Nearly optimal sparse group testing

Venkata Gandikota, Elena Grigorescu, Sidharth Jaggi, Samson Zhou

Research output: Chapter in Book/Entry/PoemConference contribution

7 Scopus citations

Abstract

Group testing is the process of pooling arbitrary subsets from a set of n items so as to identify, with a minimal number of disjunctive tests, a 'small' subset of d defective items. In 'classical' non-adaptive group testing, it is known that when d = o(n1-δ) for any δ > 0, θ(d log(n)) tests are both information-theoretically necessary, and sufficient to guarantee recovery with high probability. Group testing schemes in the literature meeting this bound require most items to be tested Ω(log(n)) times, and most tests to incorporate Ω(n/d) items. Motivated by physical considerations, we study group testing models in which the testing procedure is constrained to be 'sparse'. Specifically, we consider (separately) scenarios in which (a) items are finitely divisible and hence may participate in at most γ tests; and (b) tests are size-constrained to pool no more than ρ items per test. For both scenarios we provide information-theoretic lower bounds on the number of tests required to guarantee high probability recovery. In particular, one of our main results shows that γ-finite divisibility of items forces any group testing algorithm with probability of recovery error at most ϵ to perform at least Ω(γ(n/d)(1-2ϵ)/((1+2ϵ)γ)) tests. Analogously, for ρ-sized constrained tests, we show an information-theoretic lower bound of Ω(n log(n/d)/(ρ log(n/ρd))). In both scenarios we provide both randomized constructions (under both ϵ-error and zero-error reconstruction guarantees) and explicit constructions of computationally efficient group-testing algorithms (under ϵ-error reconstruction guarantees) that require a number of tests that are optimal up to constant factors in some regimes of n, d, γ and ρ. We also investigate the effect of unreliability/noise in test outcomes.

Original languageEnglish (US)
Title of host publication54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages401-408
Number of pages8
ISBN (Electronic)9781509045495
DOIs
StatePublished - Feb 10 2017
Externally publishedYes
Event54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016 - Monticello, United States
Duration: Sep 27 2016Sep 30 2016

Publication series

Name54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016

Other

Other54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016
Country/TerritoryUnited States
CityMonticello
Period9/27/169/30/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Hardware and Architecture
  • Control and Optimization

Fingerprint

Dive into the research topics of 'Nearly optimal sparse group testing'. Together they form a unique fingerprint.

Cite this