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Counting Permutation Patterns with Multidimensional Trees

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arxiv 2407.04971 v1 pith:IYLQGAV5 submitted 2024-07-06 cs.DS math.CO

classification cs.DSmath.CO
keywords algorithmmathbbcountingfamilytreesevaluationeveryinteger
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

We consider the well-studied pattern counting problem: given a permutation $\pi \in \mathbb{S}_n$ and an integer $k > 1$, count the number of order-isomorphic occurrences of every pattern $\tau \in \mathbb{S}_k$ in $\pi$. Our first result is an $\widetilde{\mathcal{O}}(n^2)$-time algorithm for $k=6$ and $k=7$. The proof relies heavily on a new family of graphs that we introduce, called pattern-trees. Every such tree corresponds to an integer linear combination of permutations in $\mathbb{S}_k$, and is associated with linear extensions of partially ordered sets. We design an evaluation algorithm for these combinations, and apply it to a family of linearly-independent trees. For $k=8$, we show a barrier: the subspace spanned by trees in the previous family has dimension exactly $|\mathbb{S}_8| - 1$, one less than required. Our second result is an $\widetilde{\mathcal{O}}(n^{7/4})$-time algorithm for $k=5$. This algorithm extends the framework of pattern-trees by speeding-up their evaluation in certain cases. A key component of the proof is the introduction of pair-rectangle-trees, a data structure for dominance counting.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Global Permutation Entropy

    cs.LG 2025-08 conditional novelty 6.0 of 10

    Global Permutation Entropy is a new, efficiently computable entropy index over all ordinal patterns of a time series and is claimed to converge faster and detect noise changes more robustly than standard permutation e...

  2. Tensor-to-Tensor Models with Fast Iterated Sum Features

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A corner-tree algorithm computes a large class of two-parameter iterated sums in linear time, enabling a cheap tensor-to-tensor neural layer that matches larger ResNets on CIFAR and works for texture anomaly detection.

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