Big data approach to Kazhdan-Lusztig polynomials
This work addresses a specific mathematical problem in representation theory, providing computational insights that could aid researchers in this domain, but it is incremental as it applies existing big data techniques to a known dataset.
The authors tackled the problem of understanding the structure of Kazhdan-Lusztig polynomials for the symmetric group by applying big data computational methods, such as exploratory and topological data analysis, to analyze polynomials for groups with up to 11 strands, revealing patterns and insights into their properties.
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.