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These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Finite truncations approximate null and summable sequences
Statement
Let or . Use coordinates indexed by . Define , with coordinatewise operations and norm . Let retain coordinates and set all others to zero. Then
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.
From The sequence spaces c_0 and ell-infinity, with its stated hypotheses: Let or , with absolute value in the real case and modulus in the complex case. A scalar sequence here is a function , including index zero. Let both equipped with . Thus is a specified linear subspace of the bounded-sequence space . Here means that for every real there is such that for all . 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.
From is the space of counting measure, with its stated hypotheses: On with counting measure, every function is measurable. Writing , one has by the counting-measure integral dictionary, so is exactly the usual sequence class . Also because a subset of has counting measure zero only when it is empty. Hence the quotient by almost-everywhere equality does nothing: for counting measure on , equality almost everywhere means equality everywhere.
Proof
The real absolute-sum model agrees with the counting-measure dictionary at . For either scalar field, , absolute homogeneity holds termwise, and a zero sum forces every coordinate to vanish; thus the stated model is a normed linear space.
For , , which tends to zero by the definition of convergence to zero. For , the error is , the tail of a convergent nonnegative series, so it also tends to zero. Both formulas hold at and for the zero sequence.
Each truncation has finite support, so these limits establish finite-support density in both norms. For a sequence already supported in the corresponding error is exactly zero.
Depends on
Used by
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
- Bühler–Salamon, Functional Analysis, Examples 1.35–1.36, pp.36–37 (standard reference, not scraped)