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[2412.01283] Big data approach to Kazhdan-Lusztig polynomials
Summary
This article explores the application of big data techniques to analyze Kazhdan-Lusztig polynomials, focusing on their structure within symmetric groups of up to 11 strands.
Why It Matters
Understanding Kazhdan-Lusztig polynomials is crucial in representation theory and combinatorics. This research leverages big data methodologies, potentially offering new insights and computational techniques that could enhance mathematical research and applications in related fields.
Key Takeaways
- The study applies big data approaches to traditional mathematical problems.
- Kazhdan-Lusztig polynomials are analyzed for symmetric groups up to 11 strands.
- The research may lead to innovative computational techniques in representation theory.
- Exploratory and topological data analysis methods are utilized.
- Findings could influence future research in combinatorics and machine learning.
Mathematics > Representation Theory arXiv:2412.01283 (math) [Submitted on 2 Dec 2024 (v1), last revised 24 Feb 2026 (this version, v2)] Title:Big data approach to Kazhdan-Lusztig polynomials Authors:Abel Lacabanne, Daniel Tubbenhauer, Pedro Vaz View a PDF of the paper titled Big data approach to Kazhdan-Lusztig polynomials, by Abel Lacabanne and 1 other authors View PDF 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. Comments: Subjects: Representation Theory (math.RT); Machine Learning (cs.LG); Combinatorics (math.CO) MSC classes: Primary: 05E10, 62R07, secondary: 20C08, 68P05 Cite as: arXiv:2412.01283 [math.RT] (or arXiv:2412.01283v2 [math.RT] for this version) https://doi.org/10.48550/arXiv.2412.01283 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Journal of Experimental Mathematics, 2(1):21-62, 2026 Related DOI: https://doi.org/10.56994/JXM.002.001.002 Focus to learn more DOI(s) linking to related resources Submission history From: Abel Lacabanne [view email] [v1] Mon, 2 Dec 2024 08:55:44 UTC (3,758 KB) [v2] Tue, 24 Feb 2026 07:55:05 UTC (3,704 KB) Full-text links: Access Paper: View a PDF of the paper titled Big data approach to Kazhdan-Lusztig polynomials, by Abel Lacabanne and 1 other authorsView PDFTeX Source view license Cu...