Improved Constructions for Non-adaptive Threshold Group Testing

Abstract: The basic goal in combinatorial group testing is to identify a set of up to $d$ defective items within a large population of size $n \gg d$ using a pooling strategy. Namely, the items can be grouped together in pools, and a single measurement would reveal whether there are one or more defectives in the pool. The threshold model is a generalization of this idea where a measurement returns positive if the number of defectives in the pool reaches a fixed threshold $u > 0$, negative if this number is no more than a fixed lower threshold $\ell < u$, and may behave arbitrarily otherwise. We study non-adaptive threshold group testing (in a possibly noisy setting) and show that, for this problem, $O(d^{g+2} (\log d) \log(n/d))$ measurements (where $g := u-\ell-1$ and $u$ is any fixed constant) suffice to identify the defectives, and also present almost matching lower bounds. This significantly improves the previously known (non-constructive) upper bound $O(d^{u+1} \log(n/d))$. Moreover, we obtain a framework for explicit construction of measurement schemes using lossless condensers. The number of measurements resulting from this scheme is ideally bounded by $O(d^{g+3} (\log d) \log n)$. Using state-of-the-art constructions of lossless condensers, however, we obtain explicit testing schemes with $O(d^{g+3} (\log d) qpoly(\log n))$ and $O(d^{g+3+\beta} poly(\log n))$ measurements, for arbitrary constant $\beta > 0$.