关于形状的内外同时逼近

SCG '90 Pub Date : 1990-05-01 DOI:10.1145/98524.98572
R. Fleischer, K. Mehlhorn, G. Rote, E. Welzl, C. Yap
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引用次数: 33

摘要

对于紧致欧几里得体P、Q,我们定义λ(P, Q)为最小比值r/s,其中r > 0、s > 0满足sQ’的规模P,规模rQ″。这里sQ表示因子s对Q的缩放,Q ', Q″是Q的一些平移。这个函数λ给了我们一个新的物体之间的距离函数,与之前研究的测量不同,它在仿射变换下是不变的。如果同质体被识别,这个函数的对数就是一个度规。(如果一个物体可以通过缩放和平移得到另一个物体,则两个物体是同质的)。对于整数&kgr;≥3,定义λ(&kgr;)为最小值,使得对于每一个凸多边形P存在一个λ(P, Q)≤λ(&kgr;)的凸&kgr;-gon Q。其中,我们证明了2.118…≤λ(3)≤2.25且λ(&kgr;) = 1 + &THgr;(&kgr;-2)。我们给出了一个&Ogr;(n2 log2 n)时间算法,对于任何输入的凸n-gon P,找到一个三角形T,使三角形中的λ(T, P)最小。但在线性时间内,我们可以找到λ(t, P)≤2.25的三角形t。我们的研究的动机是试图降低多边形包含问题的复杂性,以及运动规划问题。在每种情况下,我们描述了当输入的某些隐式松弛参数与1有界时将运行得更快的算法。这些算法说明了计算几何中处理复杂性的一种新的算法范式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On simultaneous inner and outer approximation of shapes
For compact Euclidean bodies P, Q, we define λ(P, Q) to be smallest ratio r/s where r > 0, s > 0 satisfy sQ′PrQ″. Here sQ denotes a scaling of Q by factor s, and Q′, Q″ are some translates of Q. This function λ gives us a new distance function between bodies which, unlike previously studied measures, is invariant under affine transformations. If homothetic bodies are identified, the logarithm of this function is a metric. (Two bodies are homothetic if one can be obtained from the other by scaling and translation). For integer &kgr; ≥ 3, define λ(&kgr;) to be the minimum value such that for each convex polygon P there exists a convex &kgr;-gon Q with λ(P, Q) ≤ λ(&kgr;). Among other results, we prove that 2.118… ≤ λ(3) ≤ 2.25 and λ(&kgr;) = 1 + &THgr;(&kgr;-2). We give an &Ogr;(n2 log2 n) time algorithm which for any input convex n-gon P, finds a triangle T that minimizes λ(T, P) among triangles. But in linear time, we can find a triangle t with λ(t, P) ≤ 2.25. Our study is motivated by the attempt to reduce the complexity of the polygon containment problem, and also the motion planning problem. In each case, we describe algorithms which will run faster when certain implicit slackness parameters of the input are bounded away from 1. These algorithms illustrate a new algorithmic paradigm in computational geometry for coping with complexity.
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