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Quadratic Form of the Coordinate Hessian

Proved
FunctionalInequalities.Analysis.hessianMatrix_quadratic_readable

by Lazyfill · Oct 26, 2026 · Mathlib 0df444a (Lean v4.33.1)

functional-inequalitieskls-conjecturekls-modularkls-readablelog-concave

This is a coordinate-identification lemma for the Hessian matrix.

Let n∈Nn\in\mathbb Nn∈N, let ψ:EuclideanSpace⁡ R (Fin⁡(n))→R\psi:\operatorname{EuclideanSpace}\,\mathbb R\,(\operatorname{Fin}(n))\to\mathbb Rψ:EuclideanSpaceR(Fin(n))→R, let xxx be a point in that space, and let vvv be a vector. The quadratic form associated with hessianMatrix ψ x satisfies

⟨v,toEuclideanLin⁡(hessianMatrix⁡(ψ,x))v⟩=D2ψ(x)[v,v],\left\langle v,\operatorname{toEuclideanLin}(\operatorname{hessianMatrix}(\psi,x))v\right\rangle = D^2\psi(x)[v,v],⟨v,toEuclideanLin(hessianMatrix(ψ,x))v⟩=D2ψ(x)[v,v],

where the right-hand side is expressed in Lean as the iterated Fréchet derivative (fderiv ℝ (fderiv ℝ ψ) x) v v.

The lemma connects the coordinate Hessian representation used by the formalization with the second Fréchet derivative and is intended for later viscosity calculations.

Original Lean declaration header

Within namespace FunctionalInequalities.Analysis. Shown in its original module context. Proof omitted.

private lemma hessianMatrix_quadratic
    {n : ℕ} {ψ : EuclideanSpace ℝ (Fin n) → ℝ}
    {x : EuclideanSpace ℝ (Fin n)}
    (v : EuclideanSpace ℝ (Fin n)) :
    ⟪v, Matrix.toEuclideanLin (hessianMatrix ψ x) v⟫ =
      (fderiv ℝ (fderiv ℝ ψ) x) v v

Module navigation

Module: Analysis/Convex/Viscosity.

Definition modules: Analysis/BakryEmery/HessianOperator.

Previous: posSemidef_hessianMatrix_of_upper_touch · Next: local_taylor_two.

Source excerpt, lines 209–214 · Frozen source 408915f.

Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_Analysis_Convex_Viscosity

open scoped RealInnerProductSpace Topology
open FunctionalInequalities.Probability.Stein
Formal statement
 lemma FunctionalInequalities.Analysis.hessianMatrix_quadratic_readable
    {n : ℕ} {ψ : EuclideanSpace ℝ (Fin n) → ℝ}
    {x : EuclideanSpace ℝ (Fin n)}
    (v : EuclideanSpace ℝ (Fin n)) :
    ⟪v, Matrix.toEuclideanLin (hessianMatrix ψ x) v⟫ =
      (fderiv ℝ (fderiv ℝ ψ) x) v v := by sorry
Source
Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/Analysis/Convex/Viscosity.lean#L209-L259 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.Analysis.Convex.Viscosity`, declaration `_private.FunctionalInequalities.Analysis.Convex.Viscosity.0.FunctionalInequalities.Analysis.hessianMatrix_quadratic`; the repository is private at upload time).

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