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Proposition 7.d — the primitive φ is Fréchet differentiable with gradient prox_g z

Proved
MoreauProx.Characterization.primitive_hasGradientAt

by mikedeng1 · Sep 27, 2026 · Mathlib 0df444a (Lean v4.33.1)

convex-analysisp2o-batch-p100bp2o-gran-per-chapterp2o-plan-paperp2o-v1proximal-map

Let HHH be a real Hilbert space, f∈Γ0(H)f \in \Gamma_0(H)f∈Γ0​(H) and ggg its dual function, and let φ(z)=12∥prox⁡gz∥2+f(prox⁡fz)\varphi(z) = \tfrac12 \|\operatorname{prox}_g z\|^2 + f(\operatorname{prox}_f z)φ(z)=21​∥proxg​z∥2+f(proxf​z) be the primitive of prox⁡g\operatorname{prox}_gproxg​. At every point z∈Hz \in Hz∈H, φ\varphiφ is Fréchet differentiable and its gradient is

∇φ(z)=prox⁡gz.\nabla \varphi(z) = \operatorname{prox}_g z .∇φ(z)=proxg​z.

This justifies the name "primitive": prox⁡g\operatorname{prox}_gproxg​ is the gradient of the convex function φ\varphiφ. It is the key input for the necessity half of Corollary 10.c.

Formalization Note The gradient is Mathlib's HasGradientAt, which is Fréchet differentiability with derivative h↦(prox⁡gz∣h)h \mapsto (\operatorname{prox}_g z \mid h)h↦(proxg​z∣h). The gradient is prox⁡gz\operatorname{prox}_g zproxg​z, not prox⁡fz\operatorname{prox}_f zproxf​z.

Preamble
import Mathlib
import Definitions.Def_MoreauProx_Characterization_Prox
open scoped InnerProductSpace
Formal statement
namespace MoreauProx.Characterization

theorem primitive_hasGradientAt {H : Type*} [NormedAddCommGroup H] [InnerProductSpace ℝ H] [CompleteSpace H]
    (f g : H → EReal) (hf : GammaZero f) (hg : g = conj f) (z : H) :
    HasGradientAt (primitive f g) (prox g z) z := by sorry

end MoreauProx.Characterization
Source
Moreau, Proximité et dualité dans un espace hilbertien, Bull. Soc. Math. France 93 (1965), p. 286, Proposition 7.d
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What the Lean code literally says, in plain math · claude-opus-5-5

Let HHH be a real Hilbert space. Let f∈Γ0(H)f\in\Gamma_0(H)f∈Γ0​(H): fff never takes the value −∞-\infty−∞, is finite somewhere, has a convex real epigraph, and is lower semicontinuous. Let g=f∗g=f^*g=f∗, where g(y)=sup⁡x(⟨x,y⟩−f(x))g(y)=\sup_x(\langle x,y\rangle-f(x))g(y)=supx​(⟨x,y⟩−f(x)) in [−∞,+∞][-\infty,+\infty][−∞,+∞].

Define the real-valued function

Φ(z)=12∥prox⁡g(z)∥2+re⁡(f(prox⁡f(z))),\Phi(z)=\tfrac12\|\operatorname{prox}_g(z)\|^2+\operatorname{re}\big(f(\operatorname{prox}_f(z))\big),Φ(z)=21​∥proxg​(z)∥2+re(f(proxf​(z))),

where prox⁡h(z)\operatorname{prox}_h(z)proxh​(z) is a chosen minimizer of u↦12∥u−z∥2+h(u)u\mapsto\frac12\|u-z\|^2+h(u)u↦21​∥u−z∥2+h(u) (or 000 if none exists), and re⁡(±∞)=0\operatorname{re}(\pm\infty)=0re(±∞)=0.

For every z∈Hz\in Hz∈H, the statement asserts that Φ\PhiΦ is Fréchet differentiable at zzz with gradient prox⁡g(z)\operatorname{prox}_g(z)proxg​(z):

Φ(z+h)=Φ(z)+⟨prox⁡g(z),h⟩+o(∥h∥)as h→0.\Phi(z+h)=\Phi(z)+\langle\operatorname{prox}_g(z),h\rangle+o(\|h\|)\quad\text{as }h\to0.Φ(z+h)=Φ(z)+⟨proxg​(z),h⟩+o(∥h∥)as h→0.

Degenerate case. If H={0}H=\{0\}H={0}, the claim is trivially true with gradient 000.

Human review
  • Endorsed by Shuze Chen · Sep 27, 2026

    Confirmed by the moderator at approval.

  • Endorsed by mikedeng1 · Sep 27, 2026

    Confirmed by the mission captain (proposal self-audit).

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