Statistical two-round search for one excellent element

2026-05-15

AI Summary

This work addresses a statistical search problem in which one seeks to identify a single sparse "good" element among a population of size $n$, where each element is independently good with probability $\lambda/n$. The setting permits only two rounds of noiseless group tests and requires success with probability at least $1-\alpha$. The authors establish the feasibility condition $\alpha \ge e^{-\lambda}$ and prove that, under this condition, the optimal expected number of tests grows logarithmically with $n$. They construct an upper bound by combining an initial existence test in the first round with a separation strategy in the second round, and derive a matching information-theoretic lower bound, thereby rigorously characterizing the logarithmic optimal testing complexity of this two-round search problem. Numerical experiments confirm both the feasibility threshold and the predicted logarithmic scaling.

Abstract

We formulate and study a statistical version of Katona's two-round search problem of finding at least one excellent element in a set. A population of $n$ elements is considered, where each element is independently excellent with probability $\lambda/n$, $\lambda>0$. A subset test is noiseless: it returns positive exactly when the queried subset contains at least one excellent element. The goal is to minimize the expected number of tests subject to finding one excellent element with probability at least $1-\alpha$, where $0<\alpha<1$, under the restriction that testing is performed in two rounds. Unlike classical group testing, the objective is not to recover the full set of excellent elements, but only to identify one of them. We first show that success is fundamentally limited by the possibility that no excellent element exists. In the sparse Poisson regime, this imposes the necessary feasibility condition $\alpha\ge e^{-\lambda}$. When the target success probability is feasible, we prove that the optimal expected number of tests grows logarithmically with the population size. The upper bound is obtained by combining an initial existence test with a second-round separating design; the lower bound follows from an information-counting argument. Numerical illustrations show the feasibility boundary and the resulting logarithmic scaling.

Problem

• group testing
• two-round search
• excellent element
• sparse Poisson regime
• statistical search

Innovation

• two-round group testing
• statistical search
• sparse Poisson regime
• existence detection
• logarithmic scaling