Alphabeta Math
LemmaStatement: AI-adaptedProof: AI-adaptedPipeline-generatedverified 2026-09-24 (gpt-6-sol)
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Elementary kernel and range annihilator identities

Statement

Assume the Axiom of Choice. Let K=R or C. For a bounded linear T:X→Y between normed spaces, (ran⁡T)⊥=ker⁡T∗,⊥(ran⁡T∗)=ker⁡T,ran⁡T‾∥⋅∥=⊥(ker⁡T∗). The closure in the last identity is in Y.

Facts & Assumptions

Given: The Axiom of Choice, The spaces, maps, scalar field, and hypotheses in the statement above. All duals consist of linear functionals over the ambient field; evaluation has no conjugation.

[A1]

AC is used through norming separation [F4] in step 1.2 and annihilator closure [F3] in step 2.1.

[F1]

From The transpose of a bounded operator, with its stated hypotheses: Let K=R or C. Let T:X→Y be bounded and linear between normed spaces. Its transpose, or Banach adjoint, is T∗:Y∗⟶X∗,(T∗g)(x)=g(Tx). The duals are def-dual-space-of-a-normed-space. Composition is bounded by lem-composition-operator-norm-inequality, so this has the displayed codomain. It is linear in g over K. No complex conjugation is inserted; a Hilbert adjoint uses a separate inner-product identification.

[F2]

From Annihilator notation and the preannihilator, with its stated hypotheses: Let K=R or C. For a normed X and arbitrary subsets M⊆X, N⊆X∗, define M⊥={f∈X∗:f(m)=0 for all m∈M},⊥N={x∈X:f(x)=0 for all f∈N}. Here X∗ is def-dual-space-of-a-normed-space. The first notation agrees with def-continuous-annihilator-of-a-subspace on span⁡M, since linearity makes vanishing on M equivalent to vanishing on its span. The preannihilator lies in X, not in X∗∗. Empty sets impose no conditions: ∅⊥=X∗ and ⊥∅=X.

[F3]

From The annihilator detects the closure of a subspace, with its stated hypotheses: For every linear subspace M⊆X, M‾=⋂f∈M⊥ker⁡f.

[F4]

From Every nonzero vector has a norming functional, with its stated hypotheses: Let X be a normed space over R or C, and let x∈X be nonzero. Then there exists f∈X∗ such that ∥f∥=1andf(x)=∥x∥.

Proof

1.1F1F2

A functional g∈Y∗ vanishes on ran⁡T exactly when g(Tx)=0 for every x, exactly when T∗g=0.

1.2F1F2F4

A vector x belongs to ⊥(ran⁡T∗) exactly when g(Tx)=0 for every g∈Y∗. This holds if Tx=0; if Tx≠0, a norming functional has g(Tx)=∥Tx∥>0, so it fails.

2.1F3F4step 1.1step 1.2∎

Apply the primal annihilator-closure identity to the linear subspace ran⁡T≤Y and substitute step 1.1. If T=0, the three identities read Y∗=Y∗, X=X, and {0}=⊥Y∗; the last equality follows from the same norming separation.

Depends on

Used by

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Sources