Convex body

Non-empty convex set in Euclidean space
A dodecahedron is a convex body.

In mathematics, a convex body in n {\displaystyle n} -dimensional Euclidean space R n {\displaystyle \mathbb {R} ^{n}} is a compact convex set with non-empty interior. Some authors do not require a non-empty interior, merely that the set is non-empty.

A convex body K {\displaystyle K} is called symmetric if it is centrally symmetric with respect to the origin; that is to say, a point x {\displaystyle x} lies in K {\displaystyle K} if and only if its antipode, x {\displaystyle -x} also lies in K . {\displaystyle K.} Symmetric convex bodies are in a one-to-one correspondence with the unit balls of norms on R n . {\displaystyle \mathbb {R} ^{n}.}

Important examples of convex bodies are the Euclidean ball, the hypercube and the cross-polytope.

Metric space structure

Write K n {\displaystyle {\mathcal {K}}^{n}} for the set of convex bodies in R n {\displaystyle \mathbb {R} ^{n}} . Then K n {\displaystyle {\mathcal {K}}^{n}} is a complete metric space with metric

d ( K , L ) := inf { ϵ 0 : K L + B n ( ϵ ) , L K + B n ( ϵ ) } {\displaystyle d(K,L):=\inf\{\epsilon \geq 0:K\subset L+B^{n}(\epsilon ),L\subset K+B^{n}(\epsilon )\}} .[1]

Further, the Blaschke Selection Theorem says that every d-bounded sequence in K n {\displaystyle {\mathcal {K}}^{n}} has a convergent subsequence.[1]

Polar body

If K {\displaystyle K} is a bounded convex body containing the origin O {\displaystyle O} in its interior, the polar body K {\displaystyle K^{*}} is { u : u , v 1 , v K } {\displaystyle \{u:\langle u,v\rangle \leq 1,\forall v\in K\}} . The polar body has several nice properties including ( K ) = K {\displaystyle (K^{*})^{*}=K} , K {\displaystyle K^{*}} is bounded, and if K 1 K 2 {\displaystyle K_{1}\subset K_{2}} then K 2 K 1 {\displaystyle K_{2}^{*}\subset K_{1}^{*}} . The polar body is a type of duality relation.

See also

References

  1. ^ a b Hug, Daniel; Weil, Wolfgang (2020). "Lectures on Convex Geometry". Graduate Texts in Mathematics. doi:10.1007/978-3-030-50180-8. ISSN 0072-5285.
  • Hiriart-Urruty, Jean-Baptiste; Lemaréchal, Claude (2001). Fundamentals of Convex Analysis. doi:10.1007/978-3-642-56468-0. ISBN 978-3-540-42205-1.
  • Rockafellar, R. Tyrrell (12 January 1997). Convex Analysis. Princeton University Press. ISBN 978-0-691-01586-6.
  • Arya, Sunil; Mount, David M. (2023). "Optimal Volume-Sensitive Bounds for Polytope Approximation". 39th International Symposium on Computational Geometry (SoCG 2023). 258: 9:1–9:16. doi:10.4230/LIPIcs.SoCG.2023.9.
  • Gardner, Richard J. (2002). "The Brunn-Minkowski inequality". Bull. Amer. Math. Soc. (N.S.). 39 (3): 355–405 (electronic). doi:10.1090/S0273-0979-02-00941-2.
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