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LeanPool.Superdiffusion.Algsuperdiff.Section3.Provider.Diffusivity.ApproximateRecurrence.PrincipalComplementEnergy

PrincipalComplementEnergy #

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

The complement-energy leg of the principal response, per grid cube #

The good-event display bounds the grid average of switchCubeEnergy against the complement indicator of the per-cube bad event principalBadEvent M Ccg R (m - h). This module supplies the other half: the same grid average against the bad event itself.

Nothing here is common to all cubes. The envelope B, the load V and the event all vary with the grid cube R, and the tail is the per-cube estimate of exists_badEventEstimate_perCube_dimFloor_ofFrozen at the same floor-normalized constant Cbase the good leg uses. That estimate is derived from the frozen Algsuperdiff.Frozen.Section3.bad_event_estimate through the scale-shift bridge, and it carries the union-bound factor 2, which is absorbed here through the Hoelder power: (2 s) ^ (3/8) = 2 ^ (3/8) . s ^ (3/8), and 2 ^ (3/8) folds into the exported envelope constant C, which is enlarged to 2 ^ (3/8) . max C₀ Kgate. No binder and no public statement changes: the enlargement only strengthens the premises the caller already supplies at C, and the dimension-only gate Kgate ≤ E⁻² gamma⁻¹ of the frozen application is derived here from the window premise gamma / 2 + exp (-(C⁻¹ E⁻² gamma⁻¹)) ≤ gamma together with gamma ≤ 1 / 1089.

Visibility #

Exactly one declaration of this module is visible outside it, the grid-average endpoint exists_const_descendantsAverage_integral_switchCubeEnergy_badEvent_le. Every other declaration here stays private.

That endpoint returns the bad leg's per-cube integrability on the conclusion side, alongside its estimate. The integrability is not a new hypothesis and nothing new is proved for it: the Hoelder split already establishes MemLp X (8/5) for the cube energy, and on this finite measure that membership read at exponent one is exactly the integrability, which then rides the conclusion chain outward through the per-cube estimate. A direct consumer needs it to recombine the bad leg with the complement leg.

Elementary real bounds #

The scale gap and the oscillation rate both need t * logb 3 t⁻¹ to be small for small t. The crude log y ≤ y - 1 is far too weak here (it only gives t * logb 3 t⁻¹ ≤ (log 3)⁻¹, which the gap budget cannot absorb); applying it at the square root instead gives a bound that vanishes with t. This is stated over abstract reals, away from any rpow-laden goal.

The moment leg #

The cube-independent operator envelope #

The centred operator envelope B at the centred cube of a given scale: 2 (sigma⁻¹ Lambda_{s,q} + sigma lambda⁻¹_{s,q}).

Equations
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Instances For

    The two endpoint identifications, at independent domain and cutoff scales #

    Promotion from the cube scale to the field scale #

    Transport along the base-point translation #

    The per-cube complement energy #

    The grid average of the complement energy #

    theorem Algsuperdiff.Section3.Provider.Diffusivity.ApproximateRecurrence.exists_const_descendantsAverage_integral_switchCubeEnergy_badEvent_le (d : ℕ) :
    ∃ (Cbase : ℝ) (c : ℝ) (C : ℝ), 1 ≤ Cbase ∧ ↑d ^ 6 ≤ Cbase ∧ 0 < c ∧ 0 < C ∧ ∀ (M : ABKModel d) (E : { E : ℝ // 1 ≤ E }) (m h : ℤ) (a : ℕ) (Q : HCPolySupport.TriadicCube d) (j : ℕ) (P : HCPolySupport.TriadicCube d → Cutoff.CutoffSample d → HCPolySupport.BlockVec d) (Cv : ℝ), Frozen.Section3.inductionState M (m - 1) E → 0 < h → a ≤ 32 → Q.scale - ↑j = recurrenceMesoScale a M.gamma m h → max (Real.exp (2 * C)) (Disorder.cstar M)⁻¹ ≤ ↑E → c⁻¹ * (Disorder.cstar M)⁻¹ ≤ ↑E → ↑E ≤ M.gamma ^ (-(1 / 5)) → M.gamma / 2 + Real.exp (-(C⁻¹ * (↑E)⁻¹ ^ 2 * M.gamma⁻¹)) ≤ M.gamma → M.gamma ≤ 1 / 1089 → ↑h ≤ 6 * Disorder.cstar M * M.gamma⁻¹ → 0 ≤ Cv → (∀ R ∈ HCPolySupport.descendantsAtDepth Q j, MeasureTheory.AEStronglyMeasurable (fun (omega : Cutoff.CutoffSample d) => switchCubeEnergy M m R (P R omega) omega) ↑(Cutoff.cutoffSampleLaw M)) → (∀ R ∈ HCPolySupport.descendantsAtDepth Q j, MeasureTheory.AEStronglyMeasurable (fun (omega : Cutoff.CutoffSample d) => √(annealedSqrtNormSq (Annealed.sigmaBar M (m - 1)) (P R omega))) ↑(Cutoff.cutoffSampleLaw M)) → (∀ R ∈ HCPolySupport.descendantsAtDepth Q j, MeasureTheory.Integrable (fun (omega : Cutoff.CutoffSample d) => annealedSqrtNormSq (Annealed.sigmaBar M (m - 1)) (P R omega) ^ 2) ↑(Cutoff.cutoffSampleLaw M)) → (HCPolySupport.descendantsAverage Q j fun (R : HCPolySupport.TriadicCube d) => ∫ (omega : Cutoff.CutoffSample d), annealedSqrtNormSq (Annealed.sigmaBar M (m - 1)) (P R omega) ^ 2 ∂↑(Cutoff.cutoffSampleLaw M)) ≤ Cv ^ 4 → (∀ R ∈ HCPolySupport.descendantsAtDepth Q j, MeasureTheory.Integrable (fun (omega : Cutoff.CutoffSample d) => switchCubeEnergy M m R (P R omega) omega * (principalBadEvent M (2 * Cbase) R (m - h)).indicator (fun (x : Cutoff.CutoffSample d) => 1) omega) ↑(Cutoff.cutoffSampleLaw M)) ∧ (HCPolySupport.descendantsAverage Q j fun (R : HCPolySupport.TriadicCube d) => ∫ (omega : Cutoff.CutoffSample d), switchCubeEnergy M m R (P R omega) omega * (principalBadEvent M (2 * Cbase) R (m - h)).indicator (fun (x : Cutoff.CutoffSample d) => 1) omega ∂↑(Cutoff.cutoffSampleLaw M)) ≤ 3 ^ 22 * (2 * (C * (Disorder.cstar M)⁻¹ * M.gamma⁻¹)) * Cv ^ 2 * (Real.exp (-(c * Disorder.cstar M * M.gamma⁻¹ * 3 ^ (-5 * BadEvents.scaleGapPos (m - h) (recurrenceMesoScale a M.gamma m h)) * 3 ^ BadEvents.scaleGapPos (recurrenceMesoScale a M.gamma m h) (m - h))) + Real.exp (-Real.exp (c * (↑E)⁻¹ ^ 2 * M.gamma⁻¹))) ^ (3 / 8)

    The bad-event leg at the grid average.

    On a grid of depth-j triadic descendants of Q whose common scale Q.scale - j is the recurrence mesoscale, the discrete Cauchy--Schwarz step of gridAverage_badEventEnergy_le turns the per-cube square roots of the previous estimate into the square root of the grid average, which is the left-hand side of the fourth-moment display. The tail is the same for every cube of the grid, since every cube of the grid has the same scale.

    Cv enters as the fourth root of the grid average of the load moment, a named binder: nothing here estimates that average. The three well-definedness binders are the per-cube ones of the previous estimate.

    : this statement holds only under the propositions supplied by its binders.