Almost optimal distribution-free sample-based testing of k-modality

Dana Ron, Asaf Rosin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

For an integer k ≥ 0, a sequence σ = σ1,..., σn over a fully ordered set is k-modal, if there exist indices 1 = a0 < a1 < · · · < ak+1 = n such that for each i, the subsequence σai,..., σai+1 is either monotonically non-decreasing or monotonically non-increasing. The property of k-modality is a natural extension of monotonicity, which has been studied extensively in the area of property testing. We study one-sided error property testing of k-modality in the distribution-free sample-based model. We prove an upper bound of1 O (√kn log k/ε) on the sample complexity, and an almost matching lower bound of Ω (√kn/ε). When the underlying distribution is uniform, we obtain a completely tight bound of Θ (√kn/ε), which generalizes what is known for sample-based testing of monotonicity under the uniform distribution.

Original languageEnglish
Title of host publicationApproximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, APPROX/RANDOM 2020
EditorsJaroslaw Byrka, Raghu Meka
ISBN (Electronic)9783959771641
DOIs
StatePublished - 1 Aug 2020
Event23rd International Conference on Approximation Algorithms for Combinatorial Optimization Problems and 24th International Conference on Randomization and Computation, APPROX/RANDOM 2020 - Virtual, Online, United States
Duration: 17 Aug 202019 Aug 2020

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume176

Conference

Conference23rd International Conference on Approximation Algorithms for Combinatorial Optimization Problems and 24th International Conference on Randomization and Computation, APPROX/RANDOM 2020
Country/TerritoryUnited States
CityVirtual, Online
Period17/08/2019/08/20

Keywords

  • Distribution-free property testing
  • K-modality
  • Sample-based property testing

All Science Journal Classification (ASJC) codes

  • Software

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