Yu‐Hong Dai
- Numerical Analysis top 0.2%
- Computational Mechanics top 0.5%
- Computational Theory and Mathematics top 0.5%
- Electrical and Electronic Engineering top 5%
- Computer Networks and Communications top 2%
- Co-authors
- R. FletcherYa‐Feng LiuZhi‐Quan LuoCaixia KouYa-xiang YuanHongchao ZhangBin ZhouLi Gao
- Topics
- Advanced Optimization Algorithms Research (67 papers)Sparse and Compressive Sensing Techniques (49 papers)Optimization and Variational Analysis (25 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE Transactions on Automatic ControlEuropean Journal of Operational Research
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Yu‐Hong Dai
119 papers receiving 3.1k citations
Hit Papers
Peers
Comparison fields: 5 of 125
- Numerical Analysis 1.6k
- Computational Mechanics 1.2k
- Computational Theory and Mathematics 1.1k
- Electrical and Electronic Engineering 656
- Computer Networks and Communications 468
Countries citing papers authored by Yu‐Hong Dai
This map shows the geographic impact of Yu‐Hong Dai'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 Yu‐Hong Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yu‐Hong Dai more than expected).
Fields of papers citing papers by Yu‐Hong Dai
This network shows the impact of papers produced by Yu‐Hong Dai. 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 Yu‐Hong Dai. The network helps show where Yu‐Hong Dai may publish in the future.
Co-authorship network of co-authors of Yu‐Hong Dai
This figure shows the co-authorship network connecting the top 25 collaborators of Yu‐Hong Dai. A scholar is included among the top collaborators of Yu‐Hong Dai 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 Yu‐Hong Dai. Yu‐Hong Dai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 0 | |
| 4 | 7 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 3 | |
| 8 | 7 | |
| 9 | 1 | |
| 10 | 1 | |
| 11 | 27 | |
| 12 | 3 | |
| 13 | 10 | |
| 14 | 2 | |
| 15 | Barzilai-Borwein step size for stochastic gradient descent | 40 |
| 16 | 10 | |
| 17 | Kernel Learning by Unconstrained Optimization | 12 |
| 18 | 6 | |
| 19 | 217 | |
| 20 | 8 |
About Yu‐Hong Dai
Yu‐Hong Dai is a scholar working on Numerical Analysis, Computational Mathematics and Computational Theory and Mathematics, having authored 129 papers that have together received 3.3k indexed citations. Recurring topics across this work include Advanced Optimization Algorithms Research (67 papers), Sparse and Compressive Sensing Techniques (49 papers) and Optimization and Variational Analysis (25 papers). The work is most often cited by research in Numerical Analysis (1.6k citations), Computational Mathematics (170 citations) and Computational Theory and Mathematics (1.1k citations). Yu‐Hong Dai has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include R. Fletcher, Ya‐Feng Liu, Zhi‐Quan Luo, Caixia Kou, Ya-xiang Yuan, Hongchao Zhang, Bin Zhou, Li Gao, Xinwei Liu and Chunfeng Cui. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Automatic Control and European Journal of Operational Research.
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.