Math 2.0

Convex and discrete geometry Definition

Convex independence

Builds on Vector spaceConvex hull

Definition

Let VV be a real vector space, let II be a set, and let (xi)i∈I(x_i)_{i \in I} be a family of points of VV. The family is convexly independent if, for every subset J⊆IJ \subseteq I and every i∈Ii \in I, xi∈conv⁡{xj:j∈J}  ⟹  i∈J,x_i \in \operatorname{conv}\set{x_j : j \in J} \implies i \in J, where conv⁡\operatorname{conv} denotes the convex hull. A subset S⊆VS \subseteq V is in convex position if the family (x)x∈S(x)_{x \in S} is convexly independent, that is, if no x∈Sx \in S lies in conv⁡(S∖{x})\operatorname{conv}(S \setminus \set{x}).

Remarks

Example: the points (0,0)(0,0), (1,0)(1,0), (0,1)(0,1), (1,1)(1,1) of ℝ2\R^2 are in convex position. Each of them is a vertex of the unit square, and no vertex is a convex combination of the other three. For instance, the other three points satisfy x+y≤1x + y \le 1, so all their convex combinations do, while (1,1)(1,1) does not. The other vertices are handled by symmetry.

Non-example: add the point (12,12)(\frac12, \frac12). It equals 12(1,0)+12(0,1)\frac12 (1,0) + \frac12 (0,1), so it lies in the convex hull of the other points.

A convexly independent family is injective, because xi=xjx_i = x_j with i≠ji \neq j puts xix_i in the hull of {xj}\set{x_j}. A finite set SS is in convex position if and only if every point of SS is an extreme point of conv⁡(S)\operatorname{conv}(S), that is, a vertex of the polytope conv⁡(S)\operatorname{conv}(S). Affinely independent families are convexly independent, but not conversely: the four corners of a square are affinely dependent.

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  1. Convex hull concept
  2. Vector space concept
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Further reading

  1. Web https://en.wikipedia.org/wiki/Convex_position
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TagPZ8KRWR8RT
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Kinddefinition
WritingW0 stub
MSC52A20, 52C35
DefinesConvex independence
UsesVector space, Convex hull

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