports2013/2013-28.pdf. [ bib ]
  44. 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 ]
  45. 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 ]
  46. 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 ]
  47. S. V. Dolgov, Boris N. Khoromskij, and Ivan V. Oseledets. Fast solution of multi-dimensional parabolic problems in the tensor train/quantized tensor train–format with initial application to the Fokker-Planck equation. SIAM J. Sci. Comput., 34(6):A3016–A3038, 2012.doi:10.1137/120864210. [ bib ]
  48. S. V. Dolgov, Boris N. Khoromskij, Ivan V. Oseledets, and Eugene E. Tyrtyshnikov. Low-rank tensor structure of solutions to elliptic problems with jumping coefficients. J. Comput. Math., 30(1):14–23, 2012. URL: http://www.mis.mpg.de/de/publications/preprints/2011/prepr2011-12.html, doi:10.4208/jcm.1110-m11si08. [ bib ]
  49. Sergey Dolgov, Boris N. Khoromskij, Ivan V. Oseledets, and Eugene E. Tyrtyshnikov. A reciprocal preconditioner for structured matrices arising from elliptic problems with jumping coefficients. Linear Algebra Appl., 436(9):2980–3007, 2012. doi:10.1016/j.laa.2011.09.010. [ bib ]
  50. S. A. Goreinov, I. V. Oseledets, and D. V. Savostyanov. Wedderburn rank reduction and Krylov subspace method for tensor approximation. Part 1: Tucker case. SIAM J. Sci. Comput., 34(1):A1–A27, 2012. doi:10.1137/100792056. [ bib ]
  51. I. V. Oseledets and S. V. Dolgov. Solution of linear systems and matrix inversion in the TT-format. SIAM J. Sci. Comput., 34(5):A2718–A2739, 2012. doi:10.1137/110833142. [ bib ]
  52. I. V. Oseledets, B. N. Khoromskij, and R. Schneider. Efficient time-stepping scheme for dynamics on TT-manifolds. Preprint 24, MPI MIS, 2012. URL: http://www.mis.mpg.de/preprints/2012/preprint2012_24.pdf. [ bib ]
  53. I. V. Oseledets and A. Yu Mikhalev. Representation of quasiseparable matrices using excluded sums and equivalent charges. Linear Algebra Appl., 436(3):699–708, 2012.doi:10.1016/j.laa.2011.07.041. [ bib ]
  54. B. N. Khoromskij and I. V. Oseledets. QTT-approximation of elliptic solution operators in higher dimensions. Rus. J. Numer. Anal. Math. Model., 26(3):303–322, 2011.doi:10.1515/rjnamm.2011.017. [ bib ]
  55. I. V. Oseledets. Improved n-term Karatsuba-like formulas in GF(2). IEEE Trans. Computers, 60(8):1212–1216, 2011. doi:10.1109/TC.2010.233. [ bib ]
  56. I. V. Oseledets. Tensor train decomposition for low-parametric representation of high-dimensional arrays and functions: Review of recent results. In Proceedings of 7th International Workshop on Multidimensional Systems (nDS). IEEE, 2011. doi:10.1109/nDS.2011.6076872. [ bib ]
  57. I. V. Oseledets. Tensor-train decomposition. SIAM J. Sci. Comput., 33(5):2295–2317, 2011.doi:10.1137/090752286. [ bib ]
  58. I. V. Oseledets. DMRG approach to fast linear algebra in the TT–format. Comput. Meth. Appl. Math., 11(3):382–393, 2011. doi:10.2478/cmam-2011-0021. [ bib ]
  59. I. V. Oseledets and E. E. Tyrtyshnikov. Algebraic wavelet transform via quantics tensor train decomposition. SIAM J. Sci. Comput., 33(3):1315–1328, 2011. doi:10.1137/100811647. [ bib ]
  60. I. V. Oseledets, E. E. Tyrtyshnikov, and N. L. Zamarashkin. Tensor-train ranks of matrices and their inverses. Comput. Meth. Appl. Math, 11(3):394–403, 2011. [ bib ]
  61. D. V. Savostyanov and I. V. Oseledets. Fast adaptive interpolation of multi-dimensional arrays in tensor train format. In Proceedings of 7th International Workshop on Multidimensional Systems (nDS). IEEE, 2011. doi:10.1109/nDS.2011.6076873. [ bib ]
  62. S. Dolgov, B. Khoromskij, I. V. Oseledets, and E. E. Tyrtyshnikov. Tensor structured iterative solution of elliptic problems with jumping coefficients. Preprint 55, MPI MIS, Leipzig, 2010. URL: http://www.mis.mpg.de/preprints/2010/preprint2010_55.pdf. [ bib ]
  63. S. A. Goreinov, I. V. Oseledets, D. V. Savostyanov, E. E. Tyrtyshnikov, and N. L. Zamarashkin. How to find a good submatrix. In V. Olshevsky and E. Tyrtyshnikov, editors, Matrix Methods: Theory, Algorithms, Applications, pages 247–256. World Scientific, Hackensack, NY, 2010. [ bib ]
  64. B. N. Khoromskij and I. V. Oseledets. DMRG+QTT approach to computation of the ground state for the molecular Schr\”odinger operator. Preprint 69, MPI MIS, Leipzig, 2010. URL: http://www.mis.mpg.de/preprints/2010/preprint2010_69.pdf. [ bib ]
  65. B. N. Khoromskij and I. V. Oseledets. Quantics-TT collocation approximation of parameter-dependent and stochastic elliptic PDEs. Comput. Methods Appl. Math., 10(4):376–394, 2010.doi:10.2478/cmam-2010-0023. [ bib ]
  66. I. V. Oseledets. Approximation of $2^d \times 2^d$ matrices using tensor decomposition. SIAM J. Matrix Anal. Appl., 31(4):2130–2145, 2010. doi:10.1137/090757861. [ bib ]
  67. I. V. Oseledets and E. A. Muravleva. Fast orthogonalization to the kernel of discrete gradient operator with application to the Stokes problem. Linear Algebra Appl., 432(6):1492–1500, 2010.doi:10.1016/j.laa.2009.11.010. [ bib ]
  68. I. V. Oseledets, D. V. Savostyanov, and E. E. Tyrtyshnikov. Cross approximation in tensor electron density computations. Numer. Linear Algebra Appl., 17(6):935–952, 2010.doi:10.1002/nla.682. [ bib ]
  69. I. V. Oseledets and E. E. Tyrtyshnikov. TT-cross approximation for multidimensional arrays.Linear Algebra Appl., 432(1):70–88, 2010. doi:10.1016/j.laa.2009.07.024. [ bib ]
  70. I. V. Oseledets. A new tensor decomposition. Doklady Math., 80(1):495–496, 2009.doi:10.1134/S1064562409040115. [ bib ]
  71. I. V. Oseledets. Approximation of matrices with logarithmic number of parameters. Doklady Math., 428(1):23–24, 2009. doi:10.1134/S1064562409050056. [ bib ]
  72. I. V. Oseledets, D. V. Savostyanov, and E. E. Tyrtyshnikov. Fast simultaneous orthogonal reduction to triangular matrices. SIAM J. Matrix Anal. Appl., 31(2):316–330, 2009.doi:10.1137/060650738. [ bib ]
  73. I. V. Oseledets, D. V. Savostyanov, and E. E. Tyrtyshnikov. Linear algebra for tensor problems.Computing, 85(3):169–188, 2009. doi:10.1007/s00607-009-0047-6. [ bib ]
  74. I. V. Oseledets, S. L. Stavtsev, and E. E. Tyrtyshnikov. Integration of oscillating functions in a quasi-threedimensional electrodynamic problem. Comput. Math. Math. Phys, 49(2):301–312, 2009.doi:10.1134/S0965542509020092. [ bib ]
  75. I. V. Oseledets and E. E. Tyrtyshnikov. Breaking the curse of dimensionality, or how to use SVD in many dimensions. SIAM J. Sci. Comput., 31(5):3744–3759, 2009. doi:10.1137/090748330. [ bib ]
  76. I. V. Oseledets and E. E. Tyrtyshnikov. Recursive decomposition of multidimensional tensors.Doklady Math., 427(1):14–16, 2009. doi:10.1134/S1064562409040036. [ bib ]
  77. I. V. Oseledets and E. E. Tyrtyshnikov. Tensor tree decomposition does not need a tree. Preprint (Submitted to Linear Algebra Appl) 2009-04, INM RAS, Moscow, 2009. URL: http://pub.inm.ras.ru/pub/inmras2009-08.pdf. [ bib ]
  78. I. V. Oseledets, E. E. Tyrtyshnikov, and N. L. Zamarashkin. Matrix inversion cases with size-independent rank estimates. Linear Algebra Appl., 431(5-7):558–570, 2009.doi:10.1016/j.laa.2009.03.001. [ bib ]
  79. I. V. Oseledets, E. E. Tyrtyshnikov, and N. L. Zamarashkin. Tensor structure of the inverse of a banded Toeplitz matrix. Doklady Math., 80(2):669–670, 2009. doi:10.1134/S106456240905010X. [ bib ]
  80. V. Olshevsky, I. V. Oseledets, and E. E. Tyrtyshnikov. Superfast inversion of two-level Toeplitz matrices using Newton iteration and tensor-displacement structure. Operator Theory: Advances and Applications, 179:229–240, 2008. doi:10.1007/978-3-7643-8539-2_14. [ bib ]
  81. I. V. Oseledets. Optimal Karatsuba-like formulae for certain bilinear forms in GF(2). Linear Algebra Appl., 429(8):2052–2066, 2008. doi:10.1016/j.laa.2008.06.004. [ bib ]
  82. I. V. Oseledets. The integral operator with logarithmic kernel has only one positive eigenvalue.Linear Algebra Appl., 428(7):1560–1564, 2008. [ bib ]
  83. I. V. Oseledets, D. V. Savostianov, and E. E. Tyrtyshnikov. Tucker dimensionality reduction of three-dimensional arrays in linear time. SIAM J. Matrix Anal. Appl., 30(3):939–956, 2008.doi:10.1137/060655894. [ bib ]
  84. I. V. Oseledets. Lower bounds for separable approximations of the Hilbert kernel. Mat. Sb., 198(3):137–144, 2007. [ bib ]
  85. V. Olshevsky, I. V. Oseledets, and E. E. Tyrtyshnikov. Tensor properties of multilevel Toeplitz and related matrices. Linear Algebra Appl., 412(1):1–21, 2006. doi:10.1016/j.laa.2005.03.040. [ bib ]
  86. I. V. Oseledets and D. V. Savostyanov. Minimization methods for approximating tensors and their comparison. Comput. Math. Math. Phys., 46(10):1641–1650, 2006.doi:10.1134/S0965542506100022. [ bib ]
  87. I. V. Oseledets, E. E Tyrtyshnikov, and N. L. Zamarashkin. On the approximation of Toeplitz matrices by a sum of the circulant and low-rank matrix. Doklady Math., 406(5):602–603, 2006. [ bib ]
  88. I. V. Oseledets and E. E. Tyrtyshnikov. A unifying approach to the construction of circulant preconditioners. Linear Algebra Appl., 418(2-3):435–449, 2006. [ bib ]
  89. I. V. Oseledets. Use of divided differences and B-splines for constructing fast discrete transforms of wavelet type on nonuniform grids. Math. Notes, 77(5-6):686–694, 2005. [ bib ]
  90. I. V. Oseledets and E. E. Tyrtyshnikov. Approximate inversion of matrices in the process of solving a hypersingular integral equation. Comput. Math. Math. Phys., 45(2):302–313, 2005. [ bib ]
  91. J. M. Ford, I. V. Oseledets, and E. E. Tyrtyshnikov. Matrix approximations and solvers using tensor products and non-standard wavelet transforms related to irregular grids. Russ. J. Numer. Anal. Math. Modelling, 19(2):185–204, 2004. doi:10.1515/156939804323089334. [ bib ]

• Gold medal from the Russian Academy of Sciences for best student work in Mathematics (2005)
• Gold medal from the Russian Academy of Sciences for the best work among young scientists in Mathematics, joint with D. V. Savostyanov (2010)

• Numerical Linear Algebra, an obligatory course for 1st year Master students in Data Science and Computational Data https://github.com/oseledets/nla2016.
• Fast methods for partial differential and integral equations, elective course for 2nd year Master students https://github.com/oseledets/fastpde2016

ФИО: Оселедец Иван Валерьевич

Занимаемая должность (должности): Профессор

Преподаваемые дисциплины: Вычислительная линейная алгебра

Ученая степень: Доктор физико-математических наук, 2012 Институт вычислительной математики РАН, Москва; Кандидат физико-математических наук, 2007, Институт вычислительной математики РАН, Москва

Ученое звание (при наличии): нет

Наименование направления подготовки и/или специальности: Прикладная математика и физика

Данные о повышении квалификации и/или профессиональной переподготовке (при наличии): нет

Общий стаж работы: 14 лет

Стаж работы по специальности: 14 лет