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DefinitionDefinition: AI-adaptedProof: Not applicablePipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-13
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.

Tensor product of distributions

Definition

For u∈D′(U), v∈D′(V), where U⊆Rp and V⊆Rq are open, define the tensor-product candidate on Φ∈D(U×V) by ⟨u⊗v,Φ⟩=⟨u, x↦⟨v,Φ(x,⋅)⟩⟩. The pairings are complex-bilinear as in Distribution. To check that the outer pairing is meaningful, let KU,KV be the compact coordinate projections of supp⁡Φ. Every inner test is supported in KV, so Distribution pairing with smooth parameter families makes its pairing a smooth function of x. That function is zero off KU, since those slices are zero, so it is a test in D(U).

The candidate is complex-linear in Φ and separately linear in u,v by linearity of each pairing. For Φ(x,y)=φ(x)ψ(y) its value is u(φ)v(ψ). If either distribution is zero, the value is zero; if either open domain is empty, the test space on the product is zero. This construction uses no choice axiom. Continuity of the candidate on the product test space, uniqueness from product tests, and equality with the reversed iterated pairing are proved in the tensor-product theorem below; they are not inferred from the notation.

Depends on

Dependency tree · 2 levels

Sources