Simplicial partitions are a fundamental structure in computational geometry, as they form the basis of optimal data structures for range searching and several related problems. Current algorithms are built on very specific spatial partitioning tools tailored for certain geometric cases. This severely limits their applicability to general set systems. In this work, we propose a simple greedy heuristic for constructing simplicial partitions of any set system. We present a thorough empirical evaluation of its behavior on a variety of geometric and non-geometric set systems, showing that it performs well on most instances.

International audience

Matchings with low crossing numbers were originally introduced in the late 1980s in the seminal works of Welzl [Welzl, 1988; Welzl, 1992] and Chazelle-Welzl [Chazelle and Welzl, 1989]. They have since become fundamental structures in combinatorics, computational geometry, and algorithms. In this paper, we study matchings with low crossing numbers and their relation to random samples. In particular, our main technical result states that, given a set system (X, 𝒮) with dual VC-dimension d and a parameter α ∈ (0, 1], a random set of Θ̃(n^{1+α}) edges of binom(X,2) contains a linear-sized matching with crossing number O (n^{1-α/d}). Furthermore, we show that this bound is optimal up to a logarithmic factor. By incorporating the above sampling step to existing algorithms, we obtain improved running times, by a factor of Θ̃(n), for computing matchings with low crossing numbers. This immediately implies new bounds for a number of well-studied problems, such as combinatorial discrepancy, ε-approximations and their applications. To the best of our knowledge, these are the first near-linear time algorithms for general, non-geometric set systems, for a) matchings with sub-linear crossing numbers, and b) discrepancy beating the standard deviation bound. As an immediate consequence we get fast algorithms for computing o(1/ε²)-sized ε-approximations.

For any set system ℋ = (V,ℛ), ℛ ⊆ 2^V, a subset S ⊆ V is called shattered if every S' ⊆ S results from the intersection of S with some set in ℛ. The VC-dimension of ℋ is the size of a largest shattered set in V. In this paper, we focus on the problem of computing the VC-dimension of graphs. In particular, given a graph G = (V,E), the VC-dimension of G is defined as the VC-dimension of (V, N), where N contains each subset of V that can be obtained as the closed neighborhood of some vertex v ∈ V in G. Our main contribution is an algorithm for computing the VC-dimension of any graph, whose effectiveness is shown through experiments on various types of practical graphs, including graphs with millions of vertices. A key aspect of its efficiency resides in the fact that practical graphs have small VC-dimension, up to 8 in our experiments. As a side-product, we present several new bounds relating the graph VC-dimension to other classical graph theoretical notions. We also establish the W[1]-hardness of the graph VC-dimension problem by extending a previous result for arbitrary set systems.

The VC-dimension of a set system is a way to capture its complexity and has been a key parameter studied extensively in machine learning and geometry communities. In this paper, we resolve two longstanding open problems on bounding the VC-dimension of two fundamental set systems: $k$-fold unions/intersections of half-spaces and the simplices set system. Among other implications, it settles an open question in machine learning that was first studied in the foundational paper of Blumer et al. (1989) as well as by Eisenstat and Angluin (2007) and Johnson (2008).