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.
Finite-action continuity equations generate AC2 Wasserstein curves
Statement
Assume AC. Let and let be a narrowly continuous finite-action solution of the continuity equation on . Then belongs to and, for almost every ,
Statement references
Finite-action continuity equation in Euclidean space
Absolutely continuous curves of order two in Wasserstein space
The Euclidean quadratic Wasserstein space
Superposition for finite-action Euclidean continuity equations
Metric derivatives of AC2 Wasserstein curves
Minkowski's integral inequality
Borel probability law on a polish space
Change of variables for expectation
Ambrosio, Gigli, and Savaré, Gradient Flows, Theorem 8.3.1
Santambrogio, Optimal Transport for Applied Mathematicians, Chapter 5, §5.3
Facts & Assumptions
Given: AC and the narrowly continuous finite-action solution .
There is a probability law on whose time marginals are , whose almost every path is absolutely continuous with almost everywhere, and whose expected path action equals the Eulerian action. Superposition for finite-action Euclidean continuity equations
Minkowski's integral inequality bounds the norm of a time integral by the time integral of the norms. Minkowski's integral inequality
The metric derivative theorem identifies the least upper control of a Wasserstein curve with its metric derivative. Metric derivatives of AC2 Wasserstein curves
If is a Borel probability law on a Polish space and is Borel into a Polish space, define ; countable additivity and total mass one follow from preimages and the measure axioms, coordinate marginals compose with coordinate maps, and for nonnegative Borel h by change of variables for expectation. Borel probability law on a polish space Measures on sigma-algebras Change of variables for expectation
Proof
For , the evaluation map , , is continuous. Define for Borel ; by [F4] this is a Borel probability. Its coordinate marginals are and , so is a coupling. Its quadratic cost satisfies by [F4]. For -almost every path, absolute continuity gives . Minkowski [F2], applied with the path probability space and Lebesgue measure on , yields The marginal identity and the pathwise velocity identity in [F1] identify the inner integral for almost every r as . Put . Finite action gives , and the displayed bound for every interval implies W2-continuity and the defining upper-control inequality.
By [F3], the metric derivative exists almost everywhere and is no larger than any nonnegative upper control. Applying it to from step 1.1 gives almost everywhere, as claimed.
Depends on
- Finite-action continuity equation in Euclidean space
- Absolutely continuous curves of order two in Wasserstein space
- The Euclidean quadratic Wasserstein space
- Superposition for finite-action Euclidean continuity equations
- Metric derivatives of AC2 Wasserstein curves
- Minkowski's integral inequality
- The Axiom of Choice
- Borel probability law on a polish space
- Measures on sigma-algebras
- Change of variables for expectation
Used by
Dependency tree · two levels
61 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
- Luigi Ambrosio, Nicola Gigli, and Giuseppe Savaré, Gradient Flows in Metric Spaces and in the Space of Probability Measures, Theorem 8.3.1 (standard reference, not scraped)
- Filippo Santambrogio, Optimal Transport for Applied Mathematicians, Chapter 5, §5.3 (standard reference, not scraped)