Banach–Mazur compactum

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Short description: Set of n-dimensional subspaces of a normed space made into a compact metric space.

In the mathematical study of functional analysis, the Banach–Mazur distance is a way to define a distance on the set [math]\displaystyle{ Q(n) }[/math] of [math]\displaystyle{ n }[/math]-dimensional normed spaces. With this distance, the set of isometry classes of [math]\displaystyle{ n }[/math]-dimensional normed spaces becomes a compact metric space, called the Banach–Mazur compactum.

Definitions

If [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] are two finite-dimensional normed spaces with the same dimension, let [math]\displaystyle{ \operatorname{GL}(X, Y) }[/math] denote the collection of all linear isomorphisms [math]\displaystyle{ T : X \to Y. }[/math] Denote by [math]\displaystyle{ \|T\| }[/math] the operator norm of such a linear map — the maximum factor by which it "lengthens" vectors. The Banach–Mazur distance between [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] is defined by [math]\displaystyle{ \delta(X, Y) = \log \Bigl( \inf \left\{ \left\|T\right\| \left\|T^{-1}\right\| : T \in \operatorname{GL}(X, Y) \right\} \Bigr). }[/math]

We have [math]\displaystyle{ \delta(X, Y) = 0 }[/math] if and only if the spaces [math]\displaystyle{ X }[/math] and [math]\displaystyle{ Y }[/math] are isometrically isomorphic. Equipped with the metric δ, the space of isometry classes of [math]\displaystyle{ n }[/math]-dimensional normed spaces becomes a compact metric space, called the Banach–Mazur compactum.

Many authors prefer to work with the multiplicative Banach–Mazur distance [math]\displaystyle{ d(X, Y) := \mathrm{e}^{\delta(X, Y)} = \inf \left\{ \left\|T\right\| \left\|T^{-1}\right\| : T \in \operatorname{GL}(X, Y) \right\}, }[/math] for which [math]\displaystyle{ d(X, Z) \leq d(X, Y) \, d(Y, Z) }[/math] and [math]\displaystyle{ d(X, X) = 1. }[/math]

Properties

F. John's theorem on the maximal ellipsoid contained in a convex body gives the estimate:

[math]\displaystyle{ d(X, \ell_n^2) \le \sqrt{n}, \, }[/math] [1]

where [math]\displaystyle{ \ell_n^2 }[/math] denotes [math]\displaystyle{ \R^n }[/math] with the Euclidean norm (see the article on [math]\displaystyle{ L^p }[/math] spaces). From this it follows that [math]\displaystyle{ d(X, Y) \leq n }[/math] for all [math]\displaystyle{ X, Y \in Q(n). }[/math] However, for the classical spaces, this upper bound for the diameter of [math]\displaystyle{ Q(n) }[/math] is far from being approached. For example, the distance between [math]\displaystyle{ \ell_n^1 }[/math] and [math]\displaystyle{ \ell_n^{\infty} }[/math] is (only) of order [math]\displaystyle{ n^{1/2} }[/math] (up to a multiplicative constant independent from the dimension [math]\displaystyle{ n }[/math]).

A major achievement in the direction of estimating the diameter of [math]\displaystyle{ Q(n) }[/math] is due to E. Gluskin, who proved in 1981 that the (multiplicative) diameter of the Banach–Mazur compactum is bounded below by [math]\displaystyle{ c\,n, }[/math] for some universal [math]\displaystyle{ c \gt 0. }[/math]

Gluskin's method introduces a class of random symmetric polytopes [math]\displaystyle{ P(\omega) }[/math] in [math]\displaystyle{ \R^n, }[/math] and the normed spaces [math]\displaystyle{ X(\omega) }[/math] having [math]\displaystyle{ P(\omega) }[/math] as unit ball (the vector space is [math]\displaystyle{ \R^n }[/math] and the norm is the gauge of [math]\displaystyle{ P(\omega) }[/math]). The proof consists in showing that the required estimate is true with large probability for two independent copies of the normed space [math]\displaystyle{ X(\omega). }[/math]

[math]\displaystyle{ Q(2) }[/math] is an absolute extensor.[2] On the other hand, [math]\displaystyle{ Q(2) }[/math]is not homeomorphic to a Hilbert cube.

See also

Notes

References