We introduce a new randomized sampling technique, called Polling which has applications to deriving efficient parallel algorithms. As an example of its use in computational geometry, we present an optimal parallel randomized algorithm for intersection of half-spaces in three dimensions. Because of well-known reductions, our methods also yield equally efficient algorithms for fundamental problems like the convex hull in three dimensions, Voronoi diagram of point sites on a plane and Euclidean minimal spanning tree. Our algorithms run in time T = O(logn) for worst-case inputs and uses P = O(n) processors in a CREW PRAM model where n is the input size. They are randomized in the sense that they use a total of only O(log
我们引入了一种称为轮询的新随机抽样技术,该技术已应用于推导高效并行算法。作为其在计算几何中使用的示例,我们提出了一种最佳的并行随机算法,用于在三个维度上相交半空间。由于众所周知的减少,我们的方法还针对基本问题(例如三维凸包,平面上点位置的Voronoi图和欧几里得最小生成树)产生了同样有效的算法。对于最坏情况的输入,我们的算法在时间T = O(logn)上运行,并在CREW PRAM模型中使用P = O(n)处理器,其中n是输入大小。从它们总共使用仅O(log
机译:超快速计算系外行星大气分子不透明度的随机采样技术
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机译:随机抽样在计算几何中的应用,II
机译:计算几何中有效并行算法的随机采样技术。
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