Alphabeta Math
DefinitionDefinition: AI-adaptedProof: Not applicablePipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-14
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.

Weak and strong topologies on tempered distributions

Definition

A set B⊆S(Rn) is bounded when sup⁡φ∈Bpαβ(φ)<∞ for every pair of multi-indices. The weak topology σ(S′,S) on tempered distributions is generated by

pφ(u)=∣⟨u,φ⟩∣(φ∈S).

The strong topology β(S′,S) is generated by

pB(u)=sup⁡φ∈B∣⟨u,φ⟩∣

as B ranges over bounded subsets of S. The empty-set supremum is zero. This supremum is finite: continuity of u gives a basic zero-neighborhood U on which ∣u∣<1, and the finitely many seminorm bounds defining U imply B⊆tU for some finite t>0; hence pB(u)≤t. The triangle inequality and homogeneity follow pointwise.

Thus a net (ui) converges weakly to u exactly when ⟨ui−u,φ⟩→0 for every φ∈S, and it converges strongly exactly when pB(ui−u)→0 for every bounded B. Singletons are bounded, so strong convergence implies weak convergence. Both topologies are Hausdorff, since distinct functionals differ on some test. No identification with either topology on D′ is asserted, and no choice axiom is used.

Depends on

Dependency tree · 2 levels

Sources