تقرير
Machine-Learning Kronecker Coefficients
| العنوان: | Machine-Learning Kronecker Coefficients |
|---|---|
| المؤلفون: | Lee, Kyu-Hwan |
| سنة النشر: | 2023 |
| المجموعة: | Mathematics Statistics |
| مصطلحات موضوعية: | Mathematics - Representation Theory, Mathematics - Combinatorics, Statistics - Machine Learning |
| الوصف: | The Kronecker coefficients are the decomposition multiplicities of the tensor product of two irreducible representations of the symmetric group. Unlike the Littlewood--Richardson coefficients, which are the analogues for the general linear group, there is no known combinatorial description of the Kronecker coefficients, and it is an NP-hard problem to decide whether a given Kronecker coefficient is zero or not. In this paper, we show that standard machine-learning algorithms such as Nearest Neighbors, Convolutional Neural Networks and Gradient Boosting Decision Trees may be trained to predict whether a given Kronecker coefficient is zero or not. Our results show that a trained machine can efficiently perform this binary classification with high accuracy ($\approx 0.98$). |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2306.04734 |
| رقم الأكسشن: | edsarx.2306.04734 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |