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Flipping out with Many Flips: Hardness of Testing k-Monotonicity

Authors Elena Grigorescu, Akash Kumar, Karl Wimmer

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Author Details

Elena Grigorescu
  • Purdue University, West Lafayette, IN, USA, https://www.cs.purdue.edu/homes/egrigore/
Akash Kumar
  • Purdue University, West Lafayette, IN, USA, https://www.cs.purdue.edu/homes/akumar
Karl Wimmer
  • Duquesne University, Pittsburgh, PA, USA, http://www.mathcs.duq.edu/~wimmer/

Funding

  • Grigorescu, Elena: Supported by NSF CCF-1649515
  • Kumar, Akash: Supported by NSF CCF-1649515 and NSF CCF-1618918

Cite AsGet BibTex

Elena Grigorescu, Akash Kumar, and Karl Wimmer. Flipping out with Many Flips: Hardness of Testing k-Monotonicity. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 116, pp. 40:1-40:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.APPROX-RANDOM.2018.40

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.

Subject Classification

ACM Subject Classification
  • Theory of computation → Sketching and sampling
  • Theory of computation → Lower bounds and information complexity
Keywords
  • Property Testing
  • Boolean Functions
  • k-Monotonicity
  • Lower Bounds

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