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arXiv 2409.01692math.PRcs.DMmath.CO
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记录偏置排列及其排列极限

Record-biased permutations and their permuton limit

Université de Lorraine(洛林大学);CNRS(法国国家科学研究中心);Inria(法国国家信息与自动化研究所);LORIA(洛林研究与信息系统实验室);Université Gustave Eiffel(古斯塔夫·埃菲尔大学);LIGM(信息学与计算力学实验室)

Mathilde Bouvel, Cyril Nicaud, Carine Pivoteau

AI总结:

本文研究了一种基于记录数的非均匀排列分布,探讨了其生成过程及高效采样方法,并分析了经典排列统计量的期望及其极限分布,最终在记录数线性增长时证明了排列趋于确定性排列极限。

摘要:

在本文中,我们研究了一种称为记录偏置排列的非均匀排列分布,通过几种生成过程解释了如何设计高效的线性随机采样器。对于几种经典排列统计量,我们利用上述生成过程计算了其期望,并在记录数为对数量级时确定了其极限分布。最后,当偏置增加以获得记录数线性增长的 regime 时,我们证明了记录偏置排列收敛于一个确定性的排列极限,并对其进行了完全刻画。该模型最初在我们的先前工作[N. Auger, M. Bouvel, C. Nicaud, C. Pivoteau, Analysis of Algorithms for Permutations Biased by Their Number of Records, AofA 2016]中提出,用于现实算法分析的背景下。本文在此基础上进行了更深入的理论研究。

英文摘要:

In this article, we study a non-uniform distribution on permutations biased by their number of records that we call \emph{record-biased permutations}. We give several generative processes for record-biased permutations, explaining also how they can be used to devise efficient (linear) random samplers. For several classical permutation statistics, we obtain their expectation using the above generative processes, as well as their limit distributions in the regime that has a logarithmic number of records (as in the uniform case). Finally, increasing the bias to obtain a regime with an expected linear number of records, we establish the convergence of record-biased permutations to a deterministic permuton, which we fully characterize. This model was introduced in our earlier work [N. Auger, M. Bouvel, C. Nicaud, C. Pivoteau, \emph{Analysis of Algorithms for Permutations Biased by Their Number of Records}, AofA 2016], in the context of realistic analysis of algorithms. We conduct here a more thorough study but with a theoretical perspective.

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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