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LeanPool.Superdiffusion.Algsuperdiff.Section4.Provider.BoundsEaL.Step3HSlot

Step3HSlot #

Ported from scottnarmstrong/Superdiffusion at 7f20476385d4af418f110303085ad0740c9eba46. Module imports, support namespace and local option directives adapted for Lean Pool.

Step 3: the sensitivity estimate at the recentered h-slot, at scale j #

The target #

Step 3 of the proof of l.bounds.mathcal.E.aL applies

the sensitivity estimate e.J.sensitivity.no.conditions of Lemma l.J.sensitivity.no.conditions in the cube □_j with μ := σ_m σ_{j-2}^{-1}, σ₀ := σ_{j-2} and h := k_L − k_{j-2} − (k_L − k_m)_{□_m}

to the single J-moment produced by Steps 1--2, "as in e.ugly.estimate.for.J.pre in Step 2 of the proof of Proposition p.mathcalE.annular.decomp".

That template is already proved in this repository, for §4.1, at the SAME witness family and with the SAME recentered h-slot:

What the export gives, and why that shape #

The conclusion bounds

J(3^j v + □_j , σ_m^{-1/2} e , σ_m^{1/2} e; a_L − (k_L − k_m)_{□_m})

by a right-hand side not containing e. That is exactly the hypothesis shape hfld of the proved primal/adjoint split Annular.normalizedBlockResponseMax_isotropicComparator_le (e.bfJ.general, constant 1), which is the step that connects this display to the block response maximum appearing in d.mathcal.E — i.e. to the inner object of the Step-1 endpoint of Step1ScaleSum.lean.

Deviations from the printed display, all inherited #

Main result #

References #

theorem Algsuperdiff.Section4.Provider.BoundsEaL.exists_responseJ_step3_recentered (d : ℕ) (dimension : 2 ≤ d) :
∃ (C : ℝ), 0 < C ∧ ∀ (M : Section3.ABKModel d) (m j L : ℤ) (hle : j - 2 ≤ L) (v : Fin d → ℤ) (omega : Section3.Cutoff.CutoffSample d) (s : ℝ) (e : HCPolySupport.Vec d), 0 < s → s ≤ 1 / 4 → M.gamma ≤ 1 / 8 → HCPolySupport.Book.Ch02.vecNorm e = 1 → HCPolySupport.Book.Ch02.responseJ (HCPolySupport.Book.Ch02.cubeDomain { scale := j, index := v }) ((Annular.subConstCutoffTriadicCoeffFamily M L (Support.fluxIncrementAverage M L m (HCPolySupport.originCube d m) omega) ⋯ omega).coeffOn { scale := j, index := v }) (Section3.Observable.inverseSqrtLoad (Section3.Annealed.sigmaBar M m) e) (Section3.Observable.sqrtLoad (Section3.Annealed.sigmaBar M m) e) ≤ C * ((↑(Section3.Annealed.sigmaBar M m))⁻¹ * ↑(Section3.Annealed.sigmaBar M (j - 2))) * (1 + (Section3.Provider.Diffusivity.ApproximateRecurrence.centeredShellUnitCube M { scale := j, index := v } hle omega (Support.fluxIncrementAverage M L m (HCPolySupport.originCube d m) omega) ⋯).gradientW1Infinity * (Frozen.Section24.unitCubeLambda (2 * M.gamma) (HCPolySupport.Book.Ch02.MultiscaleExponent.finite 2) (Section3.Provider.BadEvents.unitRescaledCutoffCoeff M { scale := j, index := v } (j - 2) omega))⁻¹).rpow (2 * s / (1 - 4 * M.gamma)) * Frozen.Section24.unitCubeHomogenizationError s (HCPolySupport.Book.Ch02.MultiscaleExponent.finite 2) (HCPolySupport.Book.Ch02.MultiscaleExponent.finite 2) (Section3.Provider.BadEvents.unitRescaledCutoffCoeff M { scale := j, index := v } (j - 2) omega) (Section3.Observable.isotropicComparatorMatrix (Section3.Annealed.sigmaBar M (j - 2))) ^ 2 + C * ((↑(Section3.Annealed.sigmaBar M m))⁻¹ * ↑(Section3.Annealed.sigmaBar M (j - 2)) ^ 2) * (Frozen.Section24.unitCubeLambda (2 * M.gamma) (HCPolySupport.Book.Ch02.MultiscaleExponent.finite 2) (Section3.Provider.BadEvents.unitRescaledCutoffCoeff M { scale := j, index := v } (j - 2) omega))⁻¹ * (1 + (Section3.Provider.Diffusivity.ApproximateRecurrence.centeredShellUnitCube M { scale := j, index := v } hle omega (Support.fluxIncrementAverage M L m (HCPolySupport.originCube d m) omega) ⋯).gradientW1Infinity * (Frozen.Section24.unitCubeLambda (2 * M.gamma) (HCPolySupport.Book.Ch02.MultiscaleExponent.finite 2) (Section3.Provider.BadEvents.unitRescaledCutoffCoeff M { scale := j, index := v } (j - 2) omega))⁻¹).rpow (4 * M.gamma / (1 - 4 * M.gamma)) * ((↑(Section3.Annealed.sigmaBar M (j - 2)))⁻¹ ^ 2 * (Section3.Provider.Diffusivity.ApproximateRecurrence.centeredShellUnitCube M { scale := j, index := v } hle omega (Support.fluxIncrementAverage M L m (HCPolySupport.originCube d m) omega) ⋯).valueL2 ^ 2 + (↑(Section3.Annealed.sigmaBar M m) * (↑(Section3.Annealed.sigmaBar M (j - 2)))⁻¹ - 1) ^ 2) + C * (((↑(Section3.Annealed.sigmaBar M m))⁻¹ + (Frozen.Section24.unitCubeLambda (2 * M.gamma) (HCPolySupport.Book.Ch02.MultiscaleExponent.finite 2) (Section3.Provider.BadEvents.unitRescaledCutoffCoeff M { scale := j, index := v } (j - 2) omega))⁻¹) * (Section3.Provider.Diffusivity.ApproximateRecurrence.centeredShellUnitCube M { scale := j, index := v } hle omega (Support.fluxIncrementAverage M L m (HCPolySupport.originCube d m) omega) ⋯).gradientW1Infinity) ^ 2

Step 3, field leg. The Section 2.4 sensitivity anchor, instantiated at §4.5's own witness

μ := σ_m σ_{j-2}^{-1} ,   σ₀ := σ_{j-2} ,   h := k_L − k_{j-2} − (k_L − k_m)_{□_m} ,

read at the lattice cube 3^j v + □_j through the proved response transport. The right-hand side does not mention e, so it is directly usable as the primal half of Annular.normalizedBlockResponseMax_isotropicComparator_le.

The three summands are the printed ones of e.apply.sensitivity.J.aL, in the anchor's unit-cube gauges and at the gapped gauge s = 2γ; see the module docstring for the complete deviation list.