# Testing local properties of arrays

arXiv: https://arxiv.org/abs/1811.07448 · HTML: https://mlsanity.com/paper/1811.07448/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [Learning and Testing Convex Functions](https://mlsanity.com/paper/2511.11498/) (2025)

## Abstract (arXiv)

We study testing of local properties in one-dimensional and multi-dimensional arrays. A property of d-dimensional arrays f:[n]d → Sigma is k-local if it can be defined by a family of k × ldots × k forbidden consecutive patterns. This definition captures numerous interesting properties. For example, monotonicity, Lipschitz continuity and submodularity are 2-local; convexity is (usually) 3-local; and many typical problems in computational biology and computer vision involve o(n)-local properties. In this work, we present a generic approach to test all local properties of arrays over any finite (and not necessarily bounded size) alphabet. We show that any k-local property of d-dimensional arrays is testable by a simple canonical one-sided error non-adaptive varepsilon-test, whose query complexity is O(ε-1k logfracε nk) for d = 1 and O(cd ε-1/d k · nd-1) for d > 1. The queries made by the canonical test constitute sphere-like structures of varying sizes, and are completely independent of the property and the alphabet Sigma. The query complexity is optimal for a wide range of parameters: For d=1, this matches the query complexity of many previously investigated local properties, while for d > 1 we design and analyze new constructions of k-local properties whose one-sided non-adaptive query complexity matches our upper bounds. For some previously studied properties, our method provides the first known sublinear upper bound on the query complexity.