Construction of Almost Disjunct Matrices for Group Testing
Arya Mazumdar

TL;DR
This paper introduces a new approach to constructing almost disjunct matrices for group testing by analyzing average code distance, resulting in more efficient matrices that identify most defect sets.
Contribution
It connects average code distance to almost disjunct matrices and provides explicit constructions with fewer tests than previous methods.
Findings
Constructed matrices with fewer rows than prior work.
Achieved high identification rate of defective sets.
Flexible parameters for different group testing scenarios.
Abstract
In a \emph{group testing} scheme, a set of tests is designed to identify a small number of defective items among a large set (of size ) of items. In the non-adaptive scenario the set of tests has to be designed in one-shot. In this setting, designing a testing scheme is equivalent to the construction of a \emph{disjunct matrix}, an matrix where the union of supports of any columns does not contain the support of any other column. In principle, one wants to have such a matrix with minimum possible number of rows (tests). One of the main ways of constructing disjunct matrices relies on \emph{constant weight error-correcting codes} and their \emph{minimum distance}. In this paper, we consider a relaxed definition of a disjunct matrix known as \emph{almost disjunct matrix}. This concept is also studied under the name of \emph{weakly separated design} in the…
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