E. A. Nurminskii
- Computational Theory and Mathematics top 5%
- Numerical Analysis top 5%
- Computational Mechanics
- Molecular Biology
- Biotechnology
- Co-authors
- G. N. LikhatskayaYu. M. Ermol’evMarina P. IsaevaE. P. KozlovskayaManlio GaudiosoElena LeychenkoMargarita MonastyrnayaElena Zelepuga
- Topics
- Advanced Optimization Algorithms Research (23 papers)Optimization and Variational Analysis (21 papers)Matrix Theory and Algorithms (7 papers)
In The Last Decade
E. A. Nurminskii
39 papers receiving 253 citations
Peers
Comparison fields: 5 of 77
- Computational Theory and Mathematics 105
- Numerical Analysis 92
- Computational Mechanics 52
- Molecular Biology 48
- Biotechnology 48
Countries citing papers authored by E. A. Nurminskii
This map shows the geographic impact of E. A. Nurminskii'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 E. A. Nurminskii with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites E. A. Nurminskii more than expected).
Fields of papers citing papers by E. A. Nurminskii
This network shows the impact of papers produced by E. A. Nurminskii. 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 E. A. Nurminskii. The network helps show where E. A. Nurminskii may publish in the future.
Co-authorship network of co-authors of E. A. Nurminskii
This figure shows the co-authorship network connecting the top 25 collaborators of E. A. Nurminskii. A scholar is included among the top collaborators of E. A. Nurminskii 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 E. A. Nurminskii. E. A. Nurminskii is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 0 | |
| 3 | 7 | |
| 4 | 45 | |
| 5 | 7 | |
| 6 | 3 | |
| 7 | 47 | |
| 8 | Convergence of the Suitable Affine Subspace Method for Finding the Least Distance to a Simplex | 3 |
| 9 | 16 | |
| 10 | 2 | |
| 11 | 0 | |
| 12 | 0 | |
| 13 | Decomposition of a Large-Scale Energy Model | 1 |
| 14 | Decomposition Algorithm Based on the Primal-Dual Approximation | 1 |
| 15 | Stochastic quasigradient algorithms for minimax problems in stochastic programming | 1 |
| 16 | Trend Analysis for Sparse Data | 0 |
| 17 | On epsilon-Differential Mappings and their Applications in Nondifferentiable Optimization | 1 |
| 18 | 4 | |
| 19 | 0 | |
| 20 | 3 |
About E. A. Nurminskii
E. A. Nurminskii is a scholar working on Numerical Analysis, Computational Theory and Mathematics and Mathematical Physics, having authored 51 papers that have together received 308 indexed citations. Recurring topics across this work include Advanced Optimization Algorithms Research (23 papers), Optimization and Variational Analysis (21 papers) and Matrix Theory and Algorithms (7 papers). The work is most often cited by research in Numerical Analysis (92 citations), Computational Theory and Mathematics (105 citations) and Biotechnology (48 citations). E. A. Nurminskii has collaborated with scholars based in Russia, Australia and Italy. Frequent co-authors include G. N. Likhatskaya, Yu. M. Ermol’ev, Marina P. Isaeva, E. P. Kozlovskaya, Manlio Gaudioso, Elena Leychenko, Margarita Monastyrnaya, Elena Zelepuga, Antonio Fuduli and T. N. Zvyagintseva. Their work appears in journals such as Mathematical Programming, Toxicon and SIAM Journal on Optimization.
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.