Subquadratic non-adaptive threshold group testing

Gianluca De Marco, Tomasz Jurdziński, Dariusz R. Kowalski, Michał Różański, Grzegorz Stachowiak

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


We consider threshold group testing – a generalization of group testing, which asks to identify a set of positive individuals in a population, by performing tests on pools of elements. Each test is represented by a subset Q of individuals and its output is yes if Q contains at least one positive element and no otherwise. Threshold group testing is the natural generalization, introduced by P. Damaschke in 2005, arising when we are given a threshold t>0 and the answer to a test Q is yes if Q contains at least t positives and no otherwise. We give upper and lower bounds for this general problem, showing a complexity separation with the classical group testing. Next, we introduce a further generalization in which the goal is minimizing not only the number of tests, but also the number of thresholds which is related to the accuracy of the tests.

Original languageEnglish (US)
Pages (from-to)42-56
Number of pages15
JournalJournal of Computer and System Sciences
StatePublished - Aug 2020


  • Deterministic algorithms
  • Group testing
  • Non-adaptive strategies
  • Probabilistic method
  • Threshold group testing

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Networks and Communications
  • Computational Theory and Mathematics
  • Applied Mathematics


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