A Simple Proof of the Shallow Packing Lemma

International audienceWe show that the shallow packing lemma follows from a simple modification of the standard proof, due to Haussler and simplified by Chazelle, of the packing lemma

Tighter Estimates for ϵ-nets for Disks

International audienceThe geometric hitting set problem is one of the basic geometric combinatorial optimization problems: given a set P of points, and a set D of geometric objects in the plane, the goal is to compute a small-sized subset of P that hits all objects in D. In 1994, Bronniman and Goodrich [5] made an important connection of this problem to the size of fundamental combinatorial structures called ϵ-nets, showing that small-sized ϵ-nets imply approximation algorithms with correspondingly small approximation ratios. Very recently, Agarwal and Pan [2] showed that their scheme can be implemented in near-linear time for disks in the plane. Altogether this gives O(1)-factor approximation algorithms in O(n) time for hitting sets for disks in the plane. This constant factor depends on the sizes of ϵ-nets for disks; unfortunately, the current state-of-the-art bounds are large – at least 24/ϵ and most likely larger than 40/ϵ. Thus the approximation factor of the Agarwal and Pan algorithm ends up being more than 40. The best lower-bound is 2/ϵ, which follows from the Pach-Woeginger construction [32] for halfplanes in two dimensions. Thus there is a large gap between the best-known upper and lower bounds. Besides being of independent interest, finding precise bounds is important since this immediately implies an improved linear-time algorithm for the hitting-set problem. The main goal of this paper is to improve the upper-bound to 13.4/ϵ for disks in the plane. The proof is constructive, giving a simple algorithm that uses only Delaunay triangulations. We have implemented the algorithm, which is available as a public open-source module. Experimental results show that the sizes of-nets for a variety of data-sets is lower, around 9/ϵ

Shallow Packings, Semialgebraic Set Systems, Macbeath Regions, and Polynomial Partitioning

The packing lemma of Haussler states that given a set system (X,R) with bounded VC dimension, if every pair of sets in R have large symmetric difference, then R cannot contain too many sets. Recently it was generalized to the shallow packing lemma, applying to set systems as a function of their shallow-cell complexity. In this paper we present several new results and applications related to packings: * an optimal lower bound for shallow packings, * improved bounds on Mnets, providing a combinatorial analogue to Macbeath regions in convex geometry, * we observe that Mnets provide a general, more powerful framework from which the state-of-the-art unweighted epsilon-net results follow immediately, and * simplifying and generalizing one of the main technical tools in [Fox et al.J. of the EMS, to appear]

Optimal Area-Sensitive Bounds for Polytope Approximation

Approximating convex bodies is a fundamental question in geometry and has a wide variety of applications. Given a convex body $K$ of diameter $\Delta$ in $\mathbb{R}^d$ for fixed $d$, the objective is to minimize the number of vertices (alternatively, the number of facets) of an approximating polytope for a given Hausdorff error $\varepsilon$. The best known uniform bound, due to Dudley (1974), shows that $O((\Delta/\varepsilon)^{(d-1)/2})$ facets suffice. While this bound is optimal in the case of a Euclidean ball, it is far from optimal for ``skinny'' convex bodies. A natural way to characterize a convex object's skinniness is in terms of its relationship to the Euclidean ball. Given a convex body $K$, define its surface diameter $\Delta_{d-1}$ to be the diameter of a Euclidean ball of the same surface area as $K$. It follows from generalizations of the isoperimetric inequality that $\Delta \geq \Delta_{d-1}$. We show that, under the assumption that the width of the body in any direction is at least $\varepsilon$, it is possible to approximate a convex body using $O((\Delta_{d-1}/\varepsilon)^{(d-1)/2})$ facets. This bound is never worse than the previous bound and may be significantly better for skinny bodies. The bound is tight, in the sense that for any value of $\Delta_{d-1}$, there exist convex bodies that, up to constant factors, require this many facets. The improvement arises from a novel approach to sampling points on the boundary of a convex body. We employ a classical concept from convexity, called Macbeath regions. We demonstrate that Macbeath regions in $K$ and $K$'s polar behave much like polar pairs. We then apply known results on the Mahler volume to bound their number

Near-Optimal Generalisations of a Theorem of Macbeath

International audienceThe existence of Macbeath regions is a classical theorem in convex geometry ("A Theorem on non-homogeneous lattices'', Annals of Math, 1952). We refer the reader to the survey of I. Barany for several applications~\cite{B07}. Recently there have been some striking applications of Macbeath regions in discrete and computational geometry. In this paper, we study Macbeath's problem in a more general setting, and not only for the Lebesgue measure as is the case in the classical theorem. We prove near-optimal generalizations for several basic geometric set systems. The problems and techniques used are closely linked to the study of epsilon-nets for geometric set systems