# Flipping out with many flips: hardness of testing k-monotonicity

## TL;DR

This paper investigates the complexity of testing k-monotonicity in functions, revealing exponential query requirements for certain cases and providing efficient testing algorithms for others, thus advancing understanding of property testing in high-dimensional domains.

## Contribution

It establishes exponential lower bounds for testing 2-monotonicity on the hypercube and introduces constant-query algorithms for testing k-monotonicity on [n]^d domains.

## Key findings

- Testing 2-monotonicity on the hypercube requires exponential queries.
- Distinguishing k-monotone functions from far functions can be done with constant queries on [n]^d.
- Exponential lower bounds contrast with efficient algorithms for related property testing problems.

## Abstract

A function f : {0, 1}^n -> {0, 1} is said to be k-monotone if it flips between 0 and 1 at most k times on every ascending chain. Such functions represent a natural generalization of (1-)monotone functions, and have been recently studied in circuit complexity, PAC learning, and cryptography. Our work is part of a renewed focus in understanding testability of properties characterized by freeness of arbitrary order patterns as a generalization of monotonicity. Recently, Canonne et al. (ITCS 2017) initiate the study of k-monotone functions in the area of property testing, and Newman et al. (SODA 2017) study testability of families characterized by freeness from order patterns on real-valued functions over the line [n] domain. We study k-monotone functions in the more relaxed parametrized property testing model, introduced by Parnas et al. (JCSS, 72(6), 2006). In this process we resolve a problem left open in previous work. Specifically, our results include the following. 1. Testing 2-monotonicity on the hypercube non-adaptively with one-sided error requires an exponential in \sqrt n number of queries. This behavior shows a stark contrast with testing (1-)monotonicity, which only needs O(\sqrt n) queries (Khot et al. (FOCS 2015)). Furthermore, even the apparently easier task of distinguishing 2-monotone functions from functions that are far from being n^.01 -monotone also requires an exponential number of queries. 2. On the hypercube [n]^d domain, there exists a testing algorithm that makes a constant number of queries and distinguishes functions that are k-monotone from functions that are far from being O(kd^2)-monotone. Such a dependency is likely necessary, given the lower bound above for the hypercube.