Sublinear decoding schemes for non-adaptive group testing with inhibitors

Thach V. Bui, Minoru Kuribayashi, Tetsuya Kojima, Isao Echizen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Identification of up to d defective items and up to h inhibitors in a set of n items is the main task of non-adaptive group testing with inhibitors. To reduce the cost of this Herculean task, a subset of the n items is formed and then tested. This is called group testing. A test outcome on a subset of items is positive if the subset contains at least one defective item and no inhibitors, and negative otherwise. We present two decoding schemes for efficiently identifying the defective items and the inhibitors in the presence of e erroneous outcomes in time poly(d, h, e, log2n>, which is sublinear to the number of items. This decoding complexity significantly improves the state-of-the-art schemes in which the decoding time is linear to the number of items, i.e., poly(d,h,e,n). Moreover, each column of the measurement matrices associated with the proposed schemes can be nonrandomly generated in polynomial order of the number of rows. As a result, one can save space for storing them. Simulation results confirm our theoretical analysis. When the number of items is sufficiently large, the decoding time in our proposed scheme is smallest in comparison with existing work. In addition, when some erroneous outcomes are allowed, the number of tests in the proposed scheme is often smaller than the number of tests in existing work.

Original languageEnglish
Title of host publicationTheory and Applications of Models of Computation - 15th Annual Conference, TAMC 2019, Proceedings
EditorsT. V. Gopal, Junzo Watada
PublisherSpringer Verlag
Number of pages21
ISBN (Print)9783030148119
Publication statusPublished - 2019
Event15th Annual Conference on Theory and Applications of Models of Computation, TAMC 2019 - Kitakyushu, Japan
Duration: Apr 13 2019Apr 16 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11436 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th Annual Conference on Theory and Applications of Models of Computation, TAMC 2019


  • Non-adaptive group testing
  • Sparse recovery
  • Sublinear algorithm

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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