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arXiv 1602.03471cs.ITmath.ITmath.STstat.TH
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通过恒定柱重设计提高分组检验率

Improved group testing rates with constant column weight designs

Matthew Aldridge, Oliver Johnson, Jonathan Scarlett

摘要:

我们考虑非自适应组测试,其中每个项目被放置在一个常数数量的测试。测试是随机随机替换的,因此测试矩阵具有(几乎)恒定的列权重。我们表明,性能得到改善相比,伯努利设计,其中每个项目被放置在每个测试独立的固定概率。特别是,我们表明,实际COMP检测算法的速度增加了31%,在所有稀疏制度。在密集的情况下,这击败了Bernoulli测试的最佳算法,在稀疏的情况下,这是任何实用算法的最佳证明性能。我们还给出了一个算法独立的上限为恒定的列重量的情况下,密集的情况下,这是再次增加了31%,类似的伯努利结果。

英文摘要:

We consider nonadaptive group testing where each item is placed in a constant number of tests. The tests are chosen uniformly at random with replacement, so the testing matrix has (almost) constant column weights. We show that performance is improved compared to Bernoulli designs, where each item is placed in each test independently with a fixed probability. In particular, we show that the rate of the practical COMP detection algorithm is increased by 31% in all sparsity regimes. In dense cases, this beats the best possible algorithm with Bernoulli tests, and in sparse cases is the best proven performance of any practical algorithm. We also give an algorithm-independent upper bound for the constant column weight case; for dense cases this is again a 31% increase over the analogous Bernoulli result.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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