Parameterized Convexity Testing

Abstract

In this work, we develop new insights into the fundamental problem of convexity testing of real-valued functions over the domain $[n]$. Specifically, we present a nonadaptive algorithm that, given inputs $\eps \in (0,1), s \in \mathbb{N}$, and oracle access to a function, $\eps$-tests convexity in $O(\log (s)/\eps)$, where $s$ is an upper bound on the number of distinct discrete derivatives of the function. We also show that this bound is tight. Since $s\le n$, our query complexity bound is at least as good as that of the optimal convexity tester (Ben Eliezer; ITCS 2019) with complexity $O(\frac{\log \eps n}{\eps})$; our bound is strictly better when $s=o(n)$. The main contribution of our work is to appropriately parameterize the complexity of convexity testing to circumvent the worst-case lower bound (Belovs et al.; SODA 2020) of $Ω(\frac{\log (\eps n)}{\eps})$ expressed in terms of the input size and obtain a more efficient algorithm.

11 pages, accepted in SOSA 2022