How statement and proof provenance work
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Exact quadratic feature-energy flow and particle limit
Example
Assume AC. In dimension , take , , , , , and for every . This cutoff family has the required relative-neighborhood property, and , so works. Thus . For any , the unique Wasserstein gradient flow is . The corresponding -particle gradient flow satisfies under the inner product . For every empirical probability and every , so any initial convergence in persists uniformly on every compact time interval.
Example references
Feature-measure energy and its regularity assumptions
The global Wasserstein gradient flow exists uniquely and is stable
Finite-particle flows converge to the measure-valued flow in W2
The Euclidean quadratic Wasserstein space
The quadratic Wasserstein distance is a metric
Facts & Assumptions
Given: AC, , , and an empirical initial probability .
Expanding the defining inequality for gives , and the first-order directional limit forces every subgradient to equal . Thus the minimum-norm subgradient is . Feature-measure energy and its regularity assumptions
The quadratic Wasserstein distance is the square root of the infimum of the squared displacement cost over couplings. The Euclidean quadratic Wasserstein space
AC is assumed in the flow and transport results. The Axiom of Choice
Finite-action continuity-equation solutions are represented by absolutely continuous characteristic paths. Superposition for finite-action Euclidean continuity equations
Proof
Given: The data in the Example.
The characteristic equation with has the solution , as direct differentiation verifies. Pushing forward by this map gives a continuity-equation solution with velocity . Its action on is finite because . For compactly supported initial laws it is the flow identified by the global uniqueness theorem. For arbitrary , the same conclusion follows directly: every finite-action PDE solution with the prescribed smooth velocity is represented by its unique characteristics via superposition, so it equals this pushforward, even when lies in no finite cutoff.
The metric gives the particle gradient of equal to , so the same scalar ODE gives . Consequently .
For , pushing both marginals of any coupling forward by multiplies its quadratic cost by ; applying the inverse scaling gives the reverse inequality for the infimum. Thus . Apply this with , , and to obtain the displayed identity. Since , its supremum for is at most the initial W2 error, proving compact-time uniform convergence.
Depends on
- The global Wasserstein gradient flow exists uniquely and is stable
- Finite-particle flows converge to the measure-valued flow in W2
- Feature-measure energy and its regularity assumptions
- The Euclidean quadratic Wasserstein space
- The quadratic Wasserstein distance is a metric
- The Axiom of Choice
- Superposition for finite-action Euclidean continuity equations
Used by
Nothing in the library uses this result yet.
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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)
- Ambrosio, Gigli, and Giuseppe Savaré, Gradient Flows in Metric Spaces and in the Space of Probability Measures (standard reference, not scraped)