Alphabeta Math
ExampleConstruction: AI-adaptedVerification: AI-adaptedPipeline-generatedverified 2026-09-26 (gpt-6-sol)
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Distance to a subspace via annihilating functionals

Example

Assume the Axiom of Choice. Let X be a real or complex normed space. For a subspace M⊆X and x∈X,

dist⁡(x,M‾)=sup⁡{∣f(x)∣:f∈M⊥, ∥f∥≤1}.

Facts & Assumptions

Given: The Axiom of Choice (The Axiom of Choice), a real or complex normed space X, a subspace M⊆X, and x∈X.

[F1]

Under AC, a real linear functional dominated by a sublinear functional extends with the same domination (Hahn-Banach dominated extension theorem for real vector spaces).

[F2]

M⊥ consists of the continuous linear functionals vanishing on M (Continuous annihilator of a linear subspace).

Verification

technique · direct
1.1givenalgebra

For f∈M⊥ with ∥f∥≤1, continuity gives f∣M‾=0. Hence, for every m∈M‾, ∣f(x)∣=∣f(x−m)∣≤∥x−m∥. Taking infima gives the ≤ direction.

2.1F1givenconstructalgebra∎

Put δ=dist⁡(x,M‾). If δ>0, define g:M‾+Kx→K by g(m+λx)=λδ. This is well defined, and ∣g(m+λx)∣=∣λ∣δ≤∥m+λx∥, so ∥g∥≤1. In the real case apply [F1] with p(y)=∥y∥; domination at y and −y gives an extension f with ∥f∥≤1. In the complex case apply [F1] to Re⁡g on the underlying real subspace, obtaining a real linear U with ∣U(y)∣≤∥y∥. Then f(y)=U(y)−iU(iy) is complex linear and extends g. For each y, choose a scalar a of modulus one with af(y)=∣f(y)∣; thus ∣f(y)∣=U(ay)≤∥y∥. This is the exact use of AC in both scalar cases; this extension lies in M⊥ and satisfies ∣f(x)∣=δ. If δ=0, then x∈M‾, so continuity makes every annihilator vanish at x. Thus equality holds in both cases.

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

9 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