On the optimality of some group testing algorithms

Matthew Aldridge

doi:10.1109/ISIT.2017.8007097

On the optimality of some group testing algorithms

Matthew Aldridge

Department of Mathematical Sciences

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

9 Citations (SciVal)

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Abstract

We consider Bernoulli nonadaptive group testing with k=Θ(n^θ) defectives, for θ∈(0,1). The practical definite defectives (DD) detection algorithm is known to be optimal for θ≥1/2. We give a new upper bound on the rate of DD, showing that DD is strictly suboptimal for θ<0.41. We also show that the SCOMP algorithm and algorithms based on linear programming achieve a rate at least as high as DD, so in particular are also optimal for θ≥1/2.

Original language	English
Title of host publication	Proceedings of 2017 IEEE International Symposium on Information Theory (ISIT)
Publisher	IEEE
Pages	3085-3089
ISBN (Electronic)	2157-8117
DOIs	https://doi.org/10.1109/ISIT.2017.8007097
Publication status	Published - Jun 2017
Event	2017 IEEE International Symposium on Information Theory (ISIT) - Aachen, Germany Duration: 25 Jun 2017 → 30 Jun 2017

Conference

Conference	2017 IEEE International Symposium on Information Theory (ISIT)
Period	25/06/17 → 30/06/17

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10.1109/ISIT.2017.8007097

optimality-group-testing (5)
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abstract = "We consider Bernoulli nonadaptive group testing with k=Θ(n^θ) defectives, for θ∈(0,1). The practical definite defectives (DD) detection algorithm is known to be optimal for θ≥1/2. We give a new upper bound on the rate of DD, showing that DD is strictly suboptimal for θ<0.41. We also show that the SCOMP algorithm and algorithms based on linear programming achieve a rate at least as high as DD, so in particular are also optimal for θ≥1/2.",

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AB - We consider Bernoulli nonadaptive group testing with k=Θ(n^θ) defectives, for θ∈(0,1). The practical definite defectives (DD) detection algorithm is known to be optimal for θ≥1/2. We give a new upper bound on the rate of DD, showing that DD is strictly suboptimal for θ<0.41. We also show that the SCOMP algorithm and algorithms based on linear programming achieve a rate at least as high as DD, so in particular are also optimal for θ≥1/2.

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On the optimality of some group testing algorithms

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