Quoc Tran-Dinh
- Computational Mechanics top 10%
- Numerical Analysis top 5%
- Artificial Intelligence top 10%
- Computational Theory and Mathematics top 10%
- Control and Systems Engineering
- Co-authors
- Ion NecoaraMoritz DiehlVolkan CevherAndrea ZanelliGábor PatakiAnastasios KyrillidisYufeng LiuAndrei Pătraşcu
- Topics
- Sparse and Compressive Sensing Techniques (16 papers)Advanced Optimization Algorithms Research (16 papers)Stochastic Gradient Optimization Techniques (11 papers)
- Partner nations
- United StatesSwitzerlandRomania
In The Last Decade
Quoc Tran-Dinh
23 papers receiving 222 citations
Peers
Comparison fields: 5 of 41
- Computational Mechanics 129
- Numerical Analysis 115
- Artificial Intelligence 100
- Computational Theory and Mathematics 56
- Control and Systems Engineering 50
Countries citing papers authored by Quoc Tran-Dinh
This map shows the geographic impact of Quoc Tran-Dinh'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 Quoc Tran-Dinh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Quoc Tran-Dinh more than expected).
Fields of papers citing papers by Quoc Tran-Dinh
This network shows the impact of papers produced by Quoc Tran-Dinh. 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 Quoc Tran-Dinh. The network helps show where Quoc Tran-Dinh may publish in the future.
Co-authorship network of co-authors of Quoc Tran-Dinh
This figure shows the co-authorship network connecting the top 25 collaborators of Quoc Tran-Dinh. A scholar is included among the top collaborators of Quoc Tran-Dinh 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 Quoc Tran-Dinh. Quoc Tran-Dinh 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 | 1 | |
| 3 | 4 | |
| 4 | 4 | |
| 5 | 3 | |
| 6 | Accelerated Primal-Dual Algorithms for a Class of Convex-Concave Saddle-Point Problems with Non-Bilinear Coupling Term | 1 |
| 7 | 1 | |
| 8 | 10 | |
| 9 | 1 | |
| 10 | 21 | |
| 11 | 9 | |
| 12 | 7 | |
| 13 | 11 | |
| 14 | 13 | |
| 15 | 14 | |
| 16 | 13 | |
| 17 | 9 | |
| 18 | Constrained convex minimization via model-based excessive gap | 5 |
| 19 | 62 | |
| 20 | 11 |
About Quoc Tran-Dinh
Quoc Tran-Dinh is a scholar working on Numerical Analysis, Computational Mathematics and Computational Mechanics, having authored 23 papers that have together received 231 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (16 papers), Advanced Optimization Algorithms Research (16 papers) and Stochastic Gradient Optimization Techniques (11 papers). The work is most often cited by research in Numerical Analysis (115 citations), Computational Mathematics (4 citations) and Computational Mechanics (129 citations). Quoc Tran-Dinh has collaborated with scholars based in United States, Switzerland and Romania. Frequent co-authors include Ion Necoara, Moritz Diehl, Volkan Cevher, Andrea Zanelli, Gábor Pataki, Anastasios Kyrillidis, Yufeng Liu, Andrei Pătraşcu, Michael R. Kosorok and Dzung T. Phan. Their work appears in journals such as Automatica, Journal of Machine Learning Research and Mathematical Programming.
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.