James V. Burke
- Computational Theory and Mathematics top 0.1%
- Numerical Analysis top 0.2%
- Control and Systems Engineering top 1%
- Computational Mechanics top 1%
- Artificial Intelligence top 5%
- Co-authors
- Michael L. OvertonAdrian S. LewisMichael C. FerrisJorge J. MorèSien DengDidier HenrionAleksandr Y. AravkinSong Xu
- Topics
- Advanced Optimization Algorithms Research (47 papers)Optimization and Variational Analysis (35 papers)Sparse and Compressive Sensing Techniques (22 papers)
- Partner nations
- United StatesCanadaItaly
In The Last Decade
James V. Burke
88 papers receiving 3.0k citations
Peers
Comparison fields: 5 of 127
- Computational Theory and Mathematics 2.0k
- Numerical Analysis 1.8k
- Control and Systems Engineering 964
- Computational Mechanics 812
- Artificial Intelligence 334
Countries citing papers authored by James V. Burke
This map shows the geographic impact of James V. Burke'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 James V. Burke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James V. Burke more than expected).
Fields of papers citing papers by James V. Burke
This network shows the impact of papers produced by James V. Burke. 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 James V. Burke. The network helps show where James V. Burke may publish in the future.
Co-authorship network of co-authors of James V. Burke
This figure shows the co-authorship network connecting the top 25 collaborators of James V. Burke. A scholar is included among the top collaborators of James V. Burke 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 James V. Burke. James V. Burke is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 7 | |
| 3 | 4 | |
| 4 | Convex vs non-convex estimators for regression and sparse estimation: the mean squared error properties of ARD and GLasso | 37 |
| 5 | 16 | |
| 6 | 23 | |
| 7 | 13 | |
| 8 | 5 | |
| 9 | 7 | |
| 10 | 101 | |
| 11 | 29 | |
| 12 | Weak sharp minima revisited Part I: basic theory | 61 |
| 13 | 59 | |
| 14 | 31 | |
| 15 | Notes On Limited Memory BFGS Updating In A Trust-Region Framework | 2 |
| 16 | 42 | |
| 17 | 54 | |
| 18 | 13 | |
| 19 | 50 | |
| 20 | 105 |
About James V. Burke
James V. Burke is a scholar working on Numerical Analysis, Computational Theory and Mathematics and Mathematical Physics, having authored 90 papers that have together received 3.5k indexed citations. Recurring topics across this work include Advanced Optimization Algorithms Research (47 papers), Optimization and Variational Analysis (35 papers) and Sparse and Compressive Sensing Techniques (22 papers). The work is most often cited by research in Numerical Analysis (1.8k citations), Computational Theory and Mathematics (2.0k citations) and Control and Systems Engineering (964 citations). James V. Burke has collaborated with scholars based in United States, Canada and Italy. Frequent co-authors include Michael L. Overton, Adrian S. Lewis, Michael C. Ferris, Jorge J. Morè, Sien Deng, Didier Henrion, Aleksandr Y. Aravkin, Song Xu, Gianluigi Pillonetto and D. Lüke. Their work appears in journals such as IEEE Transactions on Automatic Control, IEEE Transactions on Information Theory and Automatica.
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.