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Counting Permutation Patterns with Multidimensional Trees

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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.

fields

cs.LG 1

years

2025 1

verdicts

CONDITIONAL 1

representative citing papers

Global Permutation Entropy

cs.LG · 2025-08-27 · conditional · novelty 6.0

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 entropy in synthetic tests.

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  • Global Permutation Entropy cs.LG · 2025-08-27 · conditional · none · ref 197 · internal anchor

    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 entropy in synthetic tests.