Efficient Deterministic Quantitative Group Testing for Precise Information Retrieval
Dariusz R. Kowalski, Dominik Pajak

TL;DR
This paper introduces efficient, deterministic, non-adaptive algorithms for quantitative group testing that significantly reduce the number of queries needed, operate in polynomial time, and handle various complexities such as capped results and multi-sets.
Contribution
It presents the first efficient deterministic non-adaptive algorithms for QGT that improve query complexity and are computationally feasible, unlike prior randomized or theoretical approaches.
Findings
Nearly optimal query reduction by a factor of |K|
Polynomial-time construction and reconstruction algorithms
Applicable to multi-sets and capped query results
Abstract
The Quantitative Group Testing (QGT) is about learning a (hidden) subset of some large domain using a sequence of queries, where a result of a query provides information about the size of the intersection of the query with the unknown subset . Almost all previous work focused on randomized algorithms minimizing the number of queries; however, in case of large domains , randomization may result in a significant deviation from the expected precision. Others assumed unlimited computational power (existential results) or adaptiveness of queries. In this work we propose efficient non-adaptive deterministic QGT algorithms for constructing queries and deconstructing a hidden set from the results of the queries, without using randomization, adaptiveness or unlimited computational power. The efficiency is three-fold. First, in terms of almost-optimal number of queries - we…
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Taxonomy
TopicsSARS-CoV-2 detection and testing · Advanced biosensing and bioanalysis techniques · Privacy-Preserving Technologies in Data
