|
|
--- |
|
|
license: cc-by-2.0 |
|
|
pretty_name: Coefficients on Kazhdan–Lusztig polynomials for permutations of size 5 |
|
|
--- |
|
|
|
|
|
# The Coefficients of Kazhdan-Lusztig Polynomials for Permutations of Size 5 |
|
|
|
|
|
Kazhdan-Lusztig (KL) polynomials are polynomials in a variable \\(q\\) and |
|
|
with integer coefficients that (for our purposes) are indexed by a pair of permutations [1]. |
|
|
We will write the KL polynomial associated with permutations \\(\sigma\\) and \\(\nu\\) as |
|
|
\\(P_{\sigma,\nu}(q)\\). For example, the KL polynomial associated with permutations |
|
|
\\(\sigma = 1 \; 4 \; 3 \; 2 \; 7 \; 6 \; 5 \; 10 \; 9 \; 8 \; 11\\) and |
|
|
\\(\nu = 4 \; 6 \; 7 \; 8 \; 9 \; 10 \; 1 \; 11 \; 2 \; 3 \; 5\\) is |
|
|
|
|
|
\\(P_{\sigma,\nu}(q) = 1 + 16q + 103q^2 + 337q^3 + 566q^4 + 529q^5 + 275q^6 + 66q^7 + 3q^8\\) |
|
|
|
|
|
(see [here](https://gswarrin.w3.uvm.edu/research/klc/klc.html) for efficient software to compute |
|
|
these polynomials). KL polynomials have deep connections throughout several areas of mathematics. For example, |
|
|
KL polynomials are related to the dimensions of intersection homology in Schubert calculus, |
|
|
the study of the Hecke algebra, and representation theory of the symmetric group. They |
|
|
can be computed via a recursive formula [[1]](https://link.springer.com/article/10.1007/BF01390031), |
|
|
nevertheless, in many ways they remain mysterious. For instance, there is no known closed |
|
|
formula for the degree of \\(P_{\sigma,\nu}(q)\\). |
|
|
|
|
|
One family of questions revolve around the coefficients of \\(P_{\sigma,\nu}(q)\\). |
|
|
For instance, it has been hypothesized that the coefficient on the largest possible monomial term |
|
|
\\(q^{(\ell(\sigma) - \ell(\nu)-1)/2}\\) (where \\(\ell(x)\\) is a statistic of the |
|
|
permutation \\(x\\) called the *length* of the permutation), which is known as the |
|
|
\\(\mu\\)-coefficient, has a combinatorial interpretation but currently this is not |
|
|
known. Better understanding this and other coefficients is of significant |
|
|
interest to mathematicians from a range of fields. |
|
|
|
|
|
## Dataset details |
|
|
|
|
|
Each instance in this dataset consists of a pair of permutations of \\(n,x \in S_n\\) |
|
|
along with the coefficients of the polynomial \\(P_{x,w}(q)\\). If \\(x = \;1 \;2 \;3\; 4\; 5\; 6\\), |
|
|
\\(w=4 \;5\; 6\; 1 \;2 \;3\\) and \\(P_{v,w}(q) = 1 + 4q + 4q^2 + q^3\\) |
|
|
then the coefficients field is written as `1, 4, 4, 1`. Note that coefficients are listed |
|
|
by increasing degree of the power of \\(q\\) (e.g., the coefficient on \\(1\\) comes first, |
|
|
then the coefficient on \\(q\\), then the coefficient on \\(q^2\\), etc.) |
|
|
|
|
|
We summarize the limited number of values of coefficients on \\(P_{x,w}(q)\\) take when \\(x, w \in S_5\\). |
|
|
|
|
|
**Constant Terms:** |
|
|
|
|
|
| | 0 | 1 | Total number of instances | |
|
|
|----------|----------|----------|----------| |
|
|
| Train | 8,496 | 3,024 | 11,520 | |
|
|
| Test | 2,123 | 757 | 2,880 | |
|
|
|
|
|
**Coefficients on \\(q\\):** |
|
|
|
|
|
| | 0 | 1 | 2 | Total number of instances | |
|
|
|----------|----------|----------|----------|----------| |
|
|
| Train | 11,219 | 267 | 34 | 11,520 | |
|
|
| Test | 2,793 | 77 | 10 | 2,880 | |
|
|
|
|
|
**Coefficient on \\(q^2\\):** |
|
|
|
|
|
| | 0 | 1 | Total number of instances | |
|
|
|----------|----------|----------|----------| |
|
|
| Train | 11,514 | 6 | 11,520 | |
|
|
| Test | 2,876 | 4 | 2,880 | |
|
|
|
|
|
### Kazhdan-Lusztig Polynomials for Permutations of \\(6\\) elements |
|
|
|
|
|
We summarize the limited number of values coefficients on \\(P_{x,w}(q)\\) take when \\(x, w \in S_6\\). |
|
|
|
|
|
**Constant Terms:** |
|
|
|
|
|
| | 0 | 1 | Total number of instances | |
|
|
|----------|----------|----------|----------| |
|
|
| Train | 336,071 | 78,649 | 414,720 | |
|
|
| Test | 83,922 | 19,758 | 103,680 | |
|
|
|
|
|
**Coefficients on \\(q\\):** |
|
|
|
|
|
| | 0 | 1 | 2 | 3 | 4 | Total number of instances | |
|
|
|----------|----------|----------|----------|----------|----------|----------| |
|
|
| Train | 397,386 | 13,253 | 3,483 | 535 | 63 | 414,720 | |
|
|
| Test | 99,354 | 3,311 | 883 | 117 | 15 | 103,680 | |
|
|
|
|
|
**Coefficient on \\(q^2\\):** |
|
|
|
|
|
| | 0 | 1 | 2 | 3 | 4 | Total number of instances | |
|
|
|----------|----------|----------|----------|----------|----------|----------| |
|
|
| Train | 412,707 | 1,705 | 242 | 40 | 26 | 414,720 | |
|
|
| Test | 103,177 | 441 | 46 | 8 | 8 | 103,680 | |
|
|
|
|
|
**Coefficient on \\(q^3\\):** |
|
|
|
|
|
| | 0 | 1 | Total number of instances | |
|
|
|----------|----------|----------|----------| |
|
|
| Train | 414,688 | 32 | 414,720 | |
|
|
| Test | 103,670 | 10 | 103,680 | |
|
|
|
|
|
### Kazhdan-Lusztig Polynomials for Permutations of \\(7\\) elements |
|
|
|
|
|
We summarize the limited number of values coefficients on \\(P_{x,w}(q)\\) take when \\(x, w \in S_7\\). |
|
|
|
|
|
**Constant Terms:** |
|
|
|
|
|
| | 0 | 1 | Total number of instances | |
|
|
|----------|----------|----------|----------| |
|
|
| Train | 17,479,910 | 2,841,370 | 20,321,280 | |
|
|
| Test | 4,370,771 | 709,549 | 5,080,320 | |
|
|
|
|
|
**Coefficients on \\(q\\):** |
|
|
|
|
|
| | 0 | 1 | 2 | 3 | 4 | 5 | 6 | Total number of instances | |
|
|
|----------|----------|----------|----------|----------|----------|----------|----------|----------| |
|
|
| Train | 19,291,150 | 660,600 | 266,591 | 80,173 | 18,834 | 3,221 | 711 | 20,321,280 | |
|
|
| Test | 4,822,214 | 165,768 | 66,593 | 19,963 | 4,762 | 819 | 201 | 5,080,320 | |
|
|
|
|
|
**Coefficient on \\(q^2\\):** |
|
|
|
|
|
| | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | Total number of instances | |
|
|
|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------| |
|
|
| Train | 20,072,738 | 170,412 | 46,226 | 16,227 | 7,621 | 4,023 | 1,287 | 1,153 | 785 | 350 | 152 | 139 | 121 | 42 | 4 | 20,321,280 | |
|
|
| Test | 5,017,962 | 42,748 | 11,568 | 4,021 | 1,905 | 1,065 | 349 | 287 | 183 | 86 | 40 | 37 | 47 | 22 | 5,080,320 | |
|
|
|
|
|
**Coefficient on \\(q^3\\):** |
|
|
|
|
|
| | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 15 | Total number of instances | |
|
|
|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------| |
|
|
| Train | 20,291,535 | 22,094 | 4,779 | 1,660 | 590 | 195 | 206 | 115 | 34 | 26 | 24 | 18 | 4 | 20,321,280 | |
|
|
| Test | 507,2831 | 5,498 | 1,213 | 442 | 146 | 61 | 50 | 37 | 14 | 6 | 8 | 14 | 5,080,320 | |
|
|
|
|
|
**Coefficient on \\(q^4\\):** |
|
|
|
|
|
| | 0 | 1 | Total number of instances | |
|
|
|----------|----------|----------|----------| |
|
|
| Train | 17,479,910 | 2,841,370 | 20,321,280 | |
|
|
| Test | 4,370,771 | 709,549 | 5,080,320 | |
|
|
|
|
|
|
|
|
## Data Generation |
|
|
|
|
|
Datasets were generated using C code from Greg Warrington's |
|
|
[website](https://gswarrin.w3.uvm.edu/research/klc/klc.html). The code we used can be found |
|
|
[here](https://github.com/pnnl/ML4AlgComb/tree/master/kl-polynomial_coefficients). |
|
|
|
|
|
## Task |
|
|
|
|
|
**Math question:** Generate conjectures around the properties of coefficients appearing on KL polynomials. |
|
|
|
|
|
**Narrow ML task:** Predict the coefficients of \\(P_{x,w}(q)\\) given \\(x\\) and \\(w\\). |
|
|
We break this up into a separate task for each coefficient though one could |
|
|
choose to predict all simultaneously. Since there are generally very few |
|
|
different integers that arise as coefficients (at least in these small examples), |
|
|
we frame this problem as one of classification. |
|
|
|
|
|
While the classification task as framed does not capture the broader math question exactly, |
|
|
illuminating connections between \\(x\\), \\(w\\), and the coefficients of \\(P_{x,w}(q)\\) |
|
|
has the potential to yield critical insights. |
|
|
|
|
|
## Small model performance |
|
|
|
|
|
Since there are many possible tasks here, we did not run exhaustive hyperparameter searches. |
|
|
Instead, we ran ReLU MLPs with depth 4, width 256, and learning rate 0.0005. |
|
|
|
|
|
### Kazhdan-Lusztig Polynomials for Permutations of \\(5\\) elements |
|
|
|
|
|
Accuracy predicting coefficients for permutations of 5 elements: |
|
|
|
|
|
| Coefficient | MLP | Transformer | Guessing largest class | |
|
|
|----------|----------|-----------|------------| |
|
|
| \\(1\\) | \\(99.8\% \pm 0.2\%\\) | \\(99.9\% \pm 0.1\%\\) | \\(73.7\%\\) | |
|
|
| \\(q\\) | \\(99.5\% \pm 0.4\%\\) | \\(99.2\% \pm 1.0\%\\) | \\(97.0\%\\) | |
|
|
| \\(q^2\\) | \\(99.9\% \pm 0.1\%\\) | \\(100.0\% \pm 0.0\%\\) | \\(99.9\%\\) | |
|
|
|
|
|
The associated macro F1-scores are: |
|
|
|
|
|
| Coefficient | MLP | Transformer | |
|
|
|----------|----------|-----------| |
|
|
| \\(1\\) | \\(99.7\% \pm 0.1\%\\) | \\(99.9\% \pm 0.4\%\\) | |
|
|
| \\(q\\) | \\(93.9\% \pm 3.7\%\\) | \\(92.7\% \pm 7.6\%\\) | |
|
|
| \\(q^2\\) | \\(50.0\% \pm 0.0\%\\) | \\(100.0\% \pm 0.0\%\\) | |
|
|
|
|
|
### Kazhdan-Lusztig Polynomials for Permutations of \\(6\\) elements |
|
|
|
|
|
Accuracy predicting coefficients for permutations of 6 elements: |
|
|
|
|
|
| Coefficient | MLP | Transformer | Guessing largest class | |
|
|
|----------|----------|-----------|------------| |
|
|
| \\(1\\) | \\(99.9\% \pm 0.0\%\\) | \\(100.0\% \pm 0.0\%\\) | \\(80.9\%\\)| |
|
|
| \\(q\\) | \\(99.9\% \pm 0.0\%\\) | \\(99.9\% \pm 0.0\%\\) | \\(95.8\%\\) | |
|
|
| \\(q^2\\) | \\(99.9\% \pm 0.0\%\\) | \\(99.9\% \pm 0.0\%\\) | \\(99.5\%\\) | |
|
|
| \\(q^3\\) | \\(99.9\% \pm 0.0\%\\) | \\(99.9\% \pm 0.0\%\\) | \\(99.9\%\\) | |
|
|
|
|
|
The associated macro F1-scores are: |
|
|
|
|
|
| Coefficient | MLP | Transformer | |
|
|
|----------|----------|-----------| |
|
|
| \\(1\\) | \\(99.9\% \pm 0.0\%\\) | \\(100.0\% \pm 0.0\%\\) | |
|
|
| \\(q\\) | \\(99.0\% \pm 1.5\%\\) | \\(98.0\% \pm 3.7\%\\) | |
|
|
| \\(q^2\\) | \\(97.4\% \pm 5.2\%\\) | \\(98.0\% \pm 3.7\%\\) | |
|
|
| \\(q^3\\) | \\(87.9\% \pm 4.5\%\\) | \\(88.3\% \pm 17.1\%\\) | |
|
|
|
|
|
The \\(\pm\\) signs indicate 95% confidence intervals from random weight initialization and training. |
|
|
|
|
|
## Further information |
|
|
|
|
|
- **Curated by:** Henry Kvinge |
|
|
- **Funded by:** Pacific Northwest National Laboratory |
|
|
- **Language(s) (NLP):** NA |
|
|
- **License:** CC-by-2.0 |
|
|
|
|
|
## Citation |
|
|
|
|
|
**BibTeX:** |
|
|
|
|
|
|
|
|
@article{chau2025machine, |
|
|
title={Machine learning meets algebraic combinatorics: A suite of datasets capturing research-level conjecturing ability in pure mathematics}, |
|
|
author={Chau, Herman and Jenne, Helen and Brown, Davis and He, Jesse and Raugas, Mark and Billey, Sara and Kvinge, Henry}, |
|
|
journal={arXiv preprint arXiv:2503.06366}, |
|
|
year={2025} |
|
|
} |
|
|
|
|
|
**APA:** |
|
|
|
|
|
Chau, H., Jenne, H., Brown, D., He, J., Raugas, M., Billey, S., & Kvinge, H. (2025). Machine learning meets algebraic combinatorics: A suite of datasets capturing research-level conjecturing ability in pure mathematics. arXiv preprint arXiv:2503.06366. |
|
|
|
|
|
## Dataset Card Contact |
|
|
|
|
|
Henry Kvinge, acdbenchdataset@gmail.com |
|
|
|
|
|
## References |
|
|
|
|
|
[1] Kazhdan, David, and George Lusztig. "Representations of Coxeter groups and Hecke algebras." Inventiones mathematicae 53.2 (1979): 165-184. |
|
|
[2] Warrington, Gregory S. "Equivalence classes for the μ-coefficient of Kazhdan–Lusztig polynomials in Sn." Experimental Mathematics 20.4 (2011): 457-466. |