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On the smoothed complexity of convex hulls

Olivier Devillers 1 Marc Glisse 2 Xavier Goaoc 3 Rémy Thomasse 2 
1 VEGAS - Effective Geometric Algorithms for Surfaces and Visibility
Inria Nancy - Grand Est, LORIA - ALGO - Department of Algorithms, Computation, Image and Geometry
2 GEOMETRICA - Geometric computing
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Saclay - Ile de France
Abstract : We establish an upper bound on the smoothed complexity of convex hulls in $\mathbb{R}^d$ under uniform Euclidean ($\ell^2$) noise. Specifically, let $\{p_1^*, p_2^*, \ldots, p_n^*\}$ be an arbitrary set of $n$ points in the unit ball in $\mathbb{R}^d$ and let $p_i=p_i^*+x_i$, where $x_1, x_2, \ldots, x_n$ are chosen independently from the unit ball of radius $\delta$. We show that the expected complexity, measured as the number of faces of all dimensions, of the convex hull of $\{p_1,p_2, \ldots, p_n\}$ is $O\left(n^{2-\frac{4}{d+1}}\left(1+1/\delta\right)^{d-1}\right)$; the magnitude $\delta$ of the noise may vary with $n$. For $d=2$ this bound improves to $O\left(n^{\frac{2}{3}}(1+\delta^{-\frac{2}{3}}\right)$. We also analyze the expected complexity of the convex hull of $\ell^2$ and Gaussian perturbations of a nice sample of a sphere, giving a lower-bound for the smoothed complexity. We identify the different regimes in terms of the scale, as a function of $n$, and show that as the magnitude of the noise increases, that complexity varies monotonically for Gaussian noise but non-monotonically for $\ell^2$ noise.
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Olivier Devillers, Marc Glisse, Xavier Goaoc, Rémy Thomasse. On the smoothed complexity of convex hulls. Proceedings of the 31st International Symposium on Computational Geometry, Jun 2015, Eindhoven, Netherlands. pp.224-238, ⟨10.4230/LIPIcs.SOCG.2015.224⟩. ⟨hal-01144473v2⟩



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