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Approximating the distance to monotonicity of Boolean functions

Authors: Ramesh Krishnan S. Pallavoor; Sofya Raskhodnikova; Erik Waingarten;

Approximating the distance to monotonicity of Boolean functions

Abstract

AbstractWe design a nonadaptive algorithm that, given oracle access to a function which is ‐far from monotone, makes poly queries and returns an estimate that, with high probability, is an ‐approximation to the distance of to monotonicity. The analysis of our algorithm relies on an improvement to the directed isoperimetric inequality of Khot, Minzer, and Safra (SIAM J. Comput., 2018). Furthermore, we rule out a poly‐query nonadaptive algorithm that approximates the distance to monotonicity significantly better by showing that, for all constant every nonadaptive ‐approximation algorithm for this problem requires queries. This answers a question of Seshadhri (Property Testing Review, 2014) for the case of nonadaptive algorithms. We obtain our lower bound by proving an analogous bound for erasure‐resilient (and tolerant) testers. Our method also yields the same lower bounds for unateness and being a ‐junta.

Keywords

FOS: Computer and information sciences, sublinear algorithms, Discrete Mathematics (cs.DM), Randomized algorithms, property testing, tolerant and erasure-resilient testing, Computational Complexity (cs.CC), Computer Science - Computational Complexity, Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS), analysis of Boolean functions, Boolean functions, Computer Science - Discrete Mathematics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Top 10%
Top 10%
Top 10%
Green
bronze