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Mollified Log-Determinant Lower Bound

Proved
FunctionalInequalities.Analysis.posDef_and_convolution_le_log_det_hessianMatrix_readable

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

functional-inequalitieskls-conjecturekls-modularkls-readablelog-concave

This is the mollified log-det lower bound from the weak Letwin route.

Let u,F:Rn→Ru,F:\mathbb R^n\to\mathbb Ru,F:Rn→R, with uuu convex and FFF continuous. Assume that, almost everywhere in xxx, there exist a Hermitian positive-definite matrix BBB and a vector ggg such that

  1. F(x)≤log⁡det⁡BF(x)\leq\log\det BF(x)≤logdetB;
  2. for every ε>0\varepsilon>0ε>0, uuu has at xxx the quadratic expansion represented by quadraticAt B x g (u x), with error bounded by ε∥y−x∥2\varepsilon\|y-x\|^2ε∥y−x∥2 near xxx.

For every C2C^2C2 compactly supported bump ϕ\phiϕ normalized with respect to volume, and every x∈Rnx\in\mathbb R^nx∈Rn, the convolution uϕ=ϕ.normed∗uu_\phi=\phi.\mathrm{normed}*uuϕ​=ϕ.normed∗u has positive-definite Hessian and

(ϕ.normed∗F)(x)≤log⁡det⁡∇2uϕ(x).(\phi.\mathrm{normed}*F)(x) \leq \log\det\nabla^2u_\phi(x).(ϕ.normed∗F)(x)≤logdet∇2uϕ​(x).

This is the formalized mollified determinant inequality and can be reused as the regularized convexity input in moment-map arguments.

Original Lean declaration header

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

theorem posDef_and_convolution_le_log_det_hessianMatrix {n : ℕ}
    {u F : EuclideanSpace ℝ (Fin n) → ℝ} (hconv : ConvexOn ℝ univ u) (hF : Continuous F)
    (hae : ∀ᵐ x ∂(volume : Measure (EuclideanSpace ℝ (Fin n))),
      ∃ B : Matrix (Fin n) (Fin n) ℝ, B.IsHermitian ∧ B.PosDef ∧ F x ≤ Real.log B.det ∧
        ∃ g : EuclideanSpace ℝ (Fin n),
          ∀ ε > 0, ∀ᶠ y in 𝓝 x, |u y - quadraticAt B x g (u x) y| ≤ ε * ‖y - x‖ ^ 2)
    (φ : ContDiffBump (0 : EuclideanSpace ℝ (Fin n))) (x : EuclideanSpace ℝ (Fin n)) :
    (hessianMatrix (convolution (φ.normed volume) u (ContinuousLinearMap.lsmul ℝ ℝ) volume) x).PosDef ∧
      convolution (φ.normed volume) F (ContinuousLinearMap.lsmul ℝ ℝ) volume x ≤
        Real.log (hessianMatrix
          (convolution (φ.normed volume) u (ContinuousLinearMap.lsmul ℝ ℝ) volume) x).det

Module navigation

Module: Analysis/Convex/Viscosity.

Definition modules: Analysis/BakryEmery/HessianOperator, Analysis/Convex/Alexandrov.

Supporting theorems: forall_posDef_add_log_det_le_trace_iff, posSemidef_hessianMatrix_of_upper_touch, convolution_hessian_integral, trace_integral_bound.

Previous: trace_integral_bound · Next: ae_exists_quadraticAt_expansion.

Source excerpt, lines 1327–1337 · Frozen source 408915f.

Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_Analysis_Convex_Viscosity

open FunctionalInequalities.Probability.Stein
open MeasureTheory Set Metric Filter
open scoped RealInnerProductSpace ENNReal Topology
Formal statement

theorem FunctionalInequalities.Analysis.posDef_and_convolution_le_log_det_hessianMatrix_readable {n : ℕ}
    {u F : EuclideanSpace ℝ (Fin n) → ℝ} (hconv : ConvexOn ℝ univ u) (hF : Continuous F)
    (hae : ∀ᵐ x ∂(volume : Measure (EuclideanSpace ℝ (Fin n))),
      ∃ B : Matrix (Fin n) (Fin n) ℝ, B.IsHermitian ∧ B.PosDef ∧ F x ≤ Real.log B.det ∧
        ∃ g : EuclideanSpace ℝ (Fin n),
          ∀ ε > 0, ∀ᶠ y in 𝓝 x, |u y - quadraticAt B x g (u x) y| ≤ ε * ‖y - x‖ ^ 2)
    (φ : ContDiffBump (0 : EuclideanSpace ℝ (Fin n))) (x : EuclideanSpace ℝ (Fin n)) :
    (hessianMatrix (convolution (φ.normed volume) u (ContinuousLinearMap.lsmul ℝ ℝ) volume) x).PosDef ∧
      convolution (φ.normed volume) F (ContinuousLinearMap.lsmul ℝ ℝ) volume x ≤
        Real.log (hessianMatrix
          (convolution (φ.normed volume) u (ContinuousLinearMap.lsmul ℝ ℝ) volume) x).det := by sorry
Source
Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/Analysis/Convex/Viscosity.lean#L1322-L1393 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.Analysis.Convex.Viscosity`, declaration `FunctionalInequalities.Analysis.posDef_and_convolution_le_log_det_hessianMatrix`; the repository is private at upload time).

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