Orthogonal range searching on the RAM, revisited

Abstract

We present a number of new results on one of the most extensively studied topics in computational geometry, orthogonal range searching. All our results are in the standard word RAM model:1. We present two data structures for 2-d orthogonal range emptiness. The first achieves O(n lg lg n) space and O(lg lg n) query time, assuming that the n given points are in rank space. This improves the previous results by Alstrup, Brodal, and Rauhe (FOCS'00), with O(n lg ε n) space and O(lg lg n) query time, or with O(n lg lg n) space and O(lg 2 lg n) query time. Our second data structure uses O(n) space and answers queries in O(lg ε n) time. The best previous O(n)-space data structure, due to Nekrich (WADS'07), answers queries in O(lg n/ lg lg n) time.2. We give a data structure for 3-d orthogonal range reporting with O(n lg 1+ε n) space and O(lg lg n + k) query time for points in rank space, for any constant ε > 0. This improves the previous results by Afshani (ESA'08), Karpinski and Nekrich (COCOON'09), and Chan (SODA'11), with O(n lg 3 n) space and O(lg lg n + k) query time, or with O(n lg 1+ε n) space and O(lg 2 lg n + k) query time. Consequently, we obtain improved upper bounds for orthogonal range reporting in all constant dimensions above 3. Our approach also leads to a new data structure for 2-d orthogonal range minimum queries with O(n lg ε n) space and O(lg lg n) query time for points in rank space.3. We give a randomized algorithm for 4-d offline dominance range reporting/emptiness with running time O(n lg n) plus the output size. This resolves two open problems (both appeared in Preparata and Shamos' seminal book): (a) given a set of n axis-aligned rectangles in the plane, we can report all k enclosure pairs (i.e., pairs (r 1 , r 2 ) where rectangle r 1 completely encloses rectangle r 2 ) in O(n lg n + k) expected time; (b) given a set of n points in 4-d, we can find all maximal points (points not dominated by any other points) in O(n lg n) expected time. The most recent previous development on (a) was reported back in SoCG'95 by Gupta, Janardan, Smid, and Dasgupta, whose main result was an O([n lg n + k] lg lg n) algorithm. The best previous result on (b) was an O(n lg n lg lg n) algorithm due to Gabow, Bentley, and Tarjan-from STOC'84! As a consequence, we also obtain the current-record time bound for the maxima problem in all constant dimensions above 4.

On Constant Factors in Comparison-Based Geometric Algorithms and Data Structures

“…Kung, Luccio, and Preparata first gave a O (n log n) time algorithm for d = 3 and a O n log d−2 n time algorithm for d ≥ 4 [43]. For d = 4, Gabow, Bentley and Tarjan gave a O n log d−3 n log log n time algorithm [31], Chan, Larsen, and Pǎtraşcu improved the time to O n log d−3 n [9]. If we only count comparisons, the trivial solution requires dn log n + O(dn) comparisons.…”

Optimal Planar Orthogonal Skyline Counting Queries

“…[4] for an extensive list of previous results. For orthogonal range queries in the plane, with integer coordinates in [n] × [n] = {0, .…”

“…The best upper bounds known for range reporting are: Optimal space O(n) and query time O((k + 1) lg ε n) [4], and optimal query time O(lg lg n + k) with space O(n lg ε n) [1]. In both cases ε > 0 is an arbitrarily small constant.…”

“…Storing an additional O(n) sized array mapping global y-ranks to the corresponding points gives a total running time of O((1 + k) lg n/ lg lg n). To speed this up, we use the Ball-Inheritance structure of [4]. For completeness, we describe how this data structure is implemented in terms of the π arrays we have defined: Let B ≥ 2 be a parameter.…”

Sequential dependency computation via geometric data structures

“…We leave open the existence of a O(N log N )-time algorithm, and suggest Willard [6] or Chan, Larsen, and Pȃtrascu [1] as a possible starting point.…”