Zhou Sheng

442 total citations
25 papers, 280 citations indexed

About

Zhou Sheng is a scholar working on Numerical Analysis, Computational Theory and Mathematics and Computational Mechanics. According to data from OpenAlex, Zhou Sheng has authored 25 papers receiving a total of 280 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Numerical Analysis, 10 papers in Computational Theory and Mathematics and 9 papers in Computational Mechanics. Recurrent topics in Zhou Sheng's work include Advanced Optimization Algorithms Research (15 papers), Iterative Methods for Nonlinear Equations (8 papers) and Sparse and Compressive Sensing Techniques (7 papers). Zhou Sheng is often cited by papers focused on Advanced Optimization Algorithms Research (15 papers), Iterative Methods for Nonlinear Equations (8 papers) and Sparse and Compressive Sensing Techniques (7 papers). Zhou Sheng collaborates with scholars based in China and United Kingdom. Zhou Sheng's co-authors include Gonglin Yuan, Aijia Ouyang, Christopher B. Jones, Wenjie Liu, Fan Wu, Ting‐Zhu Huang, Qin Ni, Xiangrong Li, Luming Zhang and Deshi Ye and has published in prestigious journals such as PLoS ONE, Scientific Reports and Journal of Computational Physics.

In The Last Decade

Zhou Sheng

21 papers receiving 274 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Zhou Sheng China	11	194	128	114	33	19	25	280
Mehiddin Al‐Baali Oman	9	127 0.7×	70 0.5×	77 0.7×	18 0.5×	15 0.8×	17	215
Max L. N. Gonçalves Brazil	11	214 1.1×	90 0.7×	169 1.5×	9 0.3×	34 1.8×	29	284
Wah June Leong Malaysia	10	243 1.3×	123 1.0×	155 1.4×	20 0.6×	18 0.9×	35	357
Zhen-Jun Shi China	12	276 1.4×	98 0.8×	175 1.5×	34 1.0×	8 0.4×	26	377
Hakop Hakopian Armenia	8	50 0.3×	154 1.2×	82 0.7×	21 0.6×	12 0.6×	28	281
Yasushi Narushima Japan	12	429 2.2×	221 1.7×	216 1.9×	54 1.6×	51 2.7×	26	528
Yun-Bin Zhao China	13	329 1.7×	89 0.7×	374 3.3×	19 0.6×	38 2.0×	31	516
Mounir Haddou France	9	272 1.4×	131 1.0×	274 2.4×	10 0.3×	14 0.7×	26	402
Florencio I. Utreras Chile	10	57 0.3×	137 1.1×	51 0.4×	61 1.8×	31 1.6×	16	310
Martin Marsden United States	8	150 0.8×	201 1.6×	51 0.4×	26 0.8×	7 0.4×	16	318

Countries citing papers authored by Zhou Sheng

Since Specialization

Citations

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

Fields of papers citing papers by Zhou Sheng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Zhou Sheng. 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 Zhou Sheng. The network helps show where Zhou Sheng may publish in the future.

Co-authorship network of co-authors of Zhou Sheng

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Sheng, Zhou, et al.. (2024). Exploring the correlation between DNA methylation and biological age using an interpretable machine learning framework. Scientific Reports. 14(1). 24208–24208.

Sheng, Zhou, et al.. (2023). A family of gradient methods using Householder transformation with application to hypergraph partitioning. Numerical Algorithms. 95(2). 897–927.

Sheng, Zhou, Jianze Li, & Qin Ni. (2023). Jacobi-type algorithms for homogeneous polynomial optimization on Stiefel manifolds with applications to tensor approximations. Mathematics of Computation. 92(343). 2217–2245.

Sheng, Zhou, et al.. (2023). High-order Lagrange multiplier method for the coupled Klein-Gordon-Schrödinger system. Journal of Computational Physics. 493. 112456–112456. 7 indexed citations

Sheng, Zhou, Weiwei Yang, & Jie Wen. (2022). A Riemannian gradient ascent algorithm with applications to orthogonal approximation problems of symmetric tensors. Applied Numerical Mathematics. 182. 235–247. 1 indexed citations

Sheng, Zhou & Qin Ni. (2021). Computing tensor Z-eigenvalues via shifted inverse power method. Journal of Computational and Applied Mathematics. 398. 113717–113717. 4 indexed citations

Sheng, Zhou, Qin Ni, & Gonglin Yuan. (2019). Local convergence analysis of inverse iteration algorithm for computing the H-spectral radius of a nonnegative weakly irreducible tensor. Journal of Computational and Applied Mathematics. 357. 26–37. 1 indexed citations

Yuan, Gonglin, et al.. (2019). Family weak conjugate gradient algorithms and their convergence analysis for nonconvex functions. Numerical Algorithms. 84(3). 935–956. 21 indexed citations

Yuan, Gonglin, et al.. (2018). An active-set algorithm for solving large-scale nonsmooth optimization models with box constraints. PLoS ONE. 13(1). e0189290–e0189290. 10 indexed citations

10.

Sheng, Zhou, et al.. (2018). A new adaptive trust region algorithm for optimization problems. Acta Mathematica Scientia. 38(2). 479–496. 18 indexed citations

11.

Yuan, Gonglin, et al.. (2018). The Hager–Zhang conjugate gradient algorithm for large-scale nonlinear equations. International Journal of Computer Mathematics. 96(8). 1533–1547. 11 indexed citations

12.

Sheng, Zhou & Gonglin Yuan. (2018). An effective adaptive trust region algorithm for nonsmooth minimization. Computational Optimization and Applications. 71(1). 251–271. 9 indexed citations

13.

Sheng, Zhou, et al.. (2017). An adaptive trust region algorithm for large-residual nonsmooth least squares problems. Journal of Industrial and Management Optimization. 14(2). 707–718. 11 indexed citations

14.

Yuan, Gonglin, et al.. (2017). The global convergence of a modified BFGS method for nonconvex functions. Journal of Computational and Applied Mathematics. 327. 274–294. 69 indexed citations

15.

Li, Xiangrong, et al.. (2017). A modified conjugate gradient algorithm with backtracking line search technique for large-scale nonlinear equations. International Journal of Computer Mathematics. 95(2). 382–395. 8 indexed citations

16.

Yuan, Gonglin, Zhou Sheng, & Wenjie Liu. (2016). The Modified HZ Conjugate Gradient Algorithm for Large-Scale Nonsmooth Optimization. PLoS ONE. 11(10). e0164289–e0164289. 10 indexed citations

17.

Yuan, Gonglin, et al.. (2015). A Modified BFGS Formula Using a Trust Region Model for Nonsmooth Convex Minimizations. PLoS ONE. 10(10). e0140606–e0140606. 4 indexed citations

18.

Yuan, Gonglin, et al.. (2015). Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models. PLoS ONE. 10(10). e0140071–e0140071. 18 indexed citations

19.

Ouyang, Aijia, et al.. (2015). A Class of Parameter Estimation Methods for Nonlinear Muskingum Model Using Hybrid Invasive Weed Optimization Algorithm. Mathematical Problems in Engineering. 2015. 1–15. 25 indexed citations

20.

Sheng, Zhou, et al.. (2014). A Novel Parameter Estimation Method for Muskingum Model Using New Newton‐Type Trust Region Algorithm. Mathematical Problems in Engineering. 2014(1). 11 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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