Math 2.0

Convex and discrete geometry Definition

Convex polytope

Builds on Convex hullEuclidean space

Definition

Let n≥1n \ge 1. A convex polytope in ℝn\R^n is a set of the form P=conv{v1,…,vm}={λ1v1+⋯+λmvm:λi≥0, λ1+⋯+λm=1},P = \mathrm{conv}\set{v_1, \ldots, v_m} = \set{\lambda_1 v_1 + \cdots + \lambda_m v_m : \lambda_i \ge 0, \ \lambda_1 + \cdots + \lambda_m = 1}, the convex hull of finitely many points v1,…,vmv_1, \ldots, v_m of the Euclidean space ℝn\R^n. The dimension of PP is the dimension of its affine hull.

Remarks

Example: the square [0,1]2[0, 1]^2 is conv{(0,0),(1,0),(0,1),(1,1)}\mathrm{conv}\set{(0, 0), (1, 0), (0, 1), (1, 1)}, since every point (x,y)(x, y) of the square is (1−x)(1−y)(0,0)+x(1−y)(1,0)+(1−x)y(0,1)+xy(1,1)(1 - x)(1 - y) (0, 0) + x (1 - y) (1, 0) + (1 - x) y (0, 1) + x y (1, 1), with nonnegative weights of sum 11. Non-example: the closed unit disc is compact and convex but not a convex polytope. The convex hull of finitely many points has only finitely many extreme points, while every point of the unit circle is an extreme point of the disc. Equivalently, a convex polytope is a bounded set of the form {x∈ℝn:Ax≤b}\set{x \in \R^n : A x \le b} for a real m×nm \times n matrix AA and b∈ℝmb \in \R^m; this equivalence is the Minkowski–Weyl theorem. Many authors allow the empty set as a convex polytope, the hull of no points.

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  1. Convex hull concept
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Further reading

  1. Web https://en.wikipedia.org/wiki/Convex_polytope
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DefinesConvex polytope
UsesConvex hull, Euclidean space

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