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TheoremStatement: Literature-sourcedProof: AI-generatedprecheck passjudge pass (gpt-5.6-terra)audited 2026-09-04
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The based Kuratowski distance map is an isometric embedding

Statement

Let (M,d) be a nonempty metric space, fix oM, and let Ko:MCb(M) be the based Kuratowski distance map of The based Kuratowski distance map into bounded continuous functions. Then Ko is an isometric embedding in the sense of Isometry, isometric embedding, and the subspace metric on a subset:

Ko(x)Ko(y)=d(x,y)(x,yM).

Facts & Assumptions

Given: A nonempty metric space (M,d), a basepoint oM, and the map Ko:MCb(M).

[L1]

For each xM, Ko(x)(z)=d(x,z)d(o,z) defines a bounded continuous function on M (The based Kuratowski distance map into bounded continuous functions).

[L2]

An isometric embedding preserves all distances (Isometry, isometric embedding, and the subspace metric on a subset).

Proof

technique · direct
1.1

By [L1], the map Ko is well defined as a map into Cb(M). For any x,y,zM, the triangle inequality gives d(x,z)d(x,y)+d(y,z)andd(y,z)d(y,x)+d(x,z), so Ko(x)(z)Ko(y)(z)=d(x,z)d(y,z)d(x,y). Taking the supremum over z yields Ko(x)Ko(y)d(x,y).

L1algebra
2.1

Evaluating at z=y gives Ko(x)(y)Ko(y)(y)=d(x,y)0=d(x,y), and evaluating at z=x gives the same value. Therefore the supremum norm is at least d(x,y).

step 1.1algebra
3.1

Steps 1.1 and 2.1 give Ko(x)Ko(y)=d(x,y) for all x,yM, which is exactly [L2]. Hence Ko is an isometric embedding.

L2step 1.1step 2.1

Remarks

  • The proof is two lines long once the target is chosen correctly. The real work is the based definition, which keeps the functions bounded on unbounded metric spaces.

Depends on

Used by

Dependency tree · two levels

7 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources