Sublinear-Time Non-Adaptive Group Testing With O(k log n) Tests via Bit-Mixing Coding

被引:14
作者
Bondorf, Steffen [1 ,2 ]
Chen, Binbin [3 ]
Scarlett, Jonathan [1 ,4 ,5 ]
Yu, Haifeng [1 ]
Zhao, Yuda [1 ,6 ]
机构
[1] Natl Univ Singapore, Dept Comp Sci, Singapore 117417, Singapore
[2] Ruhr Univ Bochum, Fac Math, D-44801 Bochum, Germany
[3] Singapore Univ Technol & Design, Informat Syst Technol & Design Pillar, Singapore 487372, Singapore
[4] Natl Univ Singapore, Dept Math, Singapore 119076, Singapore
[5] Natl Univ Singapore, Inst Data Sci, Singapore 117602, Singapore
[6] Adv AI, Singapore 068898, Singapore
基金
新加坡国家研究基金会;
关键词
Testing; Decoding; Error probability; Encoding; Runtime; Probabilistic logic; Upper bound; Group testing; sublinear-time decoding; sparsity;
D O I
10.1109/TIT.2020.3046113
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The group testing problem consists of determining a small set of defective items from a larger set of items based on tests on groups of items, and is relevant in applications such as medical testing, communication protocols, pattern matching, and many more. While rigorous group testing algorithms have long been known with runtime at least linear in the number of items, a recent line of works has sought to reduce the runtime to poly(k log n) , where n is the number of items and k is the number of defectives. In this paper, we present such an algorithm for non-adaptive group testing termed bit mixing coding (BMC), which builds on techniques that encode item indices in the test matrix, while incorporating novel ideas based on erasure-correction coding. We show that BMC achieves asymptotically vanishing error probability with O(k log n) tests and O(k(2) . log k . log n) runtime, in the limit as n -> infinity (with k having an arbitrary dependence on n). This closes an open problem of simultaneously achieving \mathrm poly(k log n) decoding time using O(k log n) tests without any assumptions on k. In addition, we show that the same scaling laws can be attained in a commonly-considered noisy setting, in which each test outcome is flipped with constant probability.
引用
收藏
页码:1559 / 1570
页数:12
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