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Jean-Daniel Boissonnat, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt,Mariette Yvinec, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt
Jean-Daniel Boissonnat, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt,Mariette Yvinec, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt
Jean-Daniel Boissonnat, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt,Mariette Yvinec, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt
Jean-Daniel Boissonnat, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt,Mariette Yvinec, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt
The goal of this and subsequent chapters is to introduce the algorithmic methods that are used most frequently to solve geometric problems. Generally speaking, computational geometry has recourse to all of the classical algorithmic techniques. Readers examining all the algorithms described in this book from a methodological point of view will distinguish essentially three methods: the incremental method, the divide-and-conquer method, and the sweep method.
The incremental method is perhaps the method which is the most largely emphasized in the book. It is also the most natural method, since it consists of processing the input to the problem one item at a time. The algorithm initiates the process by solving the problem for a small subset of the input, then maintains the solution to the problem as the remaining data are inserted one by one. In some cases, the algorithm may initially sort the input, in order to take advantage of the fact that the data are sorted. In other cases, the order in which the data are processed is indifferent, sometimes even deliberately random. In the latter case, we are dealing with the randomized incremental method, which will be stated and analyzed at length in chapter 5. We therefore will not expand further on the incremental method in this chapter.
The divide-and-conquer method is one of the oldest methods for the design of algorithms, and its use goes well beyond geometry. In computational geometry, this method leads to very efficient algorithms for certain problems.
Jean-Daniel Boissonnat, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt,Mariette Yvinec, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt
Jean-Daniel Boissonnat, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt,Mariette Yvinec, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt
To triangulate a region is to describe it as the union of a collection of simplices whose interiors are pairwise disjoint. The region is then decomposed into elementary cells of bounded complexity. The words to triangulate and triangulation originate from the two-dimensional problem, but are commonly used in a broader context for regions and simplices of any dimension.
Triangulations and related meshes are ubiquitous in domains where the ambient space needs to be discretized, for instance in order to interpolate functions of several variables, or to numerically solve multi-dimensional differential equations using finite-element methods. Triangulations are largely used in the context of robotics to decompose the free configuration space of a robot, in the context of artificial vision to perform three-dimensional reconstructions of objects from their cross-sections, or in computer graphics to solve problems related to windows or to compute illuminations in rendering an image. Finally, in the context of computational geometry, the triangulation of a set of points, a planar map, a polygon, a polyhedron, an arrangement, or of any other spatial structures, is often a prerequisite to running another algorithm on the data. For instance, this is the case for algorithms performing point location in a planar map by using a hierarchy of triangulations, or for the numerous applications of triangulations to shortest paths and visibility problems.
Triangulations form the topic of the next three chapters. Chapter 11 recalls the basic definitions related to triangulations, and studies the combinatorics of triangulations in dimensions 2 and 3.
Jean-Daniel Boissonnat, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt,Mariette Yvinec, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt
Jean-Daniel Boissonnat, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt,Mariette Yvinec, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt
Jean-Daniel Boissonnat, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt,Mariette Yvinec, Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt
Given a class of combinatorial structures [Cscr], we consider the quantity N(n, m), the number of multiset constructions [Pscr] (of [Cscr]) of size n having exactly m [Cscr]-components. Under general analytic conditions on the generating function of [Cscr], we derive precise asymptotic estimates for N(n, m), as n→∞ and m varies through all possible values (in general 1[les ]m[les ]n). In particular, we show that the number of [Cscr]-components in a random (assuming a uniform probability measure) [Pscr]-structure of size n obeys asymptotically a convolution law of the Poisson and the geometric distributions. Applications of the results include random mapping patterns, polynomials in finite fields, parameters in additive arithmetical semigroups, etc. This work develops the ‘additive’ counterpart of our previous work on the distribution of the number of prime factors of an integer [20].
We apply an idea of Székely to prove a general upper bound on the number of incidences between a set of m points and a set of n ‘well-behaved’ curves in the plane.
A collection H of integers is called an affine d-cube if there exist d+1 positive integers x0,x1,…, xd so that
formula here
We address both density and Ramsey-type questions for affine d-cubes. Regarding density results, upper bounds are found for the size of the largest subset of {1,2,…,n} not containing an affine d-cube. In 1892 Hilbert published the first Ramsey-type result for affine d-cubes by showing that, for any positive integers r and d, there exists a least number n=h(d,r) so that, for any r-colouring of {1,2,…,n}, there is a monochromatic affine d-cube. Improvements for upper and lower bounds on h(d,r) are given for d>2.
Often when analysing randomized algorithms, especially parallel or distributed algorithms, one is called upon to show that some function of many independent choices is tightly concentrated about its expected value. For example, the algorithm might colour the vertices of a given graph with two colours and one would wish to show that, with high probability, very nearly half of all edges are monochromatic.
The classic result of Chernoff [3] gives such a large deviation result when the function is a sum of independent indicator random variables. The results of Hoeffding [5] and Azuma [2] give similar results for functions which can be expressed as martingales with a bounded difference property. Roughly speaking, this means that each individual choice has a bounded effect on the value of the function. McDiarmid [9] nicely summarized these results and gave a host of applications. Expressed a little differently, his main result is as follows.
Consider first-passage percolation on the square lattice. Welsh, who together with Hammersley introduced the subject in 1963, has formulated a problem about mean first-passage times, which, although seemingly simple, has not been proved in any non-trivial case. In this paper we give a general proof of Welsh's problem.
It is known that any k-uniform family with covering number t has at most ktt-covers. In this paper, we deal with intersecting families and give better upper bounds for the number of t-covers. Let pt(k) be the maximum number of t-covers in any k-uniform intersecting families with covering number t. We prove that, for a fixed t,
formula here
In the cases of t=4 and 5, we also prove that the coefficient of kt−1 in pt(k) is exactly (t2).
Let T be a semicomplete digraph on n vertices. Let ak(T) denote the minimum number of arcs whose addition to T results in a k-connected semicomplete digraph and let rk(T) denote the minimum number of arcs whose reversal in T results in a k-connected semicomplete digraph. We prove that if n[ges ]3k−1, then ak(T)=rk(T). We also show that this bound on n is best possible.
We study the number of comparisons in Hoare's Find algorithm. Using trivariate generating functions, we get an explicit expression for the variance of the number of comparisons, if we search for the jth element in a random permutation of n elements. The variance is also asymptotically evaluated under the assumption that j is proportional to n. Similar results for the number of passes (recursive calls) are given, too.
Let S be a generating subset of a cyclic group G such that 0=∉S and [mid ]S[mid ][ges ]5. We show that the number of sums of the subsets of S is at least min([mid ]G[mid ], 2[mid ]S[mid ]). Our bound is best possible. We obtain similar results for abelian groups and mention the generalization to nonabelian groups.
It is shown that every partially ordered set with n elements admits an endomorphism with an image of a size at least n1/7 but smaller than n. We also prove that there exists a partially ordered set with n elements such that each of its non-trivial endomorphisms has an image of size O((n log n)1/3).
Consider the complete n-graph with independent exponential (mean n) edge-weights. Let M(c, n) be the maximal size of subtree for which the average edge-weight is at most c. It is shown that M(c, n) makes the transition from o(n) to Ω(n) around some critical value c(0), which can be specified in terms of a fixed point of a mapping on probability distributions.