Applied Random Matrix Theory
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910177898266624 |
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| author | Tropp, Joel A. |
| author_facet | Tropp, Joel A. |
| contents | Random matrices now play a role in many parts of computational mathematics. To advance these applications, it is desirable to have tools that are flexible, easy to use, and powerful. Over the last 25 years, researchers have developed a remarkable family of results, called matrix concentration inequalities, that meet the criteria. This paper offers an invitation to the field of matrix concentration and its multifarious applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_27119 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Applied Random Matrix Theory Tropp, Joel A. Probability Numerical Analysis 15B52, 60B20 Random matrices now play a role in many parts of computational mathematics. To advance these applications, it is desirable to have tools that are flexible, easy to use, and powerful. Over the last 25 years, researchers have developed a remarkable family of results, called matrix concentration inequalities, that meet the criteria. This paper offers an invitation to the field of matrix concentration and its multifarious applications. |
| title | Applied Random Matrix Theory |
| topic | Probability Numerical Analysis 15B52, 60B20 |
| url | https://arxiv.org/abs/2604.27119 |