论文标题
快速O_ {预期}（n）算法，用于在E^2中找到精确的最大距离，而不是O（n^2）或O（n lg n）
Fast O_{expected}(N) Algorithm for Finding Exact Maximum Distance in E^2 Instead of O(N^2) or O(N lg N)
论文作者
论文摘要
本文描述了具有O（n）预期复杂性的新颖，快速，简单且健壮的算法，这使得在E2中找到了两个点的确切最大距离所需的运行时。已在较大的不同数据集上对所提出的算法进行了实验评估。当处理中和大数据集时，所提出的算法对应用程序进行了显着加速。它的速度超过10 000倍，在E2中随机分布点的10^6点的标准算法要快。实验证明了所提出的算法比标准算法和凸壳直径接近的优势。
This paper describes novel and fast, simple and robust algorithm with O(N) expected complexity which enables to decrease run-time needed to find an exact maximum distance of two points in E2. The proposed algorithm has been evaluated experimentally on larger different datasets. 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 algorithm for 10^6 points randomly distributed points in E2. Experiments proved the advantages of the proposed algorithm over the standard algorithm and convex hull diameters approaches.
