Quantifying minimal noncollinearity among random points
I. Pinelis Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, USA
Abstract:
Let
$\varphi_ {n, K} $ denote the largest angle in all the triangles with vertices among the
$ n $ points selected at random in a compact convex subset
$ K $ of
$\mathbb {R}^ d $ with nonempty interior, where
$ d\ge2 $. It is shown that the distribution of the random variable $\lambda_d (K)\,\frac {n^ 3}{3!}\,(\pi-\varphi_ {n, K})^{d-1} $, where
$\lambda_d (K) $ is a certain positive real number which depends only on the dimension
$d$ and the shape of
$K$, converges to the standard exponential distribution as
$n\to\infty$. By using the Steiner symmetrization, it is also shown that
$\lambda_d (K)$, which is referred to in the paper as the elongation of
$K$, attains its minimum if and only if
$K$ is a ball
$B^{(d)}$ in
$\mathbf {R}^d$. Finally, the asymptotics of
$\lambda_d(B^{(d)})$ for large
$d$ is determined.
Keywords:
convex sets, random points, geometric probability theory, integral geometry, maximal angle, convergence in distribution, Steiner symmetrization, asymptotic approximation. Received: 28.05.2017
Language: English
DOI:
10.4213/tvp5145