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An optimal hypercube algorithm for the all nearest smaller values problem

机译:最接近较小值问题的最优超立方算法

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Given a sequence of n elements, the All Nearest Smaller Values (ANSV) problem is to find, for each element in the sequence, the nearest element to the left (right) that is smaller, or to report that no such element exists. Berkman, Schieber, and Vishkin (1993) give an ANSV algorithm that runs in O(lg n) time on an (n/lg n)-processor CREW PRAM. In this paper, we present an O(lg n)-time n-processor normal hypercube algorithm for the ANSV problem. Furthermore, we prove that any normal hypercube algorithm requires /spl Omega/(n) processors to solve the ANSV problem in O(lg n) time. We use our ANSV algorithm to give the first O(lg n)-time n-processor normal hypercube algorithms for triangulating a monotone polygon and for constructing a Cartesian tree.
机译:给定N个元素的序列,所有最接近的较小值(ANSV)问题是为序列中的每个元素找到,左侧(右)的最近元素较小,或者报告不存在这样的元素。 Berkman,Schieber和Vishkin(1993)给出了一个ANSV算法,在(n / lg n)-processor船员摇摇车上的O(lg n)时间内运行。在本文中,我们为ANSV问题呈现了O(LG n)-time n-procestime n-processor常规超立体算法。此外,我们证明了任何正常的HyperCube算法都需要/ SPL omega /(n)处理器来解决O(LG n)时间中的ANSV问题。我们使用我们的ANSV算法给出第一个O(LG N)-Time N-处理器正常的超立体算法,用于三角形多边形和构建笛卡尔树。

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