Improved Bounds and Algorithms for Sparsity-Constrained Group Testing

TL;DR

This paper advances group testing by establishing bounds and algorithms for sparsity-constrained settings, including adaptive and non-adaptive scenarios with divisibility and size constraints, improving efficiency and theoretical understanding.

Contribution

It provides the first information-theoretic converse for adaptive group testing under sparsity constraints and develops near-optimal algorithms for these settings.

Findings

Asymptotically matching bounds up to a factor of e for adaptive algorithms.

Optimal adaptive algorithm for size-constrained tests.

Reduced number of tests with DD decoding compared to COMP.

Abstract

In group testing, the goal is to identify a subset of defective items within a larger set of items based on tests whose outcomes indicate whether any defective item is present. This problem is relevant in areas such as medical testing, data science, communications, and many more. Motivated by physical considerations, we consider a sparsity-based constrained setting (Gandikota et al., 2019) in which the testing procedure is subject to one of the following two constraints: items are finitely divisible and thus may participate in at most $\gamma$ tests; or tests are size-constrained to pool no more than $\rho$ items per test. While information-theoretic limits and algorithms are known for the non-adaptive setting, relatively little is known in the adaptive setting. We address this gap by providing an information-theoretic converse that holds even in the adaptive setting, as well as a near-optimal noiseless adaptive algorithm for $\gamma$-divisible items. In broad scaling regimes, our upper and lower bounds asymptotically match up to a factor of $e$. We also present a simple asymptotically optimal adaptive algorithm for $\rho$-sized tests. In addition, in the non-adaptive setting with $\gamma$-divisible items, we use the Definite Defectives (DD) decoding algorithm and study bounds on the required number of tests for vanishing error probability under the random near-constant test-per-item design. We show that the number of tests required can be significantly less than the Combinatorial Orthogonal Matching Pursuit (COMP) decoding algorithm, and is asymptotically optimal in broad scaling regimes.