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Functional analysis Definition

Banach–Mazur distance

Builds on Normed vector spaceLinear isomorphismOperator norm

Definition

Let n≥1n \ge 1, and let XX and YY be real normed vector spaces of the same dimension nn. The Banach–Mazur distance between XX and YY is d(X,Y)=inf⁡T∥T∥ ∥T−1∥.d(X, Y) = \inf_T \norm{T} \, \norm{T^{-1}}. Here TT runs over the linear isomorphisms from XX to YY, and ∥⋅∥\norm{\cdot} is the operator norm.

Remarks

Always d(X,Y)≥1d(X, Y) \ge 1, d(X,Y)=d(Y,X)d(X, Y) = d(Y, X) and d(X,Z)≤d(X,Y) d(Y,Z)d(X, Z) \le d(X, Y) \, d(Y, Z); so log⁡d\log d is a pseudometric. The infimum is attained by compactness, and d(X,Y)=1d(X, Y) = 1 if and only if XX and YY are isometric. In terms of the unit balls BXB_X and BYB_Y, d(X,Y)d(X, Y) is the least λ≥1\lambda \ge 1 with BX⊆TBY⊆λBXB_X \subseteq T B_Y \subseteq \lambda B_X for some linear isomorphism T ⁣:ℝn→ℝnT \colon \R^n \to \R^n, after identifying both spaces with ℝn\R^n. Example: let X=ℓ12X = \ell_1^2 and Y=ℓ∞2Y = \ell_\infty^2, that is, ℝ2\R^2 with the norms ∣x1∣+∣x2∣\abs{x_1} + \abs{x_2} and max⁡(∣x1∣,∣x2∣)\max(\abs{x_1}, \abs{x_2}). The map T(x1,x2)=(x1+x2,x1−x2)T(x_1, x_2) = (x_1 + x_2, x_1 - x_2) satisfies max⁡(∣x1+x2∣,∣x1−x2∣)=∣x1∣+∣x2∣\max(\abs{x_1 + x_2}, \abs{x_1 - x_2}) = \abs{x_1} + \abs{x_2}, so it is an isometry and d(X,Y)=1d(X, Y) = 1. Non-example: if dim⁡X≠dim⁡Y\dim X \ne \dim Y, there is no linear isomorphism, and the distance is not defined. John's theorem gives d(X,ℓ2n)≤nd(X, \ell_2^n) \le \sqrt{n} for every nn-dimensional normed space XX.

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  1. Linear isomorphism concept
  2. Normed vector space concept
  3. Operator norm concept
  4. Banach–Mazur distance definition

Further reading

  1. Web https://en.wikipedia.org/wiki/Banach%E2%80%93Mazur_compactum
About this page
TagVCBWR1WSK0
URIm2://VCBWR1WSK0
Kinddefinition
WritingW0 stub
MSC46B20, 52A21, 46B07
DefinesBanach–Mazur distance
UsesNormed vector space, Linear isomorphism, Operator norm

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