Wen-Ting Wu

732 total citations
16 papers, 471 citations indexed

About

Wen-Ting Wu is a scholar working on Artificial Intelligence, Computational Mechanics and Numerical Analysis. According to data from OpenAlex, Wen-Ting Wu has authored 16 papers receiving a total of 471 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 10 papers in Computational Mechanics and 8 papers in Numerical Analysis. Recurrent topics in Wen-Ting Wu's work include Stochastic Gradient Optimization Techniques (11 papers), Sparse and Compressive Sensing Techniques (10 papers) and Advanced Optimization Algorithms Research (7 papers). Wen-Ting Wu is often cited by papers focused on Stochastic Gradient Optimization Techniques (11 papers), Sparse and Compressive Sensing Techniques (10 papers) and Advanced Optimization Algorithms Research (7 papers). Wen-Ting Wu collaborates with scholars based in China, United States and Taiwan. Wen-Ting Wu's co-authors include Zhong‐Zhi Bai, Qi Zhu and Jyh‐Cheng Chen and has published in prestigious journals such as SIAM Journal on Scientific Computing, Journal of Computational and Applied Mathematics and Linear Algebra and its Applications.

In The Last Decade

Wen-Ting Wu

15 papers receiving 441 citations

Peers

Wen-Ting Wu
Anna Ma United States
Arthur G. Werschulz United States
Matt Menickelly United States
Douglas S. Gonçalves Brazil
Борис Сергеевич Кашин Russia
Yura Malitsky Sweden
Yiqin Lin China
Khalide Jbilou France
Shu-Qian Shen China
Nick Vannieuwenhoven Belgium
Anna Ma United States
Wen-Ting Wu
Citations per year, relative to Wen-Ting Wu Wen-Ting Wu (= 1×) peers Anna Ma

Countries citing papers authored by Wen-Ting Wu

Since Specialization
Citations

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

Fields of papers citing papers by Wen-Ting Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wen-Ting Wu. 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 Wen-Ting Wu. The network helps show where Wen-Ting Wu may publish in the future.

Co-authorship network of co-authors of Wen-Ting Wu

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

All Works

16 of 16 papers shown
1.
2.
Bai, Zhong‐Zhi & Wen-Ting Wu. (2023). Randomized Kaczmarz iteration methods: Algorithmic extensions and convergence theory. Japan Journal of Industrial and Applied Mathematics. 40(3). 1421–1443. 16 indexed citations
3.
Wu, Wen-Ting, et al.. (2022). On greedy randomized average block Kaczmarz method for solving large linear systems. Journal of Computational and Applied Mathematics. 413. 114372–114372. 27 indexed citations
4.
Wu, Wen-Ting. (2022). On Convergence of the Partially Randomized Extended Kaczmarz Method. East Asian Journal on Applied Mathematics. 12(2). 435–448. 5 indexed citations
5.
Wu, Wen-Ting, et al.. (2021). On relaxed filtered Krylov subspace method for non-symmetric eigenvalue problems. Journal of Computational and Applied Mathematics. 398. 113698–113698. 1 indexed citations
6.
Bai, Zhong‐Zhi & Wen-Ting Wu. (2021). On Greedy Randomized Augmented Kaczmarz Method for Solving Large Sparse Inconsistent Linear Systems. SIAM Journal on Scientific Computing. 43(6). A3892–A3911. 31 indexed citations
7.
Wu, Wen-Ting. (2021). On two-subspace randomized extended Kaczmarz method for solving large linear least-squares problems. Numerical Algorithms. 89(1). 1–31. 26 indexed citations
8.
Bai, Zhong‐Zhi, et al.. (2020). On convergence rate of the randomized Gauss-Seidel method. Linear Algebra and its Applications. 611. 237–252. 15 indexed citations
9.
Bai, Zhong‐Zhi, et al.. (2020). The power method and beyond. Applied Numerical Mathematics. 164. 29–42. 11 indexed citations
10.
Wu, Wen-Ting. (2019). On Minimization of Upper Bound for the Convergence Rate of the QHSS Iteration Method. Communications on Applied Mathematics and Computation. 1(2). 263–282. 3 indexed citations
11.
Bai, Zhong‐Zhi & Wen-Ting Wu. (2019). On partially randomized extended Kaczmarz method for solving large sparse overdetermined inconsistent linear systems. Linear Algebra and its Applications. 578. 225–250. 56 indexed citations
12.
Bai, Zhong‐Zhi & Wen-Ting Wu. (2018). On relaxed greedy randomized Kaczmarz methods for solving large sparse linear systems. Applied Mathematics Letters. 83. 21–26. 78 indexed citations
13.
Wu, Wen-Ting, et al.. (2018). Circularly Polarization Loop Antenna. 82–84. 3 indexed citations
14.
Bai, Zhong‐Zhi & Wen-Ting Wu. (2018). On convergence rate of the randomized Kaczmarz method. Linear Algebra and its Applications. 553. 252–269. 48 indexed citations
15.
Bai, Zhong‐Zhi & Wen-Ting Wu. (2018). On Greedy Randomized Kaczmarz Method for Solving Large Sparse Linear Systems. SIAM Journal on Scientific Computing. 40(1). A592–A606. 149 indexed citations
16.
Wu, Wen-Ting, et al.. (2005). Design and implementation of WIRE diameter. 2759. 428–433. 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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