This paper describes a novel and fast, simple and robust algorithm with O(N) expected complexity which enables to decrease run time needed to find the maximum distance of two points in E2. It can be easily modified for the E3 case in general. The proposed algorithm has been evaluated experimentally on larger different datasets in order to verify it and prove expected properties of it. Experiments proved the advantages of the proposed algorithm over the standard algorithms based on the Brute force, convex hull or convex hull diameters approaches. The proposed algorithm gives a significant speed-up to applications, when medium and large data sets are processed. It is over 10 000 times faster than the standard Brute force algorithm for 10 mil. points randomly distributed points in E2 and over 4 times faster than convex hull diameter computation. The speed-up of the proposed algorithm grows with the number of points processed.
翻译:本文描述了一种具有O(N)预期复杂性的新颖、快速、简单和稳健的算法,这种算法能够减少在E2中找到两个点的最大距离所需的运行时间。对于E3案件来说,这种算法一般可以很容易地修改。提议的算法是实验性地对较大的不同数据集进行评估,以便核实和证明它的预期特性。实验证明了提议的算法优于基于布鲁特力、 convex船体或convex船体直径等法的标准算法方法。提议的算法在处理中大数据集时,使应用速度大大加快。它比标准布鲁特力算法速度1 000万倍以上。在E2中随机分布的点比Convex船体直径计算速度快4倍以上。提议的算法的速度随着处理的点数的增加而加快。