E. A. Nurminskii

512 total citations
51 papers, 308 citations indexed

About

E. A. Nurminskii is a scholar working on Computational Theory and Mathematics, Numerical Analysis and Mathematical Physics. According to data from OpenAlex, E. A. Nurminskii has authored 51 papers receiving a total of 308 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Computational Theory and Mathematics, 25 papers in Numerical Analysis and 5 papers in Mathematical Physics. Recurrent topics in E. A. Nurminskii's work include Advanced Optimization Algorithms Research (23 papers), Optimization and Variational Analysis (21 papers) and Matrix Theory and Algorithms (7 papers). E. A. Nurminskii is often cited by papers focused on Advanced Optimization Algorithms Research (23 papers), Optimization and Variational Analysis (21 papers) and Matrix Theory and Algorithms (7 papers). E. A. Nurminskii collaborates with scholars based in Russia, Australia and United States. E. A. Nurminskii's co-authors include G. N. Likhatskaya, Yu. M. Ermol’ev, Marina P. Isaeva, Elena Leychenko, Manlio Gaudioso, Antonio Fuduli, Margarita Monastyrnaya, Elena Zelepuga, E. P. Kozlovskaya and Svetlana N. Kovalchuk and has published in prestigious journals such as Mathematical Programming, Toxicon and SIAM Journal on Optimization.

In The Last Decade

E. A. Nurminskii

39 papers receiving 253 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
E. A. Nurminskii Russia	9	105	92	52	48	48	51	308
Raymond Cheng United States	10	31 0.3×	39 0.4×	6 0.1×	53 1.1×	15 0.3×	51	368
Xiuqing Chen China	9	51 0.5×	12 0.1×	15 0.3×	29 0.6×	6 0.1×	40	235
John K. Hunter United States	9	21 0.2×	17 0.2×	14 0.3×	59 1.2×	3 0.1×	15	286
D. Somasundaram India	7	56 0.5×	41 0.4×	10 0.2×	46 1.0×	2 0.0×	15	299
Mausumi Sen India	14	74 0.7×	112 1.2×	4 0.1×	34 0.7×	3 0.1×	64	580
Mikhail Krastanov Bulgaria	11	86 0.8×	32 0.3×	34 0.7×	25 0.5×		41	318
P. V. Subrahmanyam India	12	200 1.9×	46 0.5×	7 0.1×	38 0.8×	3 0.1×	41	703
Ethan X. Fang United States	9	40 0.4×	39 0.4×	129 2.5×	43 0.9×		24	383
J. H. Venter South Africa	12	43 0.4×	15 0.2×	11 0.2×	20 0.4×	2 0.0×	38	587
Yannick Baraud France	10	19 0.2×	18 0.2×	45 0.9×	16 0.3×		20	408

Countries citing papers authored by E. A. Nurminskii

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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20 of 20 papers shown

Pardalos, Pãnos M. & E. A. Nurminskii. (2016). Discrete Optimization and Operations Research. Lecture notes in computer science. 8 indexed citations

Nurminskii, E. A., et al.. (2015). Synthesis of Cutting and Separating Planes in a Nonsmooth Optimization Method1. Cybernetics and Systems Analysis. 51(4). 619–631.

Tf, Solov'eva, et al.. (2011). A Novel OmpY Porin FromYersinia Pseudotuberculosis: Structure, Channel-Forming Activity and Trimer Thermal Stability. Journal of Biomolecular Structure and Dynamics. 28(4). 517–533. 7 indexed citations

Monastyrnaya, Margarita, Elena Leychenko, Marina P. Isaeva, et al.. (2010). Actinoporins from the sea anemones, tropical Radianthus macrodactylus and northern Oulactis orientalis: Comparative analysis of structure–function relationships. Toxicon. 56(8). 1299–1314. 45 indexed citations

Nurminskii, E. A.. (2009). Envelope stepsize control for iterative algorithms based on Fejer processes with attractants. Optimization methods & software. 25(1). 97–108. 7 indexed citations

Nurminskii, E. A.. (2008). The use of additional diminishing disturbances in Fejer models of iterative algorithms. Computational Mathematics and Mathematical Physics. 48(12). 2154–2161. 12 indexed citations

Nurminskii, E. A.. (2008). Fejer processes with diminishing disturbances. Doklady Mathematics. 78(2). 755–758. 3 indexed citations

Nurminskii, E. A., et al.. (2007). A method of local convex majorants for solving variational-like inequalities. Computational Mathematics and Mathematical Physics. 47(3). 341–348. 3 indexed citations

Kovalchuk, Svetlana N., et al.. (2006). Purification, cDNA cloning and homology modeling of endo-1,3-β-d-glucanase from scallop Mizuhopecten yessoensis. Comparative Biochemistry and Physiology Part B Biochemistry and Molecular Biology. 143(4). 473–485. 47 indexed citations

10.

Nurminskii, E. A.. (2005). Convergence of the Suitable Affine Subspace Method for Finding the Least Distance to a Simplex. 3 indexed citations

11.

Nurminskii, E. A., et al.. (2004). Portfolio Replication: Its Forward-Dual Decomposition. Automation and Remote Control. 65(2). 311–318. 2 indexed citations

12.

Nurminskii, E. A.. (1986). A class of convex programming methods. USSR Computational Mathematics and Mathematical Physics. 26(4). 122–128.

13.

Nurminskii, E. A., et al.. (1983). Decomposition of a Large-Scale Energy Model. IIASA PURE (International Institute of Applied Systems Analysis). 4(3). 295–308. 1 indexed citations

14.

Nurminskii, E. A.. (1982). Decomposition Algorithm Based on the Primal-Dual Approximation. IIASA PURE (International Institute of Applied Systems Analysis). 1 indexed citations

15.

Ermoliev, Y. & E. A. Nurminskii. (1980). Stochastic quasigradient algorithms for minimax problems in stochastic programming. IIASA PURE (International Institute of Applied Systems Analysis). 1 indexed citations

16.

Nurminskii, E. A., et al.. (1979). Trend Analysis for Sparse Data. IIASA PURE (International Institute of Applied Systems Analysis).

17.

Nurminskii, E. A.. (1978). On epsilon-Differential Mappings and their Applications in Nondifferentiable Optimization. IIASA PURE (International Institute of Applied Systems Analysis). 1 indexed citations

18.

Nurminskii, E. A.. (1976). Minimization of nondifferentiable functions in the presence of noise. Cybernetics and Systems Analysis. 10(4). 619–621. 4 indexed citations

19.

Nurminskii, E. A.. (1975). A method of solution of a nonlinear programming problem with constraints of the type of equalities or inequalities. Cybernetics and Systems Analysis. 10(1). 118–122.

20.

Nurminskii, E. A.. (1975). Convergence conditions of stochastic programming algorithms. Cybernetics and Systems Analysis. 9(3). 464–468. 3 indexed citations

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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