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.
Distributional derivative
Definition
For a distribution in the convention of Distribution and a multi-index , its distributional derivative is The test derivative is a continuous linear endomorphism by Test function operations are continuous. Its composition with , multiplied by the indicated sign, is therefore a continuous complex-linear functional, so this definition produces a distribution. The pairing is bilinear, with no complex conjugation. For the operation is the identity; for one coordinate derivative its sign is minus. Every derivative of the zero distribution is zero, including on the empty domain. No choice axiom enters this construction. Compatibility with classical derivatives requires an integration-by-parts argument where it is used; it is not part of the definition.
Depends on
Used by
- Dirac delta and its derivatives Definition
- Derivative of the heaviside function is dirac delta Example
- Derivatives of piecewise smooth functions include jump deltas Example
- Distributional laplacian of the newtonian kernel Example
- Sobolev weak derivatives belong to pde Remark
- A distribution with zero derivatives on a connected open set is constant Theorem
- Associativity of distribution convolution under compact support Theorem
- Convolution with a test function is smooth Theorem
- Distributional differentiation is continuous and commutes Theorem
- Leibniz rule for distributions Theorem
- Local structure of distributions as derivatives of continuous functions Theorem
- Tensor product distributions and iterated pairings Theorem
Dependency tree · two levels
7 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
- Semyon Dyatlov, Lecture notes for 18.155 (2022) (standard reference, not scraped)