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CounterexampleConstruction: AI-adaptedVerification: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-07
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The transpose of an injective map need not have norm-dense range

Statement refuted

An injective bounded operator need not have norm-dense transpose range. Over R, take the inclusion T:1c0. Its transpose is the inclusion T:1, and ranT=c0=(kerT). The operator T is injective and has dense range.

Facts & Assumptions

Given: 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.

[F1]

From The transpose of a bounded operator, with its stated hypotheses: Let K=R or C. Let T:XY be bounded and linear between normed spaces. Its transpose, or Banach adjoint, is T:YX,(Tg)(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 The continuous dual of c0 is ell-one, with its stated hypotheses: Let K=R or C. With coordinates starting at zero, the map 1(K)c0(K),afa,fa(x)=n=0anxn is a linear isometric bijection. The pairing is bilinear, including over C.

[F3]

From Counting measure specializes the representation theorem to p and q, with its stated hypotheses: Let 1p< and let q be conjugate to p. Every bounded linear functional Λ:pR is of the form Λ(a)=n=0anbn for a unique sequence bq. Moreover, Λ=bq.

[F4]

From Finite truncations approximate null and summable sequences, with its stated hypotheses: Let K=R or C. Use coordinates indexed by N={0,1,}. Define 1(K)={a:n=0an<}, with coordinatewise operations and norm a1=nan. Let PN retain coordinates 0,,N and set all others to zero. Then xPNx0(xc0(K)),aPNa10(a1(K)).

[F5]

From c_0 is a closed subspace of ell-infinity, with its stated hypotheses: c0 is a closed linear subspace of in the sup norm.

[F6]

From Annihilator notation and the preannihilator, with its stated hypotheses: Let K=R or C. For a normed X and arbitrary subsets MX, NX, define M={fX:f(m)=0 for all mM},N={xX:f(x)=0 for all fN}. Here X is def-dual-space-of-a-normed-space. The first notation agrees with def-continuous-annihilator-of-a-subspace on spanM, 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.

Counterexample

1.1

Absolute summability implies convergence to zero: infinitely many coordinates of magnitude at least ε>0 would make partial absolute sums unbounded. Also xx1, so inclusion is bounded and injective. Finite-support sequences lie in its range and are dense in c0 by truncation.

F4
1.2

Identify c0 with 1 and (1) with real . For a1 and x1, (Tfa)(x)=fa(Tx)=nanxn, so the transpose corresponds to the same coefficient sequence a, now viewed in .

F1F2F3
2.1

The transpose image is contained in c0, which is closed in , so its norm closure is contained in c0. Conversely it contains all finite-support sequences, whose sup-norm closure contains c0 by truncation. Hence that closure is exactly c0.

F4F5step 1.1step 1.2
3.1

The constant-one sequence has distance exactly one from c0: for zn0, 1zn1, so 1z1, and z=0 attains one. It is therefore outside the closure. Since kerT=0, its annihilator is all of (1)=, proving strict inclusion.

F6step 1.1step 2.1

Depends on

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