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The analysis of Range Quickselect and related problems

机译:范围快速选择分析及相关问题

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摘要

Range Quickselect, a simple modification of the well-known Quickselect algorithm for selection, can be used to efficiently find an element with rank k in a given range [i..j], out of n given elements. We study basic cost measures of Range Quickselect by computing exact and asymptotic results for the expected number of passes, comparisons and data moves during the execution of this algorithm.The key element appearing in the analysis of Range Quickselect is a trivariate recurrence that we solve in full generality. The general solution of the recurrence proves to be very useful, as it allows us to tackle several related problems, besides the analysis that originally motivated us.In particular, we have been able to carry out a precise analysis of the expected number of moves of the pth element when selecting the jth smallest element with standard Quickselect, where we are able to give both exact and asymptotic results.Moreover, we can apply our general results to obtain exact and asymptotic results for several parameters in binary search trees, namely the expected number of common ancestors of the nodes with rank i and j, the expected size of the subtree rooted at the least common ancestor of the nodes with rank i and j, and the expected distance between the nodes of ranks i and j.
机译:范围快速选择是对众所周知的选择快速选择算法的简单修改,可用于从n个给定元素中有效地找到等级k在给定范围[i..j]中的元素。我们通过计算执行此算法期间的预期通过次数,比较和数据移动的精确和渐近结果,研究Ra​​nge Quickselect的基本成本度量。RangeQuickselect分析中出现的关键要素是我们解决的三元递归问题全面性。事实证明,重现的一般解决方案非常有用,因为它可以使我们解决最初引起我们的分析之外的几个相关问题,尤其是我们能够对预期的运动次数进行精确的分析。使用标准Quickselect选择第j个最小元素时的pth元素,我们可以给出精确和渐近结果。此外,我们可以应用一般结果来获取二叉搜索树中多个参数的精确和渐近结果,即期望等级为i和j的节点的共同祖先的数量,以等级为i和j的节点的最小共同祖先为根的子树的预期大小,以及等级为i和j的节点之间的期望距离。

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