Information-theoretic and algorithmic aspects of parallel and distributed reconstruction from pooled data

• Published in: Journal of parallel and distributed computing Vol. 180; p. 104718
• Main Authors: Gebhard, Oliver, Hahn-Klimroth, Max, Kaaser, Dominik, Loick, Philipp
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.10.2023
• Subjects: Noisy pooled data; Non-adaptive algorithm; Pooled data; Quantitative group testing; Quantitative group testing; Pooled data; Non-adaptive algorithm; Noisy pooled data
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2023.104718

In the pooled data problem the goal is to efficiently reconstruct a binary signal from additive measurements. Given a signal σ∈{0,1}n, we can query multiple entries at once and get the total number of non-zero entries in the query as a result. We assume that queries are time-consuming and therefore focus on the setting where all queries are executed in parallel. First, we propose and analyze a simple and efficient greedy reconstruction algorithm. Secondly, we derive a sharp information-theoretic threshold for the minimum number of queries required to reconstruct σ with high probability. Finally, we consider two noise models: In the noisy channel model, the result for each entry of the signal flips with a certain probability. In the noisy query model, each query result is subject to random Gaussian noise. We pin down the range of error probabilities and distributions for which our algorithm reconstructs the exact initial states with high probability. Our theoretical findings are complemented by simulations where we compare our simple algorithm with approximate message passing (AMP) that is conjectured to be optimal in a number of related problems. •We investigate the non-adaptive pooled data problem (sometimes called quantitative group testing).•We prove the information-theoretic phase-transition point in the number of queries required to reconstruct the ground-truth.•We propose an easy and fast greedy-like algorithm and analyze its performance under noisy queries.•Simulations back-up the asymptotic findings for small sample sizes.