Alphabeta Math
DefinitionDefinition: AI-adaptedProof: Not applicablePipeline-generated
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.

Feature-measure energy and its regularity assumptions

Definition

Let d≥1, let H be a separable real Hilbert space, and let Ω⊆Rd be the closure of a nonempty convex open set. Let R:H→[0,∞) be differentiable, with differential Lipschitz on bounded sets in the uniform sense that for every bounded B⊆H there is a finite LB such that ∥dRz−dRz′∥H∗≤LB∥z−z′∥H(z,z′∈B), and with differential bounded on every sublevel set of R: for each c≥0, sup⁡{∥dRz∥H∗:z∈H, R(z)≤c}<∞. In particular, on each closed convex hull of a bounded feature image Φ(Qr), both a finite Lipschitz constant Lr and a finite norm bound Nr for dR are available. Let Φ:Ω→H be Fréchet differentiable and let V:Ω→[0,∞) be λV-semiconvex, with the convention that V(u)−λV2∣u∣2 is convex on Ω. For a semiconvex function f on a convex domain D with coefficient λ, define ∂λf(u)={ξ∈Rd:f(v)≥f(u)+ξ⋅(v−u)+λ2∣v−u∣2 for every v∈D}. For a locally semiconvex function g on D, define its local Fréchet subdifferential by ∂g(u)={ξ∈Rd:lim inf⁡v→u, v∈Dv≠ug(v)−g(u)−ξ⋅(v−u)∣v−u∣≥0}. When g is globally λ-semiconvex, this local subdifferential agrees with the convexified supporting set ∂λg(u). For every r>0, require a nonempty closed convex set Qr⊆Ω, nested increasingly in r, such that {u∈Ω:dist⁡(u,Qr)≤r′}⊆Qr+r′(r,r′>0), such that Φ and V are bounded on Qr, dΦ is Lipschitz on Qr, and there are fixed constants C1,C2>0 with sup⁡u∈Qr(∥dΦu∥+∥∂V(u)∥)≤C1+C2r. Here ∂V(u)=∂λVV(u) is the supporting set on Ω, and in the growth bound ∥∂V(u)∥ means its minimum element norm, equivalently the bound requires a supporting vector of norm at most C1+C2r at every u∈Qr. 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 A⊆Ω, write P2(A) for the Borel probability measures on Rd concentrated on A and having finite second moment. For a finite nonnegative Borel measure μ on Rd, set F(μ)=R ⁣(∫ΩΦ(u) dμ(u))+∫ΩV(u) dμ(u) when μ is concentrated on Ω, the Bochner integral exists, and the scalar integral is finite; set F(μ)=+∞ otherwise. For μ∈P2(Ω) with finite F(μ), write mμ=∫ΩΦ dμ and define its first-variation potential on Ω by F′(μ)(u)=dRmμ[Φ(u)]+V(u). No convexity of R is required by this definition.

Depends on

Used by

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