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Negative regular sublevels force two-homogeneous projection escape
Statement
Assume AC, the feature-energy hypotheses on , , convex differentiable , and positive two-homogeneity of . Let be a finite nonnegative measure on which is not a global minimizer of over finite nonnegative measures and satisfies -almost everywhere. Set and use the bounded-Lipschitz norm on finite measures on the sphere. Assume the dense-regular-value hypothesis for , including differentiability of near each selected regular level. Then there are and a compact sublevel set with nonempty interior with the following property. If a Wasserstein gradient flow satisfies and , then for some ,
The resulting escape time is finite and depends on the positive mass and its initial radial lower bound; no uniform escape time over all initial laws is asserted.
Statement references
Feature-measure energy and its regularity assumptions
Wasserstein gradient flow for a feature-measure energy
Two-homogeneous measure projection
First variation and optimality over nonnegative measures
A weak two-homogeneous projection limit is stationary on its support
One-sided semiconvex characteristics give W2 uniqueness and stability
Facts & Assumptions
Given: The feature-energy flow, the candidate projected measure, and the regularity hypotheses in the Statement.
The feature moment and regularizer integrals factor through , and weak convergence on the sphere controls the Hilbert-valued moment in norm. Feature and energy identities for homogeneous projections
For convex , global optimality over nonnegative measures is equivalent to nonnegativity of the first-variation potential everywhere and its vanishing almost everywhere for the candidate measure. First variation and optimality over nonnegative measures
Flow laws are represented by the unique characteristics, and their velocities are the negative minimum-norm subgradient selections. Wasserstein gradient flow for a feature-measure energy One-sided semiconvex characteristics give W2 uniqueness and stability
On a bounded set of spherical projected measures, is Lipschitz on the corresponding bounded set of feature moments; the risk component of is on the sphere. Feature-measure energy and its regularity assumptions
For and a two-homogeneous semiconvex potential, any selected supporting vector satisfies ; hence the radial velocity of a characteristic is . Feature-measure energy and its regularity assumptions
AC is inherited by the Wasserstein flow, characteristic representation, and minimum-norm selection. The Axiom of Choice
Proof
Given: AC, , , and [F1]–[F5].
Since -almost everywhere but is not a minimizer, [F2] gives a point with . Choose so that is a regular value; if on the whole sphere, take and omit its boundary. Otherwise compactness and regularity make compact with nonempty interior and give a positive lower bound for on its boundary. With the outward normal, .
For projected measures in a sufficiently small neighborhood of , [F1] and duality give , where . The Lipschitz bound in [F4] then yields for a finite . Choose small enough that, whenever the projection remains in that ball, on and the outward normal velocity is at most on .
While the projection remains in the chosen ball, a characteristic with direction in cannot leave . At its boundary the potential is differentiable, its angular velocity is , and the strict negative outward normal component from step 2.1 rules out a first outward crossing: at a first exit the signed distance to would have nonnegative right derivative, contradicting the strictly negative derivative. For in this cone, [F5] gives , so .
The initial cone has positive mass and is preserved by step 3.1. Since it is the increasing union of subsets with , one such subset has mass for some . For every particle in that subset, its radius grows at least as until the projection exits the ball. Therefore throughout that interval.
The constant test function has bounded-Lipschitz norm . Step 4.1 makes grow without bound while stays fixed and finite. Hence exceeds after finite time if the flow stayed in the ball. It must exit by that time, proving the assertion.
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
- First variation and optimality over nonnegative measures
- A weak two-homogeneous projection limit is stationary on its support
- One-sided semiconvex characteristics give W2 uniqueness and stability
- The global Wasserstein gradient flow exists uniquely and is stable
- The Axiom of Choice
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)