NonAdaptive Randomized Algorithm for Group Testing
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Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:109128, 2017.
Abstract
We study the problem of group testing with a nonadaptive randomized algorithm in the random incidence design (RID) model where each entry in the test is chosen randomly independently from $\{0,1\}$ with a fixed probability $p$.
The property that is sufficient and necessary for a unique decoding is the separability of the tests, but unfortunately no linear time algorithm is known for such tests. In order to achieve lineartime decodable tests, the algorithms in the literature use the disjunction property that gives almost optimal number of tests.
We define a new property for the tests which we call semidisjunction property. We show that there is a linear time decoding for such test and for $d\to \infty$ the number of tests converges to the number of tests with the separability property. Our analysis shows that, in the RID model, the number of tests in our algorithm is better than the one with the disjunction property even for small $d$.
The property that is sufficient and necessary for a unique decoding is the separability of the tests, but unfortunately no linear time algorithm is known for such tests. In order to achieve lineartime decodable tests, the algorithms in the literature use the disjunction property that gives almost optimal number of tests.
We define a new property for the tests which we call semidisjunction property. We show that there is a linear time decoding for such test and for $d\to \infty$ the number of tests converges to the number of tests with the separability property. Our analysis shows that, in the RID model, the number of tests in our algorithm is better than the one with the disjunction property even for small $d$.
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