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Improved Algorithm for Permutation Testing

Xiaojin Zhang

TL;DR

This work addresses efficient one-sided adaptive testing for the non-monotone forbidden pattern (1,3,2) in sequences. It introduces a monotone structure derived from the pattern’s third element and couples it with a fast binary-search-based strategy, aided by a simple $(1,2)$-pattern tester. The main result is an adaptive tester that distinguishes ε-far inputs from (1,3,2)-free ones with $O(ε^{-2}\log^4 n)$ queries, a substantial improvement over the previous $O(ε^{-7}\log^{26} n)$ bound. The approach sharpens the understanding of structure in forbidden-pattern testing and suggests pathways to generalize to broader pattern families with similar monotone-structure decompositions.

Abstract

For a permutation $π: [K]\rightarrow [K]$, a sequence $f: \{1,2,\cdots, n\}\rightarrow \mathbb R$ contains a $π$-pattern of size $K$, if there is a sequence of indices $(i_1, i_2, \cdots, i_K)$ ($i_1<i_2<\cdots<i_K$), satisfying that $f(i_a)<f(i_b)$ if $π(a)<π(b)$, for $a,b\in [K]$. Otherwise, $f$ is referred to as $π$-free. For the special case where $π= (1,2,\cdots, K)$, it is referred to as the monotone pattern. \cite{newman2017testing} initiated the study of testing $π$-freeness with one-sided error. They focused on two specific problems, testing the monotone permutations and the $(1,3,2)$ permutation. For the problem of testing monotone permutation $(1,2,\cdots,K)$, \cite{ben2019finding} improved the $(\log n)^{O(K^2)}$ non-adaptive query complexity of \cite{newman2017testing} to $O((\log n)^{\lfloor \log_{2} K\rfloor})$. Further, \cite{ben2019optimal} proposed an adaptive algorithm with $O(\log n)$ query complexity. However, no progress has yet been made on the problem of testing $(1,3,2)$-freeness. In this work, we present an adaptive algorithm for testing $(1,3,2)$-freeness. The query complexity of our algorithm is $O(ε^{-2}\log^4 n)$, which significantly improves over the $O(ε^{-7}\log^{26}n)$-query adaptive algorithm of \cite{newman2017testing}. This improvement is mainly achieved by the proposal of a new structure embedded in the patterns.

Improved Algorithm for Permutation Testing

TL;DR

This work addresses efficient one-sided adaptive testing for the non-monotone forbidden pattern (1,3,2) in sequences. It introduces a monotone structure derived from the pattern’s third element and couples it with a fast binary-search-based strategy, aided by a simple -pattern tester. The main result is an adaptive tester that distinguishes ε-far inputs from (1,3,2)-free ones with queries, a substantial improvement over the previous bound. The approach sharpens the understanding of structure in forbidden-pattern testing and suggests pathways to generalize to broader pattern families with similar monotone-structure decompositions.

Abstract

For a permutation , a sequence contains a -pattern of size , if there is a sequence of indices (), satisfying that if , for . Otherwise, is referred to as -free. For the special case where , it is referred to as the monotone pattern. \cite{newman2017testing} initiated the study of testing -freeness with one-sided error. They focused on two specific problems, testing the monotone permutations and the permutation. For the problem of testing monotone permutation , \cite{ben2019finding} improved the non-adaptive query complexity of \cite{newman2017testing} to . Further, \cite{ben2019optimal} proposed an adaptive algorithm with query complexity. However, no progress has yet been made on the problem of testing -freeness. In this work, we present an adaptive algorithm for testing -freeness. The query complexity of our algorithm is , which significantly improves over the -query adaptive algorithm of \cite{newman2017testing}. This improvement is mainly achieved by the proposal of a new structure embedded in the patterns.

Paper Structure

This paper contains 32 sections, 24 theorems, 13 equations, 1 table, 7 algorithms.

Key Result

Theorem 1

For any $\epsilon>0$, there exists an adaptive algorithm that, given query access to a function $f:[n]\rightarrow \mathbb R$ which is $\epsilon$-far from $(1,3,2)$-free, outputs a $(1,3,2)$ subsequence of $f$ with probability at least $9/10$. The query complexity of this algorithm is $O(\epsilon^{-2

Theorems & Definitions (37)

  • Theorem 1
  • Definition 1: Growing Suffix
  • Definition 2: Splittable Interval
  • Lemma 1: ben2019optimal
  • Lemma 2: newman2017testing
  • Lemma 3
  • Theorem 2
  • Lemma 4: Proposition 2.2 of newman2017testing
  • Lemma 5
  • Lemma 6
  • ...and 27 more