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We extend the notion of universal graphs to a geometric setting. A geometric graph is universal for a class $\mathcal H$ of planar graphs if it contains an embedding, that is, a crossing-free drawing, of every graph in $\mathcal H$. Our main result is that there exists a geometric graph with $n$ vertices and $O\!\left(n \log n\right)$ edges that is universal for $n$-vertex forests; this generalises a well-known result by Chung and Graham, which states that there exists an (abstract) graph with $n$ vertices and $O\!\left(n \log n\right)$ edges that contains every $n$-vertex forest as a subgraph. The upper bound of $O\!\left(n \log n\right)$ edges cannot be improved, even if more than $n$ vertices are allowed. We also prove that every $n$-vertex convex geometric graph that is universal for $n$-vertex outerplanar graphs has a near-quadratic number of edges, namely $\Omega _h(n^{2-1/h})$, for every positive integer $h$; this almost matches the trivial $O(n^2)$ upper bound given by the $n$-vertex complete convex geometric graph. Finally, we prove that there exists an $n$-vertex convex geometric graph with $n$ vertices and $O\!\left(n \log n\right)$ edges that is universal for $n$-vertex caterpillars.
We show that the maximum number of convex polygons in a triangulation of n points in the plane is O(1.5029n). This improves an earlier bound of O(1.6181n) established by van Kreveld, Löffler and Pach (2012), and almost matches the current best lower bound of Ω(1.5028n) due to the same authors. Given a planar straight-line graph G with n vertices, we also show how to compute efficiently the number of convex polygons in G.
Given a set of s points and a set of n2 lines in three-dimensional Euclidean space such that each line is incident to n points but no n lines are coplanar, we show that s = Ω(n11/4). This is the first non-trivial answer to a question recently posed by Jean Bourgain.
We formulate and give partial answers to several combinatorial problems on volumes of simplices determined by n points in 3-space, and in general in d dimensions.
(i) The number of tetrahedra of minimum (non-zero) volume spanned by n points in 3 is at most , and there are point sets for which this number is . We also present an O(n3) time algorithm for reporting all tetrahedra of minimum non-zero volume, and thereby extend an algorithm of Edelsbrunner, O'Rourke and Seidel. In general, for every , the maximum number of k-dimensional simplices of minimum (non-zero) volume spanned by n points in d is Θ(nk).
(ii) The number of unit volume tetrahedra determined by n points in 3 is O(n7/2), and there are point sets for which this number is Ω(n3 log logn).
(iii) For every , the minimum number of distinct volumes of all full-dimensional simplices determined by n points in d, not all on a hyperplane, is Θ(n).
Edited by
Jacob E. Goodman, City College, City University of New York,Janos Pach, City College, City University of New York and New York University,Emo Welzl, Eidgenössische Technische Hochschule Zürich
A binary space partition tree is a data structure for the representation of a set of objects in space. It found an increasing number of applications over the last decades. In recent years, intensifying research focused on its combinatorial properties, which affect directly the efficiency of applications. Important advances were made on binary space partitions for disjoint line segments in the plane and for axis-aligned objects in higher dimensions. New research directions were also initiated on some realistic polygonal scenes and on kinetic binary space partitions. This paper attempts to give an overview of these results and reiterates some of the most pressing open problems.
1. Introduction
The binary space partition tree is a geometric data structure obtained by a recursive partitioning scheme, called binary space partition (for short, BSP) over a set of input objects: The space is partitioned along a hyper plane into two half-spaces, then either half-space is partitioned recursively until every subproblem contains only a trivial fraction of the input objects. The concept of BSP has emerged from the computer graphics community in the seventies. It was originally designed to assist efficient hidden-surface removal algorithms for moving viewpoints, but it has later found widespread applications in many areas of computational and combinatorial geometry.
In many of the applications, the bottle neck of the space complexity is the size of the BSP tree they rely on. Combinatorial research focused on determining the worst case complexity of BSPs for certain classes of inputs.
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