Improved Lower Bound for Estimating the Number of Defective Items

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


Let X be a set of items of size n that contains some defective items, denoted by I, where I⊆ X. In group testing, a test refers to a subset of items Q⊂ X. The outcome of a test is 1 if Q contains at least one defective item, i.e., Q∩ I≠ ∅, and 0 otherwise. We give a novel approach to obtaining lower bounds in non-adaptive randomized group testing. The technique produced lower bounds that are within a factor of 1 / log log ⋯ klog n of the existing upper bounds for any constant k. Employing this new method, we can prove the following result. For any fixed constants k, any non-adaptive randomized algorithm that, for any set of defective items I, with probability at least 2/3, returns an estimate of the number of defective items |I| to within a constant factor requires at least$$\varOmega \left(\frac{\log n}{\log \log {\mathop {\cdots }\limits ^{k}}\log n}\right) $$Ω(lognloglog⋯klogn) tests. Our result almost matches the upper bound of O(log n) and solves the open problem posed by Damaschke and Sheikh Muhammad in [8, 9]. Additionally, it improves upon the lower bound of Ω(log n/ log log n) previously established by Ron and Tsur [21] and independently by Bshouty [2].

Original languageEnglish
Title of host publicationCombinatorial Optimization and Applications - 16th International Conference, COCOA 2023, Proceedings
EditorsWeili Wu, Jianxiong Guo
Number of pages13
StatePublished - 2024
Event16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023 - Hawai, United States
Duration: 15 Dec 202317 Dec 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14461 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023
Country/TerritoryUnited States


  • Estimation
  • Group Testing
  • Randomized Algorithm

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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