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A weak two-homogeneous projection limit is stationary on its support
Statement
Assume AC, , the feature-energy hypotheses, and positive two-homogeneity of and on . Let be a Wasserstein gradient flow with finite initial energy. Suppose the finite measures converge weakly on to a finite nonnegative measure . Define , where . Then for -almost every .
Statement references
Feature-measure energy and its regularity assumptions
Wasserstein gradient flow for a feature-measure energy
Superposition for finite-action Euclidean continuity equations
Bounded linear maps commute with Bochner integration
Fundamental theorem of calculus for absolutely continuous functions
AC implies DC implies countable choice
Fubini's theorem for L^1 functions on a sigma-finite product
Two-homogeneous measure projection
Feature and energy identities for homogeneous projections
Ambrosio, Gigli, and Savaré, Theorem 11.2.1 and energy identity
Facts & Assumptions
Given: AC, the model and flow in the Statement, and weak convergence of their spherical projections.
Assume the Axiom of Choice throughout, as required in the Statement and by the path and characteristic suppliers. The Axiom of Choice
The flow velocity is a Borel version of the negative minimum-norm subgradient selection almost everywhere; the characteristic construction supplies its canonical pointwise Borel version. Wasserstein gradient flow for a feature-measure energy One-sided semiconvex characteristics give W2 uniqueness and stability
Finite-action continuity-equation solutions have a superposition path law with the prescribed marginals and velocity; the energy identity for an arbitrary finite-energy flow is derived in step 1.1. Superposition for finite-action Euclidean continuity equations Wasserstein gradient flow for a feature-measure energy
For positive two-homogeneous , their integrals factor through ; weak convergence of on the compact sphere gives norm convergence of the feature moments. Feature and energy identities for homogeneous projections
The full flow potential is two-homogeneous and semiconvex, so its supporting sets and their minimum-norm selections scale by under for . Feature-measure energy and its regularity assumptions Wasserstein gradient flow for a feature-measure energy
A W2-continuous path of laws has weakly continuous two-homogeneous projections. W2 convergence implies weak convergence of homogeneous projections
Scalar Fubini applies to the time/path integrands with finite absolute integral; bounded linear functionals commute with the Bochner integral, so their scalar identities determine Hilbert-valued integrals. Fubini's theorem for L^1 functions on a sigma-finite product Bounded linear maps commute with Bochner integration
Proof
Given: The assumptions and suppliers in [F1]–[F6].
Fix . W2 continuity bounds . Positive two-homogeneity and continuity give a constant such that , , and ; for the derivative bound, the neighborhood condition puts the unit sphere in some cutoff where is Lipschitz and bounded; differentiating homogeneity away from then gives . Thus is bounded, and is bounded on . By [F2], choose a path law with marginals and almost everywhere. Pathwise FTC for , Cauchy--Schwarz, and scalar/Bochner Fubini [F6] give since . Hence is absolutely continuous. For almost every , write the selected subgradient as with ; subtracting the first-order expansion of the smooth term in the defining subdifferential inequality gives this decomposition. The flow definition gives . Along almost every path, is absolutely continuous because is locally Lipschitz. At times when , are differentiable and the supporting inequality holds, testing that inequality at for both signs of gives . These derivatives are integrable: , so . Taking expectations and using the marginals shows that is absolutely continuous with . The chain rule for now yields almost everywhere. Absolute integrability and the scalar FTC give the all-endpoint energy identity. Since , it follows that .
Fix and . Write . If , the local subdifferential definition applied along gives Since , the one-sided limits for and are respectively and its negative. Both must be nonnegative, so . By [F1], is the negative of one such vector, giving .
For , substitute in the local subdifferential definition of . Two-homogeneity gives , while . Thus iff ; the reverse implication follows by scaling by . Hence the minimum-norm selection scales by , and wherever the canonical pointwise selection is defined.
Apply [F3] to the weakly convergent projections. Then in Hilbert norm. The differentials converge in operator norm to by local Lipschitz continuity; since and are bounded on the sphere, converges uniformly to . In particular, weak convergence and boundedness of the masses give
By [F5] and the uniform convergence in step 3.1, is measurable. By [F3] and [F4], positive one-homogeneity of the velocity gives . Step 1.2 on the unit sphere gives , so . Integrating and using step 1.1 yields . A nonnegative function with a finite integral cannot converge to a positive limit; hence . Since is nonnegative, its zero integral implies -almost everywhere.
Source note
Ambrosio--Gigli--Savaré Theorem 11.2.1 assumes a proper, lower-semicontinuous, coercive energy that is convex along generalized geodesics. The local feature-energy assumptions here do not assert those global hypotheses; the needed energy identity is derived directly in step 1.1.
Depends on
- Feature-measure energy and its regularity assumptions
- Wasserstein gradient flow for a feature-measure energy
- Two-homogeneous measure projection
- Feature and energy identities for homogeneous projections
- W2 convergence implies weak convergence of homogeneous projections
- One-sided semiconvex characteristics give W2 uniqueness and stability
- The Axiom of Choice
- AC implies DC implies countable choice
- Superposition for finite-action Euclidean continuity equations
- Bounded linear maps commute with Bochner integration
- Fundamental theorem of calculus for absolutely continuous functions
- Fubini's theorem for L^1 functions on a sigma-finite product
Used by
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Sources
- Lénaïc Chizat and Francis Bach, On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport (standard reference, not scraped)
- Luigi Ambrosio, Nicola Gigli, and Giuseppe Savaré, Gradient Flows in Metric Spaces and in the Space of Probability Measures (standard reference, not scraped)