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.
The real square-summable sequence space is complete
Statement
The real sequence space with its canonical inner product and norm from Square-summable families on an arbitrary index set and the space , is complete and therefore is a real Hilbert space.
Facts & Assumptions
Given: A Cauchy sequence in the canonical norm on .
Square-summable families on an arbitrary index set and the space : The norm obeys , where the nonnegative sum is the supremum of its finite subsums; the coordinate vectors are well-defined and the space has the canonical inner product.
The reals are complete: Every Cauchy sequence of reals converges to a real number.
Hilbert space: A complete inner-product space is a Hilbert space.
Proof
For each fixed , , so is Cauchy in . By [F2] let ; each coordinate limit is unique, so these values define a sequence . To see is bounded, choose so that for all ; then for , and the finitely many earlier norms have a finite maximum. Thus for some finite . For any finite , . Taking the supremum over finite gives , hence .
Let . Choose such that for all . Fix . For every finite , coordinate convergence and continuity of finite sums give . Taking the supremum over finite yields . Thus in . Every Cauchy sequence converges, proving completeness and, by [F3], the Hilbert-space claim.
Depends on
Used by
- Chebyshev weighted spectral Sobolev scale and projection Definition
- Legendre weighted spectral-Sobolev scale and projection Definition
- A compact diagonal model displays the Tikhonov bias-noise balance Example
- Landweber semiconvergence for diagonal noisy data Example
- Underestimated noise can put the Morozov target below every residual Example
- Tikhonov source-condition rates balance noise at qualification one Theorem
Dependency tree · two levels
37 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
- Gerald Teschl, Topics in Real and Functional Analysis, version November 17, 2017 (standard reference, not scraped)