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arXiv · 2010.02518

Strongly separable matrices for nonadaptive combinatorial group testing

Jinping Fan·Hung-Lin Fu·Yujie Gu·Ying Miao·Maiko Shigeno
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Abstract

In nonadaptive combinatorial group testing (CGT), it is desirable to identify a small set of up to $d$ defectives from a large population of $n$ items with as few tests (i.e. large rate) and efficient identifying algorithm as possible. In the literature, $d$-disjunct matrices ($d$-DM) and $\bar{d}$-separable matrices ($\bar{d}$-SM) are two classical combinatorial structures having been studied for several decades. It is well-known that a $d$-DM provides a more efficient identifying algorithm than a $\bar{d}$-SM, while a $\bar{d}$-SM could have a larger rate than a $d$-DM. In order to combine the advantages of these two structures, in this paper, we introduce a new notion of \emph{strongly $d$-separable matrix} ($d$-SSM) for nonadaptive CGT and show that a $d$-SSM has the same identifying ability as a $d$-DM, but much weaker requirements than a $d$-DM. Accordingly, the general bounds on the largest rate of a $d$-SSM are established. Moreover, by the random coding method with expurgation, we derive an improved lower bound on the largest rate of a $2$-SSM which is much higher than the best known result of a $2$-DM.

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Jinping Fan, Hung-Lin Fu, Yujie Gu, Ying Miao, Maiko Shigeno. 2020-10-06. Strongly separable matrices for nonadaptive combinatorial group testing. https://arxiv.org/abs/2010.02518

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