815 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- I. Oseledets26 · h 51
- A. Bogatyrev13 · h 10
- K. Metlov11 · h 14
- S. Matveev4 profiles10 · h 9
- A. Smirnov4 profiles8 · h 24
- M. Rakhuba2 profiles7 · h 6
- N. Brilliantov2 profiles6 · h 30
- P. Krapivsky2 profiles6 · h 45
- Alexander Osinsky2 profiles5 · h 12
- E. Tyrtyshnikov5 · h 32
- P. Berloff5 · h 33
- S. Budzinskiy5 · h 6
- Russian Academy of SciencesRU62 papers
- Skolkovo Institute of Science and TechnologyRU35 papers
- Lomonosov Moscow State UniversityRU22 papers
- Moscow Institute of Physics and TechnologyRU13 papers
- Donetsk Institute for Physics and Engineering named after O.O. GalkinUA7 papers
- Imperial College LondonGB6 papers
- Boston UniversityUS5 papers
- National Research University Higher School of EconomicsRU5 papers
- University of LeicesterGB5 papers
- University of HoustonUS4 papers
- University of ReadingGB4 papers
- ETH ZurichCH3 papers
4 papers · 1 filter
An adaptive octree finite element method for PDEs posed on surfaces
Alexey Y. Chernyshenko, Maxim A. Olshanskii
The paper develops a finite element method for partial differential equations posed on hypersurfaces in , . The method uses traces of bulk finite element funct…
Unifying time evolution and optimization with matrix product states
Jutho Haegeman, Christian Lubich, Ivan Oseledets +2
We show that the time-dependent variational principle provides a unifying framework for time-evolution methods and optimisation methods in the context of matrix product states. In…
Low rank approximations for the DEPOSIT computer code
Mikhail Litsarev, Ivan Oseledets
We present an efficient technique based on low-rank separated approximations for the computation of three-dimensional integrals in the computer code DEPOSIT that describes ion-atom…
Fast multidimensional convolution in low-rank formats via cross approximation
M. V. Rakhuba, I. V. Oseledets
We propose a new cross-conv algorithm for approximate computation of convolution in different low-rank tensor formats (tensor train, Tucker, Hierarchical Tucker). It has better com…