María P. Vassileva

756 total citations
36 papers, 514 citations indexed

About

María P. Vassileva is a scholar working on Numerical Analysis, Computational Theory and Mathematics and Modeling and Simulation. According to data from OpenAlex, María P. Vassileva has authored 36 papers receiving a total of 514 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Numerical Analysis, 18 papers in Computational Theory and Mathematics and 10 papers in Modeling and Simulation. Recurrent topics in María P. Vassileva's work include Iterative Methods for Nonlinear Equations (34 papers), Advanced Optimization Algorithms Research (30 papers) and Matrix Theory and Algorithms (18 papers). María P. Vassileva is often cited by papers focused on Iterative Methods for Nonlinear Equations (34 papers), Advanced Optimization Algorithms Research (30 papers) and Matrix Theory and Algorithms (18 papers). María P. Vassileva collaborates with scholars based in Dominican Republic, Spain and Bulgaria. María P. Vassileva's co-authors include Juan R. Torregrosa, Alicia Cordero, P. Vindel, Peter Vassilev, Georgi Georgiev, Francisco I. Chicharro and Eulalia Martı́nez and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Mathematics and Computation and Journal of Computational and Applied Mathematics.

In The Last Decade

María P. Vassileva

32 papers receiving 495 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
María P. Vassileva Dominican Republic 12 416 218 121 64 25 36 514
Nikos I. Kavallaris United Kingdom 11 38 0.1× 152 0.7× 80 0.7× 37 0.6× 20 0.8× 46 384
Renate Schaaf United States 10 66 0.2× 186 0.9× 132 1.1× 16 0.3× 41 1.6× 15 471
Jianqiang Sun China 9 162 0.4× 8 0.0× 46 0.4× 47 0.7× 5 0.2× 42 363
Jingyu Li China 13 44 0.1× 129 0.6× 180 1.5× 79 1.2× 64 2.6× 40 473
Felix Sadyrbaev Latvia 10 121 0.3× 58 0.3× 47 0.4× 12 0.2× 4 0.2× 89 309
Óscar Sánchez Spain 9 34 0.1× 87 0.4× 69 0.6× 5 0.1× 39 1.6× 18 299
Hongsong Feng United States 12 47 0.1× 139 0.6× 5 0.0× 70 1.1× 1 0.0× 24 300
Grégory Faye France 10 15 0.0× 27 0.1× 28 0.2× 13 0.2× 1 0.0× 34 301
A. Erkip Türkiye 10 16 0.0× 31 0.1× 19 0.2× 26 0.4× 3 0.1× 29 250
Pengcheng Niu China 12 9 0.0× 206 0.9× 88 0.7× 17 0.3× 30 1.2× 90 520

Countries citing papers authored by María P. Vassileva

Since Specialization
Citations

This map shows the geographic impact of María P. Vassileva'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 María P. Vassileva with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites María P. Vassileva more than expected).

Fields of papers citing papers by María P. Vassileva

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by María P. Vassileva. 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 María P. Vassileva. The network helps show where María P. Vassileva may publish in the future.

Co-authorship network of co-authors of María P. Vassileva

This figure shows the co-authorship network connecting the top 25 collaborators of María P. Vassileva. A scholar is included among the top collaborators of María P. Vassileva 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 María P. Vassileva. María P. Vassileva is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Cordero, Alicia, et al.. (2024). High-efficiency implicit scheme for solving first-order partial differential equations. Results in Applied Mathematics. 24. 100507–100507. 1 indexed citations
2.
Cordero, Alicia, et al.. (2024). Inverse matrix estimations by iterative methods with weight functions and their stability analysis. Applied Mathematics Letters. 155. 109122–109122. 1 indexed citations
3.
Cordero, Alicia, et al.. (2024). Increasing in three units the order of convergence of iterative methods for solving nonlinear systems. Mathematics and Computers in Simulation. 223. 509–522. 5 indexed citations
4.
Cordero, Alicia, et al.. (2024). Maximally efficient damped composed Newton-type methods to solve nonlinear systems of equations. Applied Mathematics and Computation. 492. 129231–129231. 1 indexed citations
5.
6.
Cordero, Alicia, et al.. (2023). Fractal Complexity of a New Biparametric Family of Fourth Optimal Order Based on the Ermakov–Kalitkin Scheme. Fractal and Fractional. 7(6). 459–459. 4 indexed citations
7.
Cordero, Alicia, et al.. (2023). A highly efficient class of optimal fourth-order methods for solving nonlinear systems. Numerical Algorithms. 95(4). 1879–1904. 9 indexed citations
8.
Cordero, Alicia, et al.. (2023). Generalized conformable fractional Newton-type method for solving nonlinear systems. Numerical Algorithms. 93(3). 1171–1208. 9 indexed citations
9.
Cordero, Alicia, et al.. (2023). Stability Analysis of a New Fourth-Order Optimal Iterative Scheme for Nonlinear Equations. Axioms. 13(1). 34–34. 5 indexed citations
10.
Cordero, Alicia, et al.. (2021). Semilocal Convergence of the Extension of Chun’s Method. Axioms. 10(3). 161–161. 2 indexed citations
11.
Cordero, Alicia, et al.. (2019). Generalized Inverses Estimations by Means of Iterative Methods with Memory. Mathematics. 8(1). 2–2. 16 indexed citations
12.
Cordero, Alicia, Juan R. Torregrosa, & María P. Vassileva. (2017). A family of parametric schemes of arbitrary even order for solving nonlinear models: CMMSE2016. Journal of Mathematical Chemistry. 55(7). 1443–1460.
13.
Cordero, Alicia, Juan R. Torregrosa, & María P. Vassileva. (2016). Weighted Gaussian correction of Newton-type methods for solving nonlinear systems. 59(107). 23–38. 1 indexed citations
14.
Cordero, Alicia, et al.. (2016). Stability of a fourth order bi-parametric family of iterative methods. Journal of Computational and Applied Mathematics. 312. 94–102. 5 indexed citations
15.
Cordero, Alicia, et al.. (2013). Chaos in King’s iterative family. Applied Mathematics Letters. 26(8). 842–848. 102 indexed citations
16.
Cordero, Alicia, Juan R. Torregrosa, & María P. Vassileva. (2012). Pseudocomposition: A technique to design predictor–corrector methods for systems of nonlinear equations. Applied Mathematics and Computation. 218(23). 11496–11504. 27 indexed citations
17.
Cordero, Alicia, Juan R. Torregrosa, & María P. Vassileva. (2012). Increasing the order of convergence of iterative schemes for solving nonlinear systems. Journal of Computational and Applied Mathematics. 252. 86–94. 24 indexed citations
18.
Cordero, Alicia, Juan R. Torregrosa, & María P. Vassileva. (2011). Three-step iterative methods with optimal eighth-order convergence. Journal of Computational and Applied Mathematics. 235(10). 3189–3194. 60 indexed citations
19.
Cordero, Alicia, Juan R. Torregrosa, & María P. Vassileva. (2011). A family of modified Ostrowski’s methods with optimal eighth order of convergence. Applied Mathematics Letters. 24(12). 2082–2086. 26 indexed citations
20.
Cordero, Alicia, et al.. (2011). Artificial satellites preliminary orbit determination by the modified high-order Gauss method. International Journal of Computer Mathematics. 89(3). 347–356. 2 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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