Ivan Oseledets

178 papers receiving 6.0k citations

Ivan Oseledets's Hit Papers

Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions 2016 · 287 citations
2870+5+10Years since publication50010001.5k

Peers

Ivan Oseledets
Comparison fields: 5 of 143
  • Computational Mathematics 3.4k
  • Statistical and Nonlinear Physics 1.3k
  • Computational Theory and Mathematics 1.4k
  • Computational Mechanics 1.4k
  • Numerical Analysis 275
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Countries citing papers authored by Ivan Oseledets

Since Specialization
Citations

This map shows the geographic impact of Ivan Oseledets's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ivan Oseledets with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivan Oseledets more than expected).

Fields of papers citing papers by Ivan Oseledets

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ivan Oseledets. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ivan Oseledets. The network helps show where Ivan Oseledets may publish in the future.

Co-authors

The 25 scholars most cited alongside Ivan Oseledets, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ivan Oseledets Line = papers co-authored together Ivan Oseledets links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 201 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Tensor-Train Decomposition
Hit paper breakdown →
20111656
2
Unifying time evolution and optimization with matrix product states
Hit paper breakdown →
2016515
3 2009351
4 2009325
5
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions
Hit paper breakdown →
2016287
6 2008138
7 2010133
8
Time integration of tensor trains
2016127
9 2010125
10 2012118
11 2013116
12 202294
13
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions
201694
14 201285
15 201476
16 201276
17 202267
18 201167
19 202063
20 201863

About Ivan Oseledets

Ivan Oseledets is a scholar working on Computational Mathematics, Computational Theory and Mathematics, Artificial Intelligence, Computational Mechanics and Computer Vision and Pattern Recognition, having authored 201 papers that have together received 6.4k indexed citations. Recurring topics across this work include Tensor decomposition and applications (80 papers), Matrix Theory and Algorithms (49 papers), Model Reduction and Neural Networks (22 papers), Electromagnetic Scattering and Analysis (21 papers), Sparse and Compressive Sensing Techniques (18 papers), Advanced Numerical Methods in Computational Mathematics (11 papers), Adversarial Robustness in Machine Learning (11 papers) and Remote Sensing in Agriculture (10 papers). The work is most often cited by research in Computational Mathematics (3.4k citations), Statistical and Nonlinear Physics (1.3k citations), Computational Theory and Mathematics (1.4k citations), Computational Mechanics (1.4k citations) and Numerical Analysis (275 citations). Ivan Oseledets has collaborated with scholars based in Russia, Germany and United States. Frequent co-authors include E. E. Tyrtyshnikov, Eugene E. Tyrtyshnikov, Christian Lubich, Bart Vandereycken, Boris N. Khoromskij, Dmitry Savostyanov, Sergey Dolgov, Frank Verstraete, Jutho Haegeman and Andrzej Cichocki. Their work appears in journals such as SIAM Journal on Scientific Computing, Linear Algebra and its Applications, Journal of Computational Physics, IEEE Access and Computational Methods in Applied Mathematics.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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