Vladimir Kazeev
- Computational Mathematics top 0.5%
- Computational Theory and Mathematics top 5%
- Computational Mechanics top 10%
- Statistical and Nonlinear Physics top 5%
- Molecular Biology
- Co-authors
- Christoph SchwabBoris N. KhoromskijMustafa KhammashEugene E. TyrtyshnikovIvan OseledetsЕ. Е. ТыртышниковSergey DolgovPhilipp Petersen
- Topics
- Tensor decomposition and applications (13 papers)Advanced Numerical Methods in Computational Mathematics (6 papers)Matrix Theory and Algorithms (6 papers)
- Cited by
- Computational MathematicsStatistical and Nonlinear PhysicsComputational Theory and Mathematics
- Partner nations
- SwitzerlandRussiaAustria
In The Last Decade
Vladimir Kazeev
17 papers receiving 303 citations
Peers
Comparison fields: 5 of 48
- Computational Mathematics 231
- Computational Theory and Mathematics 116
- Computational Mechanics 107
- Statistical and Nonlinear Physics 93
- Molecular Biology 57
Countries citing papers authored by Vladimir Kazeev
This map shows the geographic impact of Vladimir Kazeev'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 Vladimir Kazeev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vladimir Kazeev more than expected).
Fields of papers citing papers by Vladimir Kazeev
This network shows the impact of papers produced by Vladimir Kazeev. 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 Vladimir Kazeev. The network helps show where Vladimir Kazeev may publish in the future.
Co-authorship network of co-authors of Vladimir Kazeev
This figure shows the co-authorship network connecting the top 25 collaborators of Vladimir Kazeev. A scholar is included among the top collaborators of Vladimir Kazeev based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Vladimir Kazeev. Vladimir Kazeev is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 5 | |
| 3 | 2 | |
| 4 | 20 | |
| 5 | 15 | |
| 6 | 14 | |
| 7 | 15 | |
| 8 | 4 | |
| 9 | 79 | |
| 10 | 46 | |
| 11 | 28 | |
| 12 | 2 | |
| 13 | 75 | |
| 14 | 8 | |
| 15 | 2 | |
| 16 | 7 | |
| 17 | On explicit QTT representation of Laplace operator and its inverse | 3 |
About Vladimir Kazeev
Vladimir Kazeev is a scholar working on Computational Mathematics, Computational Theory and Mathematics and Numerical Analysis, having authored 17 papers that have together received 326 indexed citations. Recurring topics across this work include Tensor decomposition and applications (13 papers), Advanced Numerical Methods in Computational Mathematics (6 papers) and Matrix Theory and Algorithms (6 papers). The work is most often cited by research in Computational Mathematics (231 citations), Statistical and Nonlinear Physics (93 citations) and Computational Theory and Mathematics (116 citations). Vladimir Kazeev has collaborated with scholars based in Switzerland, Russia and Austria. Frequent co-authors include Christoph Schwab, Boris N. Khoromskij, Mustafa Khammash, Eugene E. Tyrtyshnikov, Ivan Oseledets, Е. Е. Тыртышников, Sergey Dolgov and Philipp Petersen. Their work appears in journals such as PLoS Computational Biology, SIAM Journal on Scientific Computing and Numerische Mathematik.
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.