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LemmaStatement: AI-adaptedProof: AI-adaptedPipeline-generatedprecheck passjudge pass (gpt-5.6-terra)audited 2026-09-09
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Evaluation defines a bounded scalar-linear map into the bidual

Statement

Let X be a normed space over K∈{R,C}. Set X∗∗=(X∗)∗. Evaluation defines a bounded K-linear map JX:X⟶X∗∗,(JXx)(f)=f(x), with ∥JXx∥≤∥x∥ for every x∈X. This assertion uses ZF alone; it does not assert injectivity without HB.

Facts & Assumptions

[F1]

The dual is the space of bounded scalar-linear functionals with norm sup⁡∥x∥≤1∣f(x)∣ (The dual space X^* of a normed space and its dual norm).

[F2]

The operator norm is a norm on the vector space of bounded linear operators (The operator norm is a norm on the space of bounded linear operators).

Proof

Given: A normed K-space X; HB is not assumed.

1.1givenF1F2

By the operator-norm lemma, the space X∗ of bounded scalar-linear maps X→K is itself a normed vector space. Therefore its dual (X∗)∗ is defined; this is the meaning of X∗∗.

2.1step 1.1F1algebra

Fix x∈X. For a,b∈K and f,g∈X∗, evaluation gives (af+bg)(x)=af(x)+bg(x), so the map Ex:f↦f(x) is scalar-linear. If x≠0, ∣f(x)∣=∥x∥∣f(x/∥x∥)∣≤∥x∥∥f∥; if x=0, f(x)=0. Thus Ex is bounded and belongs to X∗∗.

3.1step 2.1algebra

Define JX(x)=Ex. For x,y∈X and a,b∈K, evaluating at every f∈X∗ gives JX(ax+by)(f)=f(ax+by)=aJX(x)(f)+bJX(y)(f). Equality at all arguments is equality of functions, hence JX is scalar-linear.

4.1step 2.1step 3.1F1∎

Taking the supremum of ∣Ex(f)∣≤∥x∥∥f∥ over ∥f∥≤1 yields ∥JXx∥≤∥x∥. This also proves boundedness of JX with constant one. At x=0, E0 is the zero functional and has norm zero. No step required HB or completeness.

Source notes

Brezis §1.3 first paragraph, pp.8–9 through the isometry formula; Teschl paragraph preceding Theorem 4.20 and its upper-bound proof, pp.115–116.

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Sources