Big Data Approach to Kazhdan-Lusztig Polynomials

Abel Lacabanne; Daniel Tubbenhauer; Pedro Vaz

doi:10.56994/JXM.002.001.002

Big Data Approach to Kazhdan-Lusztig Polynomials

Authors

Abel Lacabanne Université Clermont Auvergne, LMBP https://orcid.org/0000-0001-8691-3270
Daniel Tubbenhauer The University of Sydney https://orcid.org/0000-0001-7265-5047
Pedro Vaz Université catholique de Louvain, IRMP https://orcid.org/0000-0001-9422-4707

DOI:

https://doi.org/10.56994/JXM.002.001.002

Keywords:

Visualization, Exploratory data analysis, Topological data analysis, Conjecturing, Kazhdan-Lusztig polynomials

Abstract

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.

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Published

03/01/2026

How to Cite

Lacabanne, A., Tubbenhauer, D., & Vaz, P. (2026). Big Data Approach to Kazhdan-Lusztig Polynomials. Journal of Experimental Mathematics, 2(1), 21–62. https://doi.org/10.56994/JXM.002.001.002