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license: cc-by-2.0
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---
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license: cc-by-2.0
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pretty_name: the mHeight of permutations of size 9
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---
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# The mHeight Function of a Permutation of Size 9
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Truly challenging open problems in mathematics often require the development
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of new mathematical constructions (or even entire new areas of mathematics).
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This dataset represents a modest example of this. The mHeight function is
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a statistic associated with a permutation that relates to all \\(3412\\)-patterns
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in the permutation. It was developed and plays a crucial role in the proof by
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Gaetz and Gao [1] which resolved a long-standing conjecture of Billey and Postnikov
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[2] about the coefficients on Kazhdan-Lusztig polynomials
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(see our [Kazhdan-Lusztig polynomial dataset](https://github.com/pnnl/ML4AlgComb/tree/master/kl-polynomial_coefficients))
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which carry important geometric information about certain spaces,
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Schubert varieties, that are of interest both to mathematicians and physicists.
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The task of predicting the mHeight function represents an interesting opportunity
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to understand whether a non-trivial intermediate step in an important proof can
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be learned by machine learning.
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## \\((3412)\\) patterns and the mHeight of a permutation
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A \\(3412\\) *pattern* in a permutation \\(\sigma = a_1 \ldots a_n \in S_n\\) is a
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quadruple \\((a_i,a_j,a_k,a_\ell)\\) such that \\(i < j < k < \ell\\) but
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\\(a_k < a_\ell < a_i < a_j\\). Patterns have deep connections to algebra
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and geometry [3]. Suppose \\(\sigma\\) contains at least
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one occurrence of a \\(3412\\) pattern, \\((a_i,a_j,a_k,a_\ell)\\).
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The *height* of \\((a_i,a_j,a_k,a_\ell)\\) is \\(a_i - a_\ell\\). The *mHeight* of
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\\(\sigma\\) is then the minimum height over all \\(3412\\) patterns in \\(\sigma\\).
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If \\(\sigma\\) contains no \\(3412\\) permutations then the mHeight is set to 0.
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## Dataset
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This dataset contains permutations of \\(9\\) elements labeled by their mHeight. Permutations are written in
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1-line notation.
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For \\(n = 9\\), mHeight takes values 0, 1, 2, 3, 4, 5, so we frame this as a classification task.
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| mHeight value | 0 | 1 | 2 | 3 | 4 | 5 | Total number of instances |
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|----------|----------|----------|----------|----------|----------|----------|----------|
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| Train | 49,092 | 3,161 | 524 | 77 | 9 | 1 | 52,864 |
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| Test | 12,317 | 759 | 118 | 19 | 3 | 0 | 13,216 |
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## Data Generation
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The datasets generation scripts can be found at [here](https://github.com/pnnl/ML4AlgComb/tree/master/mheight_function).
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## Task
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**ML task:** Re-discover the notation of mHeight from a performant model.
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## Small model performance
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We provide some basic baselines for this task. Benchmarking details can be found in the associated paper.
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| Size | Logistic regression | MLP | Transformer | Guessing 0 |
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|----------|----------|-----------|------------|------------|
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| \\(n= 9\\) | \\(93.2\%\\) | \\(99.8\% \pm 0.6\%\\) | \\(99.9\% \pm 0.4\%\\) | \\(93.2\%\\) |
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The \\(\pm\\) signs indicate 95% confidence intervals from random weight initialization and training.
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## References
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[1] Gaetz, Christian, and Yibo Gao. "On the minimal power of \\(q\\) in a Kazhdan-Lusztig polynomial." arXiv preprint arXiv:2303.13695 (2023).
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[2] Billey, Sara, and Alexander Postnikov. "Smoothness of Schubert varieties via patterns in root subsystems." Advances in Applied Mathematics 34.3 (2005): 447-466.
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[3] Billey, Sara C. "Pattern avoidance and rational smoothness of Schubert varieties." Advances in Mathematics 139.1 (1998): 141-156.
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