5,585 research outputs found

    Minimum Convex Partitions and Maximum Empty Polytopes

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    Let SS be a set of nn points in Rd\mathbb{R}^d. A Steiner convex partition is a tiling of conv(S){\rm conv}(S) with empty convex bodies. For every integer dd, we show that SS admits a Steiner convex partition with at most (n1)/d\lceil (n-1)/d\rceil tiles. This bound is the best possible for points in general position in the plane, and it is best possible apart from constant factors in every fixed dimension d3d\geq 3. We also give the first constant-factor approximation algorithm for computing a minimum Steiner convex partition of a planar point set in general position. Establishing a tight lower bound for the maximum volume of a tile in a Steiner convex partition of any nn points in the unit cube is equivalent to a famous problem of Danzer and Rogers. It is conjectured that the volume of the largest tile is ω(1/n)\omega(1/n). Here we give a (1ε)(1-\varepsilon)-approximation algorithm for computing the maximum volume of an empty convex body amidst nn given points in the dd-dimensional unit box [0,1]d[0,1]^d.Comment: 16 pages, 4 figures; revised write-up with some running times improve

    Distance-Sensitive Planar Point Location

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    Let S\mathcal{S} be a connected planar polygonal subdivision with nn edges that we want to preprocess for point-location queries, and where we are given the probability γi\gamma_i that the query point lies in a polygon PiP_i of S\mathcal{S}. We show how to preprocess S\mathcal{S} such that the query time for a point~pPip\in P_i depends on~γi\gamma_i and, in addition, on the distance from pp to the boundary of~PiP_i---the further away from the boundary, the faster the query. More precisely, we show that a point-location query can be answered in time O(min(logn,1+logarea(Pi)γiΔp2))O\left(\min \left(\log n, 1 + \log \frac{\mathrm{area}(P_i)}{\gamma_i \Delta_{p}^2}\right)\right), where Δp\Delta_{p} is the shortest Euclidean distance of the query point~pp to the boundary of PiP_i. Our structure uses O(n)O(n) space and O(nlogn)O(n \log n) preprocessing time. It is based on a decomposition of the regions of S\mathcal{S} into convex quadrilaterals and triangles with the following property: for any point pPip\in P_i, the quadrilateral or triangle containing~pp has area Ω(Δp2)\Omega(\Delta_{p}^2). For the special case where S\mathcal{S} is a subdivision of the unit square and γi=area(Pi)\gamma_i=\mathrm{area}(P_i), we present a simpler solution that achieves a query time of O(min(logn,log1Δp2))O\left(\min \left(\log n, \log \frac{1}{\Delta_{p}^2}\right)\right). The latter solution can be extended to convex subdivisions in three dimensions

    Happy endings for flip graphs

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    We show that the triangulations of a finite point set form a flip graph that can be embedded isometrically into a hypercube, if and only if the point set has no empty convex pentagon. Point sets of this type include convex subsets of lattices, points on two lines, and several other infinite families. As a consequence, flip distance in such point sets can be computed efficiently.Comment: 26 pages, 15 figures. Revised and expanded for journal publicatio

    Approximating the Maximum Overlap of Polygons under Translation

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    Let PP and QQ be two simple polygons in the plane of total complexity nn, each of which can be decomposed into at most kk convex parts. We present an (1ε)(1-\varepsilon)-approximation algorithm, for finding the translation of QQ, which maximizes its area of overlap with PP. Our algorithm runs in O(cn)O(c n) time, where cc is a constant that depends only on kk and ε\varepsilon. This suggest that for polygons that are "close" to being convex, the problem can be solved (approximately), in near linear time
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