Random sampling, halfspace range reporting, and construction of (/spl les/k)-levels in three dimensions

Timothy M. Chan
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引用次数: 6

Abstract

Given n points in three dimensions, we show how to answer halfspace range reporting queries in O(log n+k) expected time for an output size k. Our data structure can be preprocessed in optimal O(n log n) expected time. We apply this result to obtain the first optimal randomized algorithm for the construction of the (/spl les/k)-level in an arrangement of n planes in three dimensions. The algorithm runs in O(n log n+nk/sup 2/) expected time. Our techniques are based on random sampling. Applications in two dimensions include an improved data structure for "k nearest neighbors" queries, and an algorithm that constructs the order-k Voronoi diagram in O(n log n+nk log k) expected time.
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随机抽样,半空间范围报告,以及三维(/spl les/k)-水平的构建
给定三维空间中的n个点,我们展示了如何在O(log n+k)预期时间内回答输出大小为k的半空间范围报告查询。我们的数据结构可以在最优O(n log n)预期时间内进行预处理。我们应用这一结果获得了在三维n个平面排列中构造(/spl les/k)-级的第一个最优随机算法。算法运行的预期时间为O(n log n+nk/sup 2/)。我们的技术是基于随机抽样。二维的应用程序包括“k个最近邻”查询的改进数据结构,以及在O(n log n+nk log k)预期时间内构建order-k Voronoi图的算法。
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