Alphabeta Math
LemmaStatement: AI-adaptedProof: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-07
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.

Finite truncations approximate null and summable sequences

Statement

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)).

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 sequence spaces c_0 and ell-infinity, with its stated hypotheses: Let K=R or C, with absolute value in the real case and modulus a+ib=a2+b2 in the complex case. A scalar sequence here is a function x:NK, including index zero. Let ={x=(xn)nN:supnNxn<},c0={x:xn0}, both equipped with x=supnNxn. Thus c0 is a specified linear subspace of the bounded-sequence space . Here xn0 means that for every real ε>0 there is NN such that xn<ε for all nN. Addition and scalar multiplication are coordinatewise. The scalar triangle inequality makes bounded sequences and null sequences linear spaces and gives the triangle inequality for the displayed supremum norm. Absolute homogeneity follows coordinatewise, and a zero supremum forces every coordinate to vanish.

[F2]

From p is the Lp space of counting measure, with its stated hypotheses: On (N,P(N),#) with counting measure, every function f:NR is measurable. Writing ak:=f(k), one has fpd#=k=0akp(0<p<), by the counting-measure integral dictionary, so Lp(#) is exactly the usual sequence class p. Also f=supkNak, because a subset of N has counting measure zero only when it is empty. Hence the quotient by almost-everywhere equality does nothing: for counting measure on N, equality almost everywhere means equality everywhere.

Proof

1.1

The real absolute-sum model agrees with the counting-measure dictionary at p=1. For either scalar field, an+bnan+bn, absolute homogeneity holds termwise, and a zero sum forces every coordinate to vanish; thus the stated model is a normed linear space.

F2given
2.1

For xc0, xPNx=supn>Nxn, which tends to zero by the definition of convergence to zero. For a1, the error is n>Nan, the tail of a convergent nonnegative series, so it also tends to zero. Both formulas hold at N=0 and for the zero sequence.

F1step 1.1
3.1

Each truncation has finite support, so these limits establish finite-support density in both norms. For a sequence already supported in {0,,N} the corresponding error is exactly zero.

step 2.1

Depends on

Used by

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Sources