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Gruber P. Convex and Discrete Geometry

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250 <strong>Convex</strong> Polytopes<br />

Generalized <strong>Convex</strong> Polyhedra<br />

For the geometric theory of positive definite quadratic forms, see Sect. 29.4, for<br />

Dirichlet-Voronoĭ tilings, see Sect. 32.1, <strong>and</strong> in other contexts, a more general<br />

notion of convex polyhedron is needed: call a closed convex set in E d a generalized<br />

convex polyhedron if its intersection with any convex polytope is a convex polytope.<br />

A generalized convex polyhedron looks locally like a convex polyhedron, but may<br />

have countably many different faces. An example of a generalized convex polygon<br />

is the set<br />

conv � (u,v)∈ Z 2 : v ≥ u 2� ⊆ E 2 .<br />

Normal Cones of Polytopes <strong>and</strong> Polyhedra<br />

Let P be a convex polyhedron <strong>and</strong> p a boundary point of P.Thenormal cone NP(p)<br />

of P at p is the closed convex cone of all exterior normal vectors of support hyperplanes<br />

of P at p, that is,<br />

NP(p) = � u : u · x ≤ u · p for all x ∈ P � .<br />

For later reference we need the following result, where the ai are the row vectors of<br />

the n × d matrix A, theβi the components of the vector b ∈ E n , <strong>and</strong> the inequality<br />

Ax ≤ b is to be understood componentwise. We consider the row vectors ai as<br />

vectors in E d <strong>and</strong> thus write ai · u instead of a T i · u for the inner product. Using the<br />

matrix product <strong>and</strong> considering ai as a row vector, we also write the inner product in<br />

the form aiu.<br />

Proposition 14.1. Let P ={x : Ax ≤ b} be a convex polyhedron <strong>and</strong> p ∈ bd P.<br />

Assume that ai p = βi precisely for i = 1,...,k. Then<br />

(4) NP(p) = pos � �<br />

a1,...,ak .<br />

If p is a vertex of P, then<br />

(5) dim NP(p) = d.<br />

Proof. Since NP(p) depends on P only locally <strong>and</strong> is translation invariant, we may<br />

assume that p = o. Then<br />

NP(p) = NC(o), where C is the closed convex cone {x : Bx ≤ o}<br />

<strong>and</strong> B is the k × d matrix consisting of the rows a1,...,ak. Since each of the hyperplanes<br />

{x : ai · x = ai x = 0} supports C at o, it follows that NC(o) ⊇{a1,...,ak}<br />

<strong>and</strong> thus<br />

NC(o) ⊇ pos � �<br />

a1,...,ak .<br />

To show that equality holds, assume that, on the contrary, there is c ∈ NC(o) \<br />

pos{a1,...,ak}. Choose a hyperplane {x : u · x = α} which strictly separates c <strong>and</strong><br />

the closed convex cone pos{a1,...,ak}, sayu · c >α>u · x for each x of the

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