Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.
AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.
Let d and t be fixed positive integers, and let denote the complete d-partite hypergraph with t vertices in each of its parts, whose hyperedges are the d-tuples of the vertex set with precisely one element from each part. According to a fundamental theorem of extremal hypergraph theory, due to Erd6s [6], the number of hyperedges of a d-uniform hypergraph on n vertices that does not contain as a subhypergraph, is nd-1/t(d-1). This bound is not far from being optimal. We address the same problem restricted to intersection hypergraphs of (d 1)-dimensional simplices in Rd. Given an n-element set S of such simplices, let H-d(S) denote the d-uniform hypergraph whose vertices are the elements of S, and a d-tuple is a hyperedge if and only if the corresponding simplices have a point in common. We prove that if H-d(S) does not contain K-t(d) . . t , as a subhypergraph, then its number of edges is O(n) if d = 2, and O(n(d-1)++epsilon) for any e > 0 if d >= 3. This is almost a factor of n better than Erdos's above bound. Our result is tight, apart from the error term E in the exponent. In particular, for d = 2, we obtain a theorem of Fox and Pach [7], which states that every Kt,t-free intersection graph of n segments in the plane has 0(n) edges. The original proof was based on a separator theorem that does not generalize to higher dimensions. The new proof works in any dimension and is simpler: it uses size-sensitive cuttings, a variant of random sampling. (C) 2016 Elsevier Inc. All rights reserved.
Philippe Schwaller, Junwu Chen