An Efficient Algorithm for Capacity-Approaching Noisy Adaptive Group Testing

Abstract

In this paper, we consider the group testing problem with adaptive test designs and noisy outcomes. We propose a computationally efficient four-stage procedure with components including random binning, identification of bins containing defective items, 1-sparse recovery via channel codes, and a "clean-up" step to correct any errors from the earlier stages. We prove that the asymptotic required number of tests comes very close to the best known information-theoretic achievability bound (which is based on computationally intractable decoding), and approaches a capacity-based converse bound in the low-sparsity regime.