2013/07/10 by Sheng Cai, Cai, Sheng, Mohammad Jahangoshahi +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #Advanced biosensing and bioanalysis techniques #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning and Algorithms #SARS-CoV-2 detection and testing
paper · pdf · doi:10.48550/arxiv.1307.2811
openalex publication_date 2013/07/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Group-testing refers to the problem of identifying (with high probability) a (small) subset of D defectives from a (large) set of N items via a "small" number of "pooled" tests. For ease of presentation in this work we focus on the regime when D = \cON1-\gap for some \gap > 0. The tests may be noiseless or noisy, and the testing procedure may be adaptive (the pool defining a test may depend on the outcome of a previous test), or non-adaptive (each test is performed independent of the outcome of other tests). A rich body of literature demonstrates that Θ(Dlog(N)) tests are information-theoretically necessary and sufficient for the group-testing problem, and provides algorithms that achieve this performance. However, it is only recently that reconstruction algorithms with computational complexity that is sub-linear in N have started being investigated (recent work by \citeGurI:04,IndN:10, NgoP:11 gave some of the first such algorithms). In the scenario with adaptive tests with noisy outcomes, we present the first scheme that is simultaneously order-optimal (up to small constant factors) in both the number of tests and the decoding complexity (\cODlog(N) in both the performance metrics). The total number of stages of our adaptive algorithm is "small" (\cOlog(D)). Similarly, in the scenario with non-adaptive tests with noisy outcomes, we present the first scheme that is simultaneously near-optimal in both the number of tests and the decoding complexity (via an algorithm that requires \cODlog(D)log(N) tests and has a decoding complexity of \cal O(D(log N+log2D)). Finally, we present an adaptive algorithm that only requires 2 stages, and for which both the number of tests and the decoding complexity scale as \cal O(D(log N+log2D)). For all three settings the probability of error of our algorithms scales as \cO1/(poly(D).