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LeanPool.HighContrastHomogenization.Support.Book.Ch05.Theorems.Section57.ProbeMax

Coarse-graining support: Support.Book.Ch05.Theorems.Section57.ProbeMax #

Imported from the Apache-2.0 CoarseGraining development at commit c7ddd76c08ade64fed1b8d2ca51be14dfee8deb4.

Normalized finite-probe maxima #

The quenched minimal-scale theorem needs one random observable which controls the localized response for every unit vector. The finite-basis reduction allows this observable to be a finite maximum over normalized coordinate and pair probes.

The three finite probe types: coordinate, sum of two coordinates, and difference of two coordinates.

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    Coordinate probes are already normalized; plus/minus probes are divided by two.

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      Localized maximum over the normalized finite probe family.

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        theorem HCPolySupport.Book.Ch05.Section57.localizedFirstQuenchedEstimate_normalizedProbeJMax {d : ℕ} [NeZero d] {σ : ℝ} (hσ_pos : 0 < σ) (params : QuantitativeCoarseGrainedEllipticityParams d) :
        ∃ (Cfluct : ℝ) (Centry : ℝ) (α : ℝ), 0 < Cfluct ∧ 0 < Centry ∧ 0 < α ∧ ∀ {P : Ch04.RestrictionCoeffLaw d} (hP : Ch04.RestrictionLawCarrier P) (hStruct : Ch04.RestrictionStructuralLaw P) (hΓ : GammaSigmaCoarseGrainedEllipticity P hP hStruct), hΓ.sigma = σ → hΓ.params = params → ∀ {ℓ n m : ℕ}, ℓ < n → n < m → have N0 := annealedAlgebraicEntryScale P hΓ.toQuantitativeCoarseGrainedEllipticity Centry; have D := descendantsAtScale (originCube d ↑(N0 + m)) ↑(N0 + n); have S := Finset.univ; IndependentSums.IsBigOWith P (IndependentSums.gammaSigma (min σ 2)) (fun (a : RegCoeffField d) => localizedNormalizedProbeJMax hP hStruct (N0 + m) (N0 + n) a - Real.rpow 3 (-α * ↑ℓ)) ((3 * Real.log ↑S.card) ^ (min σ 2)⁻¹ * ((3 * Real.log ↑D.card) ^ (min σ 2)⁻¹ * (Cfluct * 3 ^ (-↑d / 2 * ↑(↑(N0 + n) - ↑(N0 + ℓ)).toNat) * hΓ.thetaHat ^ 2)))

        Localized first-quenched estimate for the normalized finite-probe maximum.

        This is the fixed-vector localized estimate, applied to the finite normalized probe family and combined by the Chapter 4 finite-maximum rule.

        theorem HCPolySupport.Book.Ch05.Section57.localizedFirstQuenchedEstimate_normalizedProbeJMax_uniformAnnealedExponent {d : ℕ} [NeZero d] (params : QuantitativeCoarseGrainedEllipticityParams d) :
        ∃ (Centry : ℝ) (a : ℝ), 0 < Centry ∧ 0 < a ∧ ∀ {σ : ℝ}, 0 < σ → ∃ (Cfluct : ℝ), 0 < Cfluct ∧ ∀ {P : Ch04.RestrictionCoeffLaw d} (hP : Ch04.RestrictionLawCarrier P) (hStruct : Ch04.RestrictionStructuralLaw P) (hΓ : GammaSigmaCoarseGrainedEllipticity P hP hStruct), hΓ.sigma = σ → hΓ.params = params → ∀ {ℓ n m : ℕ}, ℓ < n → n < m → have N0 := annealedAlgebraicEntryScale P hΓ.toQuantitativeCoarseGrainedEllipticity Centry; have D := descendantsAtScale (originCube d ↑(N0 + m)) ↑(N0 + n); have S := Finset.univ; IndependentSums.IsBigOWith P (IndependentSums.gammaSigma (min σ 2)) (fun (aω : RegCoeffField d) => localizedNormalizedProbeJMax hP hStruct (N0 + m) (N0 + n) aω - Real.rpow 3 (-a * ↑ℓ)) ((3 * Real.log ↑S.card) ^ (min σ 2)⁻¹ * ((3 * Real.log ↑D.card) ^ (min σ 2)⁻¹ * (Cfluct * 3 ^ (-↑d / 2 * ↑(↑(N0 + n) - ↑(N0 + ℓ)).toNat) * hΓ.thetaHat ^ 2)))

        Uniform-in-σ version of localizedFirstQuenchedEstimate_normalizedProbeJMax.

        theorem HCPolySupport.Book.Ch05.Section57.isBigO_localizedNormalizedProbeJMax {d : ℕ} [NeZero d] {σ : ℝ} (hσ_pos : 0 < σ) (params : QuantitativeCoarseGrainedEllipticityParams d) :
        ∃ (C : ℝ), 0 < C ∧ ∀ {P : Ch04.RestrictionCoeffLaw d} (hP : Ch04.RestrictionLawCarrier P) (hStruct : Ch04.RestrictionStructuralLaw P) (hΓ : GammaSigmaCoarseGrainedEllipticity P hP hStruct), hΓ.sigma = σ → hΓ.params = params → ∀ {m n : ℕ}, n < m → have D := descendantsAtScale (originCube d ↑m) ↑n; have S := Finset.univ; IndependentSums.IsBigO P (IndependentSums.gammaSigma σ) (localizedNormalizedProbeJMax hP hStruct m n) ((3 * Real.log ↑S.card) ^ σ⁻¹ * ((3 * Real.log ↑D.card) ^ σ⁻¹ * (C * hΓ.thetaHat ^ 2)))

        Crude Γσ estimate for the localized normalized finite-probe maximum.