Ilan Newman ; Nithin Varma - Strongly Sublinear Algorithms for Testing Pattern Freeness

theoretics:10131 - TheoretiCS, January 12, 2024, Volume 3 - https://doi.org/10.46298/theoretics.24.1
Strongly Sublinear Algorithms for Testing Pattern FreenessArticle

Authors: Ilan Newman ; Nithin Varma

    For a permutation $\pi:[k] \to [k]$, a function $f:[n] \to \mathbb{R}$ contains a $\pi$-appearance if there exists $1 \leq i_1 < i_2 < \dots < i_k \leq n$ such that for all $s,t \in [k]$, $f(i_s) < f(i_t)$ if and only if $\pi(s) < \pi(t)$. The function is $\pi$-free if it has no $\pi$-appearances. In this paper, we investigate the problem of testing whether an input function $f$ is $\pi$-free or whether $f$ differs on at least $\varepsilon n$ values from every $\pi$-free function. This is a generalization of the well-studied monotonicity testing and was first studied by Newman, Rabinovich, Rajendraprasad and Sohler (Random Structures and Algorithms 2019). We show that for all constants $k \in \mathbb{N}$, $\varepsilon \in (0,1)$, and permutation $\pi:[k] \to [k]$, there is a one-sided error $\varepsilon$-testing algorithm for $\pi$-freeness of functions $f:[n] \to \mathbb{R}$ that makes $\tilde{O}(n^{o(1)})$ queries. We improve significantly upon the previous best upper bound $O(n^{1 - 1/(k-1)})$ by Ben-Eliezer and Canonne (SODA 2018). Our algorithm is adaptive, while the earlier best upper bound is known to be tight for nonadaptive algorithms.


    Volume: Volume 3
    Published on: January 12, 2024
    Accepted on: November 20, 2023
    Submitted on: October 10, 2022
    Keywords: Computer Science - Data Structures and Algorithms,68W20, 68W25,F.2

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