On the power of the semi-separated pair decomposition
Computational Geometry: Theory and Applications
A faster algorithm for computing motorcycle graphs
Proceedings of the twenty-ninth annual symposium on Computational geometry
Computational Geometry: Theory and Applications
Computational Geometry: Theory and Applications
| Hi-index | 0.00 |
We revisit one of the most fundamental classes of data structure problems in computational geometry: range searching. Matoušek (Discrete Comput. Geom. 10:157–182, 1993) gave a partition tree method for d-dimensional simplex range searching achieving O(n) space and O(n 1−1/d ) query time. Although this method is generally believed to be optimal, it is complicated and requires O(n 1+ε ) preprocessing time for any fixed ε0. An earlier method by Matoušek (Discrete Comput. Geom. 8:315–334, 1992) requires O(nlogn) preprocessing time but O(n 1−1/d logO(1) n) query time. We give a new method that achieves simultaneously O(nlogn) preprocessing time, O(n) space, and O(n 1−1/d ) query time with high probability. Our method has several advantages: It is conceptually simpler than Matoušek’s O(n1−1/d)-time method. Our partition trees satisfy many ideal properties (e.g., constant degree, optimal crossing number at almost all layers, and disjointness of the children’s cells at each node). It leads to more efficient multilevel partition trees, which are needed in many data structuring applications (each level adds at most one logarithmic factor to the space and query bounds, better than in all previous methods). A similar improvement applies to a shallow version of partition trees, yielding O(nlogn) time, O(n) space, and O(n1−1/⌊d/2⌋) query time for halfspace range emptiness in even dimensions d≥4. Numerous consequences follow (e.g., improved results for computing spanning trees with low crossing number, ray shooting among line segments, intersection searching, exact nearest neighbor search, linear programming queries, finding extreme points, …).