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Feature-measure energy and its regularity assumptions
Definition
Let , let be a separable real Hilbert space, and let be the closure of a nonempty convex open set. Let be differentiable, with differential Lipschitz on bounded sets in the uniform sense that for every bounded there is a finite such that and with differential bounded on every sublevel set of : for each , In particular, on each closed convex hull of a bounded feature image , both a finite Lipschitz constant and a finite norm bound for are available. Let be Fréchet differentiable and let be -semiconvex, with the convention that is convex on . For a semiconvex function on a convex domain with coefficient , define For a locally semiconvex function on , define its local Fréchet subdifferential by When is globally -semiconvex, this local subdifferential agrees with the convexified supporting set . For every , require a nonempty closed convex set , nested increasingly in , such that such that and are bounded on , is Lipschitz on , and there are fixed constants with Here is the supporting set on , and in the growth bound means its minimum element norm, equivalently the bound requires a supporting vector of norm at most at every . No bound is imposed on the domain normal cone. This is a bounded-selection variant of the source assumptions: Assumption 2.1(iii)(c) in Chizat and Bach explicitly uses the maximal element norm, which implies the minimum-norm bound wherever the supporting set is nonempty. The source does not specify that its maximal bound excludes domain normals. The bounded-selection convention here allows relative-domain supporting sets with unbounded normal vectors; results using this variant require their own proofs. For a Borel set , write for the Borel probability measures on concentrated on and having finite second moment. For a finite nonnegative Borel measure on , set when is concentrated on , the Bochner integral exists, and the scalar integral is finite; set otherwise. For with finite , write and define its first-variation potential on by No convexity of is required by this definition.
Depends on
Used by
- Localized Hilbert lift of the feature energy Definition
- One-hidden-layer particle-measure model Definition
- Partially one-homogeneous signed measure projection Definition
- Relaxed objective for a partially one-homogeneous projection Definition
- Signed-square ReLU feature parameterization Definition
- Two-homogeneous measure projection Definition
- Wasserstein gradient flow for a feature-measure energy Definition
- Exact quadratic feature-energy flow and particle limit Example
- Mixture convexity does not imply displacement convexity Example
- Moving features can represent a target missed by a fixed feature Example
- Separated and nonseparated initial supports Example
- A homogeneous flow preserves spherical support separation Lemma
- A partial signed projection limit has convergent penalty and energy Lemma
- A weak two-homogeneous projection limit is stationary on its support Lemma
- Feature and energy identities for homogeneous projections Lemma
- Feature energy is W2-continuous on a common cutoff Lemma
- First variation and optimality over nonnegative measures Lemma
- Negative regular sublevels force two-homogeneous projection escape Lemma
- Nonoptimal partial projections have an escaping sign branch Lemma
- One-sided semiconvex characteristics give W2 uniqueness and stability Lemma
- Partial feature flows have exact energy and weight-moment identities Lemma
- Partial one-homogeneous energy is continuous under W2 convergence Lemma
- Partial-homogeneity flows preserve box separation Lemma
- Signwise first-variation conditions minimize the relaxed objective Lemma
- Stationarity of a partially homogeneous signed projection limit Lemma
- The full global partial-model hypotheses force a constant penalty coefficient Lemma
- The Hilbert lift induces the localized Wasserstein continuity equation Lemma
- The localized feature energy is semiconvex along generalized geodesics Lemma
- The localized flow cannot escape all cutoffs in finite time Lemma
- The localized lifted feature energy is semiconvex Lemma
- The localized lifted subdifferential is a pointwise constrained subdifferential Lemma
- The selected pointwise field is a strong Wasserstein subgradient Lemma
- A partially homogeneous signed projection limit is a minimizer Theorem
- A separating two-homogeneous flow has globally minimizing projection limits Theorem
- Finite-particle flows converge to the measure-valued flow in W2 Theorem
- Particle number and training time limits interchange qualitatively Theorem
- The global Wasserstein gradient flow exists uniquely and is stable Theorem
- The localized Wasserstein gradient flow exists, satisfies EVI, and is unique Theorem
Dependency tree · two levels
14 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
- 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)