论文标题
算法加速度的极性，球形和正交空间细分：o（1） - 多角度/多面体测试
Polar, Spherical and Orthogonal Space Subdivisions for an Algorithm Acceleration: O(1) Point-in-Polygon/Polyhedron Test
论文作者
论文摘要
如果要处理较大的数据集，算法的加速度正在成为一个关键问题。算法的评估主要是通过使用计算几何方法和计算复杂性评估来完成的。但是，在当今的工程问题中，这种方法并不尊重处理的项目数量总是有限的，并且重要的角色也扮演着读/写操作的速度。一种通用方法如何加快算法是空间细分技术的应用，通常使用正交空间细分。在本文中，描述了非正交细分。提出的方法可以显着改善内存消耗和运行时的复杂性。提出的修改空间细分技术在两个简单的问题点式多边形和cont-In-Convex多面体测试上证明了。
Acceleration of algorithms is becoming a crucial problem, if larger data sets are to be processed. Evaluation of algorithms is mostly done by using computational geometry approach and evaluation of computational complexity. However in todays engineering problems this approach does not respect that number of processed items is always limited and a significant role plays also speed of read/write operations. One general method how to speed up an algorithm is application of space subdivision technique and usually the orthogonal space subdivision is used. In this paper non-orthogonal subdivisions are described. The proposed approach can significantly improve memory consumption and run-time complexity. The proposed modified space subdivision techniques are demonstrated on two simple problems Point-in-Convex Polygon and Point-in-Convex Polyhedron tests.
