A Diagonal Splitting Algorithm for Adaptive Group Testing

Chaorui Yao, Pavlos Nikolopoulos, Christina Fragouli

Abstract: Group testing enables to identify infected individuals in a population using a smaller number of tests than individual testing. To achieve this, group testing algorithms commonly assume knowledge of the number of infected individuals; nonadaptive and several adaptive algorithms fall in this category. Some adaptive algorithms, like binary splitting, operate without this assumption, but require a number of stages that may scale linearly with the size of the population. In this paper, we contribute a new algorithm that enables a balance between the number of tests and the number of stages used, and which we term diagonal splitting algorithm (DSA). Diagonal splitting, like binary splitting, does not require knowledge of the number of infected individuals, yet unlike binary splitting, is orderoptimal w.r.t. the expected number of tests it requires and is guaranteed to succeed in a small number of stages that scales at most logarithmically with the size of the population. Numerical evaluations, for diagonal splitting and a hybrid approach we propose, support our theoretical findings.

External IDs:dblp:conf/isit/YaoNF23