English

Quantifying minimal non-collinearity among random points

Probability 2016-08-29 v3 Combinatorics Metric Geometry

Abstract

Let φn,K\varphi_{n,K} denote the largest angle in all the triangles with vertices among the nn points selected at random in a compact convex subset KK of Rd\mathbb{R}^d with nonempty interior, where d2d\ge2. It is shown that the distribution of the random variable λd(K)n33!(πφn,K)d1\lambda_d(K)\,\frac{n^3}{3!}\,(\pi-\varphi_{n,K})^{d-1}, where λd(K)\lambda_d(K) is a certain positive real number which depends only on the dimension dd and the shape of KK, converges to the standard exponential distribution as nn\to\infty. By using the Steiner symmetrization, it is also shown that λd(K)\lambda_d(K) -- which is referred to in the paper as the elongation of KK -- attains its minimum if and only if KK is a ball B(d)B^{(d)} in Rd\mathbb{R}^d. Finally, the asymptotics of λd(B(d))\lambda_d(B^{(d)}) for large dd is determined.

Keywords

Cite

@article{arxiv.1608.04455,
  title  = {Quantifying minimal non-collinearity among random points},
  author = {Iosif Pinelis},
  journal= {arXiv preprint arXiv:1608.04455},
  year   = {2016}
}

Comments

7 pages. Version 2: minor mistakes and typos are fixed. Version 3: it is also shown that the elongation attains its minimum only on balls, and the large-dimension asymptotics of the elongation for balls is determined

R2 v1 2026-06-22T15:20:32.676Z