Zheng‐Hai Huang

3.4k total citations
139 papers, 2.4k citations indexed

About

Zheng‐Hai Huang is a scholar working on Computational Theory and Mathematics, Numerical Analysis and Computational Mathematics. According to data from OpenAlex, Zheng‐Hai Huang has authored 139 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 82 papers in Computational Theory and Mathematics, 69 papers in Numerical Analysis and 53 papers in Computational Mathematics. Recurrent topics in Zheng‐Hai Huang's work include Advanced Optimization Algorithms Research (66 papers), Matrix Theory and Algorithms (55 papers) and Tensor decomposition and applications (53 papers). Zheng‐Hai Huang is often cited by papers focused on Advanced Optimization Algorithms Research (66 papers), Matrix Theory and Algorithms (55 papers) and Tensor decomposition and applications (53 papers). Zheng‐Hai Huang collaborates with scholars based in China, Hong Kong and United States. Zheng‐Hai Huang's co-authors include Liqun Qi, Shenglong Hu, Yong Wang, Xueli Bai, Jiye Han, Kun Shang, Chen Ling, Defeng Sun, Yang Lei and Junqiang Han and has published in prestigious journals such as IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing and Information Sciences.

In The Last Decade

Zheng‐Hai Huang

129 papers receiving 2.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Zheng‐Hai Huang China 27 1.6k 1.3k 1.1k 492 278 139 2.4k
Guanglu Zhou Australia 21 1.1k 0.7× 836 0.7× 879 0.8× 321 0.7× 76 0.3× 68 1.8k
Bernard Mourrain France 28 1.3k 0.8× 282 0.2× 508 0.5× 1.5k 3.1× 343 1.2× 157 3.0k
Jiawang Nie United States 27 1.4k 0.9× 1.2k 0.9× 546 0.5× 358 0.7× 39 0.1× 83 2.0k
Moody T. Chu United States 27 1.4k 0.9× 840 0.7× 181 0.2× 426 0.9× 214 0.8× 97 2.6k
Hédy Attouch France 22 1.9k 1.2× 1.3k 1.1× 138 0.1× 1.5k 3.1× 465 1.7× 44 3.3k
Shoham Sabach Israel 18 1.0k 0.6× 773 0.6× 89 0.1× 870 1.8× 414 1.5× 37 2.1k
Gaohang Yu China 19 392 0.2× 478 0.4× 375 0.3× 426 0.9× 250 0.9× 83 1.5k
Yangyang Xu United States 18 313 0.2× 355 0.3× 476 0.4× 1.4k 2.8× 793 2.9× 72 2.5k
Wenyu Sun China 14 669 0.4× 870 0.7× 127 0.1× 422 0.9× 81 0.3× 69 1.4k

Countries citing papers authored by Zheng‐Hai Huang

Since Specialization
Citations

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

Fields of papers citing papers by Zheng‐Hai Huang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Zheng‐Hai Huang. 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 Zheng‐Hai Huang. The network helps show where Zheng‐Hai Huang may publish in the future.

Co-authorship network of co-authors of Zheng‐Hai Huang

This figure shows the co-authorship network connecting the top 25 collaborators of Zheng‐Hai Huang. A scholar is included among the top collaborators of Zheng‐Hai Huang 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 Zheng‐Hai Huang. Zheng‐Hai Huang 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.
Huang, Zheng‐Hai, et al.. (2025). Tensor Robust Principal Component Analysis Based on a Two-Layer Tucker Rank Minimization Model. SIAM Journal on Imaging Sciences. 18(2). 1522–1561.
2.
He, Tiantian, Shenglong Hu, & Zheng‐Hai Huang. (2024). A Low-Rank Tensor Completion Method via Strassen–Ottaviani Flattening. SIAM Journal on Imaging Sciences. 17(4). 2242–2276. 1 indexed citations
3.
Sun, Zhongfeng, Yun-Bin Zhao, Jinchuan Zhou, & Zheng‐Hai Huang. (2024). Dynamic thresholding algorithm with memory for linear inverse problems. Inverse Problems. 41(1). 15004–15004.
4.
Zhang, Ting, Yong Wang, & Zheng‐Hai Huang. (2024). Projected fixed-point method for vertical tensor complementarity problems. Computational Optimization and Applications. 89(1). 219–245. 2 indexed citations
5.
Wang, Yong, et al.. (2023). A linearized method for solving tensor complementarity problems with implicit Z-tensors. Optimization Letters. 18(5). 1151–1171. 2 indexed citations
6.
Huang, Zheng‐Hai, et al.. (2023). Existence of the least element solution of the vertical block Z-tensor complementarity problem. Optimization Letters. 17(7). 1697–1709. 7 indexed citations
7.
Huang, Zheng‐Hai, et al.. (2023). Tensor Robust Principal Component Analysis via Tensor Fibered Rank and \({\boldsymbol{{l_p}}}\) Minimization. SIAM Journal on Imaging Sciences. 16(1). 423–460. 7 indexed citations
8.
Guo, Huiyuan, Zheng‐Hai Huang, & Xinzhen Zhang. (2022). Low rank matrix recovery with impulsive noise. Applied Mathematics Letters. 134. 108364–108364. 2 indexed citations
9.
Huang, Zheng‐Hai, et al.. (2022). Unconstrained minimization of block-circulant polynomials via semidefinite program in third-order tensor space. Journal of Global Optimization. 84(2). 415–440.
10.
Huang, Zheng‐Hai, et al.. (2021). Nonemptiness and Compactness of Solution Sets to Weakly Homogeneous Generalized Variational Inequalities. Journal of Optimization Theory and Applications. 189(3). 919–937. 1 indexed citations
11.
Bai, Xueli, et al.. (2021). Unique solvability of weakly homogeneous generalized variational inequalities. Journal of Global Optimization. 80(4). 921–943. 1 indexed citations
12.
Yang, Xu, et al.. (2021). A homotopy method for solving multilinear systems with strong completely positive tensors. Applied Mathematics Letters. 124. 107636–107636. 6 indexed citations
13.
Yang, Xu & Zheng‐Hai Huang. (2021). Bounds of the solution set of the tensor complementarity problem. Optimization Letters. 15(8). 2701–2718. 11 indexed citations
14.
Huang, Zheng‐Hai, et al.. (2020). T-positive semidefiniteness of third-order symmetric tensors and T-semidefinite programming. Computational Optimization and Applications. 78(1). 239–272. 22 indexed citations
15.
Huang, Zheng‐Hai, et al.. (2020). Nonemptiness and Compactness of Solution Sets to Generalized Polynomial Complementarity Problems. Journal of Optimization Theory and Applications. 185(1). 80–98. 13 indexed citations
16.
Huang, Zheng‐Hai, et al.. (2020). A Note on the Nonemptiness and Compactness of Solution Sets of Weakly Homogeneous Variational Inequalities. SIAM Journal on Optimization. 30(1). 132–148. 11 indexed citations
17.
Huang, Zheng‐Hai, et al.. (2019). The sparsest solution of the union of finite polytopes via its nonconvex relaxation. Mathematical Methods of Operations Research. 89(3). 485–507. 1 indexed citations
18.
Wang, Jing, Zheng‐Hai Huang, & Xu Yang. (2019). Existence and uniqueness of solutions of the generalized polynomial variational inequality. Optimization Letters. 14(6). 1571–1582. 9 indexed citations
19.
Wang, Yong, Zheng‐Hai Huang, & Liqun Qi. (2018). Global Uniqueness and Solvability of Tensor Variational Inequalities. Journal of Optimization Theory and Applications. 177(1). 137–152. 43 indexed citations
20.
Huang, Zheng‐Hai. (1998). D ∞ -APPROXIMATION OF QUADRATIC VARIATIONS OF SMOOTH IT PROCESSES ***.

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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