Math 2.0

Convex and discrete geometry Definition

Voronoi diagram

Builds on Euclidean spaceEuclidean distance

Definition

Let n≥1n \ge 1 and let S={p1,…,pk}S = \set{p_1, \dots, p_k} be a finite set of k≥1k \ge 1 distinct points of Euclidean space ℝn\R^n, called sites. Let ∥x−y∥\norm{x - y} denote the Euclidean distance. The Voronoi cell of the site pip_i is V(pi)={x∈ℝn:∥x−pi∥≤∥x−pj∥ for all j=1,…,k}.V(p_i) = \set{x \in \R^n : \norm{x - p_i} \le \norm{x - p_j} \text{ for all } j = 1, \dots, k}. The Voronoi diagram of SS is the family of cells V(p1),…,V(pk)V(p_1), \dots, V(p_k).

Remarks

Example: for the two sites p1=(−1,0)p_1 = (-1, 0) and p2=(1,0)p_2 = (1, 0) in ℝ2\R^2, the condition ∥x−p1∥≤∥x−p2∥\norm{x - p_1} \le \norm{x - p_2} reduces to x1≤0x_1 \le 0. So V(p1)V(p_1) is the closed half-plane x1≤0x_1 \le 0, V(p2)V(p_2) is the half-plane x1≥0x_1 \ge 0, and they meet along the perpendicular bisector x1=0x_1 = 0.

Non-example: the cells {x:x1≤−1}\set{x : x_1 \le -1} and {x:x1≥−1}\set{x : x_1 \ge -1} are not the Voronoi diagram of these two sites, since the point (−1/2,0)(-1/2, 0) is closer to p1p_1 but lies in the second set only.

Each condition ∥x−pi∥≤∥x−pj∥\norm{x - p_i} \le \norm{x - p_j} defines a closed half-space, so each cell is a convex polyhedron. The cells cover ℝn\R^n, and two distinct cells have disjoint interiors, so the Voronoi diagram is a tiling of ℝn\R^n. The Voronoi cell of a point of a lattice, with all lattice points as sites, is a Voronoi cell in the sense of the geometry of numbers.

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  1. Euclidean distance concept
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Further reading

  1. Web https://en.wikipedia.org/wiki/Voronoi_diagram
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Kinddefinition
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MSC52C99, 68U05
DefinesVoronoi diagram
UsesEuclidean space, Euclidean distance

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