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

PrincipalResponseComposeDisplayTwo #

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

Provider: display 2 of leg (iv) in the manuscript's own printed shape #

Source display in ABK26, inside Step 3 of the proof of l.approximate.recurrence.formula (label):

  ( avsum_{z in 3^n Zd cap cu_K} E[ | bfAhom_{m-1}^{1/2} P_z |^4 ] )^{1/4}  <=  C .

The proved exists_const_fourthMoment_annealedSqrtNormSq_principalPz_le (PrincipalResponseDisplayTwo) delivers this display in two respects weaker than printed:

This module removes both gaps, and nothing else.

The order swap #

descendantsAverage Q j is by definition (card)^{-1} times a finite Finset sum, so the swap is the linearity of the Bochner integral over a finite index set (integral_const_mul and integral_finsetSum). What linearity costs is exactly one side condition: each individual expectation must exist. That is hintR below, a per-cube integrability binder. It is a well-definedness condition on the manuscript's own E[ ... ], of the same class as the measurability binder hmeasX already carried by the proved display, and it is not a bound: no numerical estimate, and no conclusion of any predecessor node, is assumed by it. hmeasR is the matching per-cube measurability, from which the proved display's own composite hmeasX is produced here rather than demanded of the caller.

The bare C #

gaugeRatio sigmaLow sigmaTop = max (sigmaTop / sigmaLow) (sigmaLow / sigmaTop). At the manuscript's own pair of scales, sigmaLow = shom_{m-h} and sigmaTop = shom_{m-1}, the two-sided envelope e.shom.h.bounds (the first clause of d.mathcalS.def) controls both quotients through the two proved running-diffusivity comparisons

  shom_i <= 4 . 3^{cgamma (i - j)} . shom_j ,      shom_j <= 4 shom_i
                                                  (j <= i <= m_0) .

Since h >= 1 we have m - h <= m - 1, and the shell budget h <= 6 cstar cgamma^{-1}/7183 -- a gate the proved display already binds -- together with cstar <= 3/2 gives cgamma ((m-1) - (m-h)) = cgamma (h - 1) <= 6 cstar <= 9, so the exponential factor is at most 3^9. Hence

  gaugeRatio shom_{m-h} shom_{m-1}  <=  4 . 3^9 ,

an absolute constant. The only genuinely new input is the gate m - 1 <= m_0 (hm1), which places the reading scale inside the range on which the induction state is available; it is carried as an explicit binder, never discharged and never hidden. The manuscript's "by e.shom.h.bounds" is exactly this step, and this is where its scale requirement becomes visible.

Binders #

Every theorem below is a **** provider result: each is stated under propositions the caller must supply, and none of them is a source-facing frozen declaration. What the caller owes, plainly:

No proof step of the source argument, and no conclusion of a predecessor node, is assumed by any of them.

References #

The order of the two averages #

Expectation and grid average commute. descendantsAverage Q j is a normalized finite Finset sum, so the manuscript's avsum_z E[ . ] and the pathwise-then-integrate E[ avsum_z . ] agree as soon as each individual expectation exists.

: this statement holds only under the propositions supplied by its binders; it is a provider A, not a source-facing frozen declaration.

Per-cube measurability upgrades to measurability of the grid average.

: this statement holds only under the propositions supplied by its binders; it is a provider A, not a source-facing frozen declaration.

The reading gauge #

theorem Algsuperdiff.Section3.Provider.Diffusivity.ApproximateRecurrence.gaugeRatio_sigmaBar_le_readingConst {d : ℕ} (M : ABKModel d) {m0 : ℤ} {E : { E : ℝ // 1 ≤ E }} (hS : Frozen.Section3.inductionState M m0 E) {m : ℤ} {h : ℕ} (hhpos : 0 < h) (hm1 : m - 1 ≤ m0) (hh : ↑h ≤ 6 * Disorder.cstar M * M.gamma⁻¹) :
gaugeRatio (Annealed.sigmaBar M (m - ↑h)) (Annealed.sigmaBar M (m - 1)) ≤ 4 * 3 ^ 9

The gauge factor is an absolute constant at the manuscript's own pair of scales.

On the range where the induction state is available -- m - h <= m - 1 <= m0 -- e.shom.h.bounds gives shom_{m-1} <= 4 . 3^{cgamma (h-1)} shom_{m-h} and shom_{m-h} <= 4 shom_{m-1}. The shell budget h <= 6 cstar cgamma^{-1}/7183 together with cstar <= 3/2 caps the exponent at 9, so both quotients are at most 4 . 3^9.

hm1 is the reading-scale gate: it is a genuine input, not derivable from the gates, and is carried as a binder.

: this statement holds only under the propositions supplied by its binders; it is a provider A, not a source-facing frozen declaration.

Display 2 in the printed shape #

theorem Algsuperdiff.Section3.Provider.Diffusivity.ApproximateRecurrence.exists_const_gridAverage_fourthMoment_annealedSqrtNormSq_principalPz_le (d : ℕ) (hd : 2 ≤ d) :
∃ (C : ℝ), 0 < C ∧ ∃ (gamma0 : ℝ), 0 < gamma0 ∧ gamma0 ≤ 1 / 4 ∧ ∀ (M : ABKModel d), M.gamma ≤ gamma0 → ∀ (m0 : ℤ) (E : { E : ℝ // 1 ≤ E }), Frozen.Section3.inductionState M m0 E → ∀ (m K : ℤ) (h : ℕ), 0 < h → m - ↑h ≤ m0 → m - 1 ≤ m0 → ↑h ≤ 6 * Disorder.cstar M * M.gamma⁻¹ → 10 ^ 10 * M.gamma⁻¹ ≤ ↑K - ↑m → ∀ (e e' : HCPolySupport.Vec d), HCPolySupport.Book.Ch02.vecNorm e ≤ 1 → HCPolySupport.Book.Ch02.vecNorm e' ≤ 1 → ∀ (j : ℕ) (wD : Cutoff.CutoffSample d → HCPolySupport.H10Function (HCPolySupport.openCubeSet (HCPolySupport.originCube d K))) (wN : Cutoff.CutoffSample d → HCPolySupport.H1MeanZeroFunction (HCPolySupport.openCubeSet (HCPolySupport.originCube d K))), (∀ (z : Cutoff.CutoffSample d), HCPolySupport.IsZeroTraceDirichletRhsWeakSolution (fun (x : HCPolySupport.Vec d) => 1) (HCPolySupport.openCubeSet (HCPolySupport.originCube d K)) (wD z) fun (x : HCPolySupport.Vec d) => -Corrector.streamForcing (↑(Annealed.sigmaBar M (m - ↑h)))⁻¹ (↑z) (m - ↑h) m e x) → (∀ (z : Cutoff.CutoffSample d), HCPolySupport.IsMeanZeroNeumannRhsWeakSolution (fun (x : HCPolySupport.Vec d) => 1) (HCPolySupport.openCubeSet (HCPolySupport.originCube d K)) (wN z) fun (x : HCPolySupport.Vec d) => -Corrector.streamForcing (↑(Annealed.sigmaBar M (m - ↑h)))⁻¹ (↑z) (m - ↑h) m e' x) → (∀ R ∈ HCPolySupport.descendantsAtDepth (HCPolySupport.originCube d K) j, Measurable fun (z : Cutoff.CutoffSample d) => annealedSqrtNormSq (Annealed.sigmaBar M (m - 1)) (principalPz (Annealed.sigmaBar M (m - ↑h)) (↑z) (m - ↑h) m e e' R (wD z) (wN z)) ^ 2) → (∀ R ∈ HCPolySupport.descendantsAtDepth (HCPolySupport.originCube d K) j, MeasureTheory.Integrable (fun (z : Cutoff.CutoffSample d) => annealedSqrtNormSq (Annealed.sigmaBar M (m - 1)) (principalPz (Annealed.sigmaBar M (m - ↑h)) (↑z) (m - ↑h) m e e' R (wD z) (wN z)) ^ 2) ↑(Cutoff.cutoffSampleLaw M)) → (HCPolySupport.descendantsAverage (HCPolySupport.originCube d K) j fun (R : HCPolySupport.TriadicCube d) => ∫ (z : Cutoff.CutoffSample d), annealedSqrtNormSq (Annealed.sigmaBar M (m - 1)) (principalPz (Annealed.sigmaBar M (m - ↑h)) (↑z) (m - ↑h) m e e' R (wD z) (wN z)) ^ 2 ∂↑(Cutoff.cutoffSampleLaw M)) ^ (1 / 4) ≤ C

Display 2 of leg (iv) of l.approximate.recurrence.formula, in the manuscript's own order and with the printed bare constant.

There is a dimension-free constant C and a positive threshold gamma0 such that, for every ABK model with cgamma <= gamma0, every valid induction state S(m0, E), every m, K, h obeying the four scale gates m - h <= m0, m - 1 <= m0, h <= 6 cstar cgamma^{-1} and K >= m + 10^10 cgamma^{-1}, all directions e, e' of Euclidean length at most one, every localization depth j and every sample family of solutions of the two problems e.def.w,

  ( avsum_R E[ | bfAhom_{m-1}^{1/2} P_R |^4 ] )^{1/4}  <=  C ,

with R running over the triadic descendants of cu_K at depth j and P_R the localized load of e.Pz.def, read at the manuscript's own gauge shom_{m-1}.

hmeasR and hintR are the per-cube well-definedness binders of the printed E[ . ]; hm1 is the reading-scale gate discussed in the module header.

: this statement holds only under the propositions supplied by its binders; it is a provider A, not a source-facing frozen declaration.