André Uschmajew
- Computational Mathematics top 0.1%
- Computational Mechanics top 5%
- Computational Theory and Mathematics top 2%
- Statistical and Nonlinear Physics top 5%
- Numerical Analysis top 5%
- Co-authors
- Reinhold SchneiderBart VandereyckenDaniel KreßnerThorsten RohwedderMarkus BachmayrLars KarlssonMichael SteinlechnerZhening Li
- Topics
- Tensor decomposition and applications (26 papers)Matrix Theory and Algorithms (21 papers)Sparse and Compressive Sensing Techniques (19 papers)
- Journals
- IEEE Transactions on Neural Networks and Learning SystemsSIAM Journal on Numerical AnalysisMathematical Programming
- Partner nations
- GermanySwitzerlandUnited Kingdom
In The Last Decade
André Uschmajew
33 papers receiving 701 citations
Peers
Comparison fields: 5 of 65
- Computational Mathematics 522
- Computational Mechanics 311
- Computational Theory and Mathematics 262
- Statistical and Nonlinear Physics 168
- Numerical Analysis 109
Countries citing papers authored by André Uschmajew
This map shows the geographic impact of André Uschmajew'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 André Uschmajew with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites André Uschmajew more than expected).
Fields of papers citing papers by André Uschmajew
This network shows the impact of papers produced by André Uschmajew. 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 André Uschmajew. The network helps show where André Uschmajew may publish in the future.
Co-authorship network of co-authors of André Uschmajew
This figure shows the co-authorship network connecting the top 25 collaborators of André Uschmajew. A scholar is included among the top collaborators of André Uschmajew 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 André Uschmajew. André Uschmajew is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 4 | |
| 3 | 2 | |
| 4 | 11 | |
| 5 | 19 | |
| 6 | 7 | |
| 7 | 16 | |
| 8 | 20 | |
| 9 | 68 | |
| 10 | 9 | |
| 11 | A New Convergence Proof For The Higher-Order Power Method And Generalizations | 16 |
| 12 | 2 | |
| 13 | 22 | |
| 14 | 15 | |
| 15 | 39 | |
| 16 | 65 | |
| 17 | 59 | |
| 18 | 100 | |
| 19 | 6 | |
| 20 | 9 |
About André Uschmajew
André Uschmajew is a scholar working on Computational Mathematics, Numerical Analysis and Computational Theory and Mathematics, having authored 35 papers that have together received 758 indexed citations. Recurring topics across this work include Tensor decomposition and applications (26 papers), Matrix Theory and Algorithms (21 papers) and Sparse and Compressive Sensing Techniques (19 papers). The work is most often cited by research in Computational Mathematics (522 citations), Numerical Analysis (109 citations) and Computational Theory and Mathematics (262 citations). André Uschmajew has collaborated with scholars based in Germany, Switzerland and United Kingdom. Frequent co-authors include Reinhold Schneider, Bart Vandereycken, Daniel Kreßner, Thorsten Rohwedder, Markus Bachmayr, Lars Karlsson, Michael Steinlechner, Zhening Li, Yuji Nakatsukasa and Wolfgang Hackbusch. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, SIAM Journal on Numerical Analysis 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.