关于具有限制大小查询的Rényi–Ulam对策

IF 0.9 4区 数学 Q3 MATHEMATICS, APPLIED
Ádám X. Fraknói , Dávid Á. Márton , Dániel G. Simon , Dániel A. Lenger
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引用次数: 0

摘要

我们研究了著名的Rényi–Ulam游戏的以下版本。两名选手——提问者和回答者——相互对抗。响应者想到集合{1,…,n}中的一个数字,提问者必须找到这个数字。要做到这一点,他可以询问一组最多k个元素是否包含思维数。响应者立即回答“是”或“否”,但在游戏中,他最多可能会撒谎ℓ 时间。提问者确定找到未知元素所需的最小查询次数由RU表示ℓk(n)。首先,我们开发了一个高效的工具,我们称之为凸性引理。利用这个引理,我们给出了RU的一般下界ℓk(n)和与下限相差至多2的上界ℓ+1.当n与k相比足够大时,我们也给出了它的精确值。通过这些,我们设法改进和推广了孟、林和杨在2013年一篇关于该情况的论文中获得的结果ℓ=1.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the Rényi–Ulam game with restricted size queries

We investigate the following version of the well-known Rényi–Ulam game. Two players – the Questioner and the Responder – play against each other. The Responder thinks of a number from the set {1,,n}, and the Questioner has to find this number. To do this, he can ask whether a chosen set of at most k elements contains the thought number. The Responder answers with YES or NO immediately, but during the game, he may lie at most times. The minimum number of queries needed for the Questioner to surely find the unknown element is denoted by RUk(n). First, we develop a highly effective tool that we call Convexity Lemma. By using this lemma, we give a general lower bound of RUk(n) and an upper bound which differs from the lower one by at most 2+1. We also give its exact value when n is sufficiently large compared to k. With these, we managed to improve and generalize the results obtained by Meng, Lin, and Yang in a 2013 paper about the case =1.

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来源期刊
Discrete Optimization
Discrete Optimization 管理科学-应用数学
CiteScore
2.10
自引率
9.10%
发文量
30
审稿时长
>12 weeks
期刊介绍: Discrete Optimization publishes research papers on the mathematical, computational and applied aspects of all areas of integer programming and combinatorial optimization. In addition to reports on mathematical results pertinent to discrete optimization, the journal welcomes submissions on algorithmic developments, computational experiments, and novel applications (in particular, large-scale and real-time applications). The journal also publishes clearly labelled surveys, reviews, short notes, and open problems. Manuscripts submitted for possible publication to Discrete Optimization should report on original research, should not have been previously published, and should not be under consideration for publication by any other journal.
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