An Efficient Randomized Algorithm for Higher-Order Abstract Voronoi Diagrams

Given a set of n sites in the plane, the order-k Voronoi diagram is a planar subdivision such that all points in a region share the same k nearest sites. The order-k Voronoi diagram arises for the k-nearest-neighbor problem, and there has been a lot of work for point sites in the Euclidean metric. In this paper, we study order-k Voronoi diagrams defined by an abstract bisecting curve system that satisfies several practical axioms, and thus our study covers many concrete order-k Voronoi diagrams. We propose a randomized incremental construction algorithm that runs in O(k(n-k) log^2 n +n log^3 n) steps, where O(k(n-k)) is the number of faces in the worst case. Due to those axioms, this result applies to disjoint line segments in the L_p norm, convex polygons of constant size, points in the Karlsruhe metric, and so on. In fact, this kind of run time with a polylog factor to the number of faces was only achieved for point sites in the L_1 or Euclidean metric before

On the Complexity of Randomly Weighted Voronoi Diagrams

In this paper, we provide an $O(n \mathrm{polylog} n)$ bound on the expected complexity of the randomly weighted Voronoi diagram of a set of $n$ sites in the plane, where the sites can be either points, interior-disjoint convex sets, or other more general objects. Here the randomness is on the weight of the sites, not their location. This compares favorably with the worst case complexity of these diagrams, which is quadratic. As a consequence we get an alternative proof to that of Agarwal etal [AHKS13] of the near linear complexity of the union of randomly expanded disjoint segments or convex sets (with an improved bound on the latter). The technique we develop is elegant and should be applicable to other problems

Diagramas de Voronoi de ordem k na geometria projetiva orientada

Orientador: Pedro Jussieu de RezendeDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Nesta dissertação, apresentamos uma generalização do diagrama de Voronoi: consideramos diagramas de Voronoi de ordem k no plano projetivo orientado T². Este espaço admite retas orientadas assim como muitos outros conceitos geométricos fundamentais de maneira consistente. Neste contexto, demonstramos várias propriedades de diagramas de Voronoi, algumas delas intrínsecas a T². Por exemplo, o diagrama de Voronoi de ordem k de um conjunto de n sítios em T² tem um número exato de regiões e é antípoda do diagrama de Voronoi de ordem n - k do mesmo conjunto de sítios, para todo k : 1 < k < n. Finalmente, apresentamos uma generalização, de R² para T², de dois algoritmos para construção de diagramas de Voronoi de ordem k. O primeiro algoritmo constrói os diagramas de Voronoi de todas as ordens para busca dos k vizinhos mais próximos, em tempo e espaço ótimos; enquanto o segundo é um algoritmo incremental randomizado on-line para construir o diagrama de Voronoi de cada ordem, independentemente. Para este segundo algoritmo, apresentamos um novo método para localização de pontos, o qual reduz a complexidade de tempo por um fator logarítmico e que é muito mais simples que o original.Abstract: In this dissertation, we present a generalization of the Voronoi diagram: we consider order k Voronoi diagrams in the oriented projective plane T². This space handles oriented lines as well as many other fundamental geometric concepts in a consistent way. In this context, we show several properties of Voronoi diagrams, some of them intrinsic to T². For example, the order k Voronoi diagram of a set of n sites in T² has an exact number of regions. Furthermore, this diagram is antipodal to the order n - k Voronoi diagram of the same set of sites, for all k : 1 < k < n. Finally, we present a generalization, from R² to T², of two algorithms for constructing order k Voronoi diagrams. The first one constructs all Voronoi diagrams for k nearest neighbor search, in optimal time and space, and the other is an on-line randomized incremental algorithm for constructing each order k Voronoi diagram, independently. For this second algorithm, we present a new method for point location which improves the time complexity by a logarithmic factor and which is much simpler than the original one.MestradoMestre em Ciência da Computaçã

Searching edges in the overlap of two plane graphs

Consider a pair of plane straight-line graphs, whose edges are colored red and blue, respectively, and let n be the total complexity of both graphs. We present a O(n log n)-time O(n)-space technique to preprocess such pair of graphs, that enables efficient searches among the red-blue intersections along edges of one of the graphs. Our technique has a number of applications to geometric problems. This includes: (1) a solution to the batched red-blue search problem [Dehne et al. 2006] in O(n log n) queries to the oracle; (2) an algorithm to compute the maximum vertical distance between a pair of 3D polyhedral terrains one of which is convex in O(n log n) time, where n is the total complexity of both terrains; (3) an algorithm to construct the Hausdorff Voronoi diagram of a family of point clusters in the plane in O((n+m) log^3 n) time and O(n+m) space, where n is the total number of points in all clusters and m is the number of crossings between all clusters; (4) an algorithm to construct the farthest-color Voronoi diagram of the corners of n axis-aligned rectangles in O(n log^2 n) time; (5) an algorithm to solve the stabbing circle problem for n parallel line segments in the plane in optimal O(n log n) time. All these results are new or improve on the best known algorithms.Comment: 22 pages, 6 figure

Dense point sets have sparse Delaunay triangulations

The spread of a finite set of points is the ratio between the longest and shortest pairwise distances. We prove that the Delaunay triangulation of any set of n points in R^3 with spread D has complexity O(D^3). This bound is tight in the worst case for all D = O(sqrt{n}). In particular, the Delaunay triangulation of any dense point set has linear complexity. We also generalize this upper bound to regular triangulations of k-ply systems of balls, unions of several dense point sets, and uniform samples of smooth surfaces. On the other hand, for any n and D=O(n), we construct a regular triangulation of complexity Omega(nD) whose n vertices have spread D.Comment: 31 pages, 11 figures. Full version of SODA 2002 paper. Also available at http://www.cs.uiuc.edu/~jeffe/pubs/screw.htm

Data Structures for Halfplane Proximity Queries and Incremental Voronoi Diagrams

We consider preprocessing a set $S$ of $n$ points in convex position in the plane into a data structure supporting queries of the following form: given a point $q$ and a directed line $\ell$ in the plane, report the point of $S$ that is farthest from (or, alternatively, nearest to) the point $q$ among all points to the left of line $\ell$. We present two data structures for this problem. The first data structure uses $O(n^{1+\varepsilon})$ space and preprocessing time, and answers queries in $O(2^{1/\varepsilon} \log n)$ time, for any $0 < \varepsilon < 1$. The second data structure uses $O(n \log^3 n)$ space and polynomial preprocessing time, and answers queries in $O(\log n)$ time. These are the first solutions to the problem with $O(\log n)$ query time and $o(n^2)$ space. The second data structure uses a new representation of nearest- and farthest-point Voronoi diagrams of points in convex position. This representation supports the insertion of new points in clockwise order using only $O(\log n)$ amortized pointer changes, in addition to $O(\log n)$-time point-location queries, even though every such update may make $\Theta(n)$ combinatorial changes to the Voronoi diagram. This data structure is the first demonstration that deterministically and incrementally constructed Voronoi diagrams can be maintained in $o(n)$ amortized pointer changes per operation while keeping $O(\log n)$-time point-location queries.Comment: 17 pages, 6 figures. Various small improvements. To appear in Algorithmic