Centered Difference Bound for Moment-Map Potentials
ProvedFunctionalInequalities.Analysis.secondDifference_le_of_map_gradient_readableThis formalizes the centered-difference estimate associated with regularity of the moment map, cited in Lemma 2.2 of Letwin (arXiv:2607.24164v1) (Klartag [41]).
Let , let , and assume:
- is -Lipschitz and convex;
- ;
- is compact and convex;
- is and is convex for some ;
- the pushforward of the tilted volume measure with density under equals the measure with density on ;
- is .
Then, for every ,
This exports the exact coefficient used in the subsequent Lipschitz-gradient regularity argument.
Original Lean declaration header
Within namespace FunctionalInequalities.Analysis. Shown in its original module context. Proof omitted.
theorem secondDifference_le_of_map_gradient {n : ℕ} {u V : EuclideanSpace ℝ (Fin n) → ℝ}
{L : NNReal} (hu : LipschitzWith L u) (hconv : ConvexOn ℝ univ u)
(hnorm : ∫ x, Real.exp (-u x) ∂(volume : Measure (EuclideanSpace ℝ (Fin n))) = 1)
{K : Set (EuclideanSpace ℝ (Fin n))} (hK : IsCompact K) (hKc : Convex ℝ K)
(hV : ContDiff ℝ 1 V) {κ : ℝ} (hκ : 0 < κ)
(hstrong : ConvexOn ℝ univ (fun y => V y - (κ / 2) * ‖y‖ ^ 2))
(hmap : ((volume : Measure (EuclideanSpace ℝ (Fin n))).tilted (-u)).map (gradient u) =
(volume : Measure (EuclideanSpace ℝ (Fin n))).withDensity
(K.indicator fun p => ENNReal.ofReal (Real.exp (-V p))))
(hC1 : ContDiff ℝ 1 u) :
∀ x h : EuclideanSpace ℝ (Fin n),
u (x + h) + u (x - h) - 2 * u x ≤ (4 / κ) * ‖h‖ ^ 2
Module navigation
Module: Analysis/Convex/GradientLipschitz.
Selected supporting theorems: ae_exists_quadraticAt_expansion, exists_forall_le_and_isCompact_sublevel_of_convexOn_integrable_exp_neg, exists_penalized_maximum_with_comparison, posDef_and_convolution_le_log_det_hessianMatrix, posDef_and_det_eq_of_quadratic_expansion.
Source excerpt, lines 89–100 · Frozen source 408915f.
import Definitions.Def_KLSModular_FunctionalInequalities_Analysis_Convex_GradientLipschitz open MeasureTheory Set Metric Filter
theorem FunctionalInequalities.Analysis.secondDifference_le_of_map_gradient_readable {n : ℕ} {u V : EuclideanSpace ℝ (Fin n) → ℝ}
{L : NNReal} (hu : LipschitzWith L u) (hconv : ConvexOn ℝ univ u)
(hnorm : ∫ x, Real.exp (-u x) ∂(volume : Measure (EuclideanSpace ℝ (Fin n))) = 1)
{K : Set (EuclideanSpace ℝ (Fin n))} (hK : IsCompact K) (hKc : Convex ℝ K)
(hV : ContDiff ℝ 1 V) {κ : ℝ} (hκ : 0 < κ)
(hstrong : ConvexOn ℝ univ (fun y => V y - (κ / 2) * ‖y‖ ^ 2))
(hmap : ((volume : Measure (EuclideanSpace ℝ (Fin n))).tilted (-u)).map (gradient u) =
(volume : Measure (EuclideanSpace ℝ (Fin n))).withDensity
(K.indicator fun p => ENNReal.ofReal (Real.exp (-V p))))
(hC1 : ContDiff ℝ 1 u) :
∀ x h : EuclideanSpace ℝ (Fin n),
u (x + h) + u (x - h) - 2 * u x ≤ (4 / κ) * ‖h‖ ^ 2 := by sorry