Big data approach to Kazhdan-Lusztig polynomials
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914345118597120 |
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| author | Lacabanne, Abel Tubbenhauer, Daniel Vaz, Pedro |
| author_facet | Lacabanne, Abel Tubbenhauer, Daniel Vaz, Pedro |
| contents | We investigate the structure of Kazhdan-Lusztig polynomials of the symmetric group by leveraging computational approaches from big data, including exploratory and topological data analysis, applied to the polynomials for symmetric groups of up to 11 strands. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01283 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Big data approach to Kazhdan-Lusztig polynomials Lacabanne, Abel Tubbenhauer, Daniel Vaz, Pedro Representation Theory Machine Learning Combinatorics Primary: 05E10, 62R07, secondary: 20C08, 68P05 We investigate the structure of Kazhdan-Lusztig polynomials of the symmetric group by leveraging computational approaches from big data, including exploratory and topological data analysis, applied to the polynomials for symmetric groups of up to 11 strands. |
| title | Big data approach to Kazhdan-Lusztig polynomials |
| topic | Representation Theory Machine Learning Combinatorics Primary: 05E10, 62R07, secondary: 20C08, 68P05 |
| url | https://arxiv.org/abs/2412.01283 |