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Estimating the distance to a monotone function

Authors: Nir Ailon; Bernard Chazelle; Seshadhri Comandur; Ding Liu;

Estimating the distance to a monotone function

Abstract

AbstractIn standard property testing, the task is to distinguish between objects that have a property 𝒫 and those that are ε‐far from 𝒫, for some ε > 0. In this setting, it is perfectly acceptable for the tester to provide a negative answer for every input object that does not satisfy 𝒫. This implies that property testing in and of itself cannot be expected to yield any information whatsoever about the distance from the object to the property. We address this problem in this paper, restricting our attention to monotonicity testing. A function f : {1,…,n} ↦ R is at distance εf from being monotone if it can (and must) be modified at εfn places to become monotone. For any fixed δ > 0, we compute, with probability at least 2/3, an interval [(1/2 − δ)ε,ε] that encloses εf. The running time of our algorithm is O(εf−1 log log εf− 1 log n), which is optimal within a factor of loglog εf−1 and represents a substantial improvement over previous work. We give a second algorithm with an expected running time of O(εf−1 log nlog log log n). Finally, we extend our results to multivariate functions. © 2007 Wiley Periodicals, Inc. Random Struct. Alg., 2007

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Keywords

sublinear algorithms, Combinatorial probability, monotone functions, distance estimation, Analysis of algorithms and problem complexity, Randomized algorithms, tolerant property testing, property testing

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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!
41
Top 10%
Top 10%
Top 10%
bronze