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On the Average Case of MergeInsertion

机译:关于MergeInsertion的平均情况

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摘要

Mergelnsertion, also known as the Ford-Johnson algorithm, is a sorting algorithm which, up to today, for many input sizes achieves the best known upper bound on the number of comparisons. Indeed, it gets extremely close to the information-theoretic lower bound. While the worst-case behavior is well understood, only little is known about the average case. This work takes a closer look at the average case behavior. In particular, we establish an upper bound of n log n — 1.4005n + o(n) comparisons. We also give an exact description of the probability distribution of the length of the chain a given element is inserted into and use it to approximate the average number of comparisons numerically. Moreover, we compute the exact average number of comparisons for n up to 148. Furthermore, we experimentally explore the impact of different decision trees for binary insertion. To conclude, we conduct experiments showing that a slightly different insertion order leads to a better average case and we compare the algorithm to the recent combination with (1,2)-Insertionsort by Iwama and Teruyama.
机译:Mergelnsertion,也称为Ford-Johnson算法,是一种排序算法,直到今天,对于许多输入大小,它都达到了比较次数最广为人知的上限。实际上,它非常接近信息理论的下限。尽管最坏情况的行为已广为人知,但对一般情况知之甚少。这项工作将仔细研究平均案例行为。特别是,我们建立了n个log n — 1.4005n + o(n)比较的上限。我们还给出了插入给定元素的链的长度的概率分布的精确描述,并使用它来近似地计算比较的平均次数。此外,我们计算n的精确比较平均数,最高可达148。此外,我们还实验性地探索了不同决策树对二进制插入的影响。总而言之,我们进行的实验表明,插入顺序略有不同会导致更好的平均情况,并且将算法与Iwama和Teruyama的(1,2)-Insertionsort的最新组合进行了比较。

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