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LeanPool.HighContrastHomogenization.Entry.Response.Core.ResponseCalibrationBundles

High-contrast homogenization: Entry.Response.Core.ResponseCalibrationBundles #

Imported from the Apache-2.0 HighContrastHomogenization development at commit 7a13dbcd8d6609264a713373f5c69ceeac870472.

Calibration and source smallness from the raw output #

This file derives two of the hypothesis bundles that adapted_response_core is assembled from, directly from the raw multiscale output RawOutput: response_calibrated_blocks (e.response.calibrated.blocks) produces the calibration bundle RespCalibrated from the Riccati identity and the response-imbalance comparison, and response_source_smallness (e.response.source.smallness) produces a source-smallness constant Bresp from the two-grid source normalization and the scale-decay estimate. It also carries the load-quadratic identities — respLsqMinus/Plus, respEJMinus/Plus, respTauMinus, and the pairings of the response load vectors against respP — that later files in the chain need.

theorem HCPolySupport.HighContrast.Multiscale.response_calibrated_blocks (d : ℕ) (_hd : 2 ≤ d) (γ : ℝ) (_hγ : γ ∈ Set.Ico 0 1) (S : SelectionData) (_hS : S.Selects d γ) :
∃ (C : ℝ), 0 < C ∧ ∀ (ε σ : ℝ), ε ∈ Set.Ioc 0 S.eps0 → σ ∈ Set.Ioc 0 ε → ∀ (Cglob Cprof Csrc Bresp : ℝ) (H : ℕ) (P : MeasureTheory.Measure (CoeffSpace d)) (E : BlockMat d) (Ψ : ℝ → ℝ) (Kg : ℝ) (Src : CoeffSpace d → ℝ) (B : ℝ) (jStar : ℕ) (F : BlockMat d) (s t : ℤ), RawOutput d γ S ε σ Cglob Cprof Csrc H Bresp P E Ψ Kg Src B jStar F s t → RespCalibrated C P jStar F s t

e.response.calibrated.blocks. The constant depends only on (d, γ), never on the law, F, E, Pi or σ.

Route: the Riccati identity GeometricMean.geoMean_riccati gives M(E) <= E <= d(E)^{1/2} M(E) (p.response.transfer); then raw.calib_lo/raw.calib_hi at s, the comparison E_t <= E_s <= r E_t of response_imbalance_comparison, and GeometricMean.geoMean_mono. The congruence cost is sqrt(r(1+xi)/(1-xi)) <= C(d) because xi <= 1/2 and r < e^{d/2}; D commutes with M_0.

The premises _hε, _hσ are carried: without them 1 ≤ B and jStar ≤ s are unavailable. The binder names are underscore-prefixed because they do not occur in the statement's own type; the hypotheses are unchanged and the proof uses them (with no linter suppression).

The source smallness estimate #

theorem HCPolySupport.HighContrast.Multiscale.response_source_smallness (d : ℕ) (_hd : 2 ≤ d) (γ : ℝ) (_hγ : γ ∈ Set.Ico 0 1) (S : SelectionData) (_hS : S.Selects d γ) :
∃ (Csrc : ℝ), 0 < Csrc ∧ ∃ (C : ℝ), 0 < C ∧ ∀ ε ∈ Set.Ioc 0 S.eps0, ∀ δad ∈ Set.Ioc 0 1, ∀ (H : ℕ), max 4 S.h ≤ H → ∀ η ∈ Set.Ioo 0 (1 / 2), ∀ (Cprof : ℝ), 0 < Cprof → ∀ σ ∈ Set.Ioc 0 ε, ∀ (Cglob : ℝ), 0 < Cglob → ∃ (Bresp : ℝ), 1 ≤ Bresp ∧ ∀ (P : MeasureTheory.Measure (CoeffSpace d)) (E : BlockMat d) (Ψ : ℝ → ℝ) (Kg : ℝ) (Src : CoeffSpace d → ℝ) (B : ℝ) (jStar : ℕ) (F : BlockMat d) (s t : ℤ), RawOutput d γ S ε σ Cglob Cprof Csrc H Bresp P E Ψ Kg Src B jStar F s t → RespSourceSmall d γ C η E F jStar s t

e.response.source.smallness: C Pi e(m)^2 3^{-rho_max(t-j_*)} <= eta^{1/Q} and C Pi e(m)^2 3^{-3(s-j_*)/2} <= 1, once B_resp is large enough. B_resp is chosen after H, eta, sigma and C_glob, exactly the printed order (e.response.source.smallness).

Route: raw.hsrc and Annealed.adaptedMean_refBlock_normalization (e.two.grid.source.normalization, e.two.grid.source.normalization) supply Csrc and the envelope constant; raw.ecc (e.global.selection.eccentricity, e.global.selection.eccentricity) bounds e(m)^2 by (2+Pi)^{2 Cglob}; raw.hs_lo and raw.ht_hi (e.global.selection.scales, e.global.selection.scales) with scale_decay_of_hs_lo turn 3^{-rho(u-j_*)} into (2+Pi)^{-rho B}, and exists_Bresp_of_decay then produces Bresp. raw.hB gives Bresp <= B, and 1 <= aspectRatio E comes from Analysis.one_le_aspectRatio.

A skew matrix has vanishing quadratic form.

theorem HCPolySupport.HighContrast.Multiscale.respLsqMinus_eq {d : ℕ} (P : MeasureTheory.Measure (CoeffSpace d)) (jStar : ℕ) (F : BlockMat d) (t : ℤ) (e : Vec d) :
respLsqMinus P jStar F t e = blockVecDot (-respP (respMean P jStar F t) e, respQ (respMean P jStar F t) e - matVecMul (respSkew (respMean P jStar F t)) (respP (respMean P jStar F t) e)) (blockMatVecMul (respMean P jStar F t) (-respP (respMean P jStar F t) e, respQ (respMean P jStar F t) e - matVecMul (respSkew (respMean P jStar F t)) (respP (respMean P jStar F t) e)))

(L^-)^2 = (-p, q - h_t p). E_t (-p, q - h_t p).

theorem HCPolySupport.HighContrast.Multiscale.respLsqPlus_eq {d : ℕ} (P : MeasureTheory.Measure (CoeffSpace d)) (jStar : ℕ) (F : BlockMat d) (t : ℤ) (e : Vec d) :
respLsqPlus P jStar F t e = blockVecDot (-respP (respMean P jStar F t) e, -(respQ (respMean P jStar F t) e + matVecMul (respSkew (respMean P jStar F t)) (respP (respMean P jStar F t) e))) (blockMatVecMul (respMean P jStar F t) (-respP (respMean P jStar F t) e, -(respQ (respMean P jStar F t) e + matVecMul (respSkew (respMean P jStar F t)) (respP (respMean P jStar F t) e))))

(L^+)^2 = (-p, -(q + h_t p)). E_t (-p, -(q + h_t p)).

theorem HCPolySupport.HighContrast.Multiscale.vecDot_respP_respqMinus_eq_one {d : ℕ} (P : MeasureTheory.Measure (CoeffSpace d)) (jStar : ℕ) (F : BlockMat d) (t : ℤ) (e : Vec d) (he : vecDot e e = 1) (hM : Matrix.PosDef (respM (respMean P jStar F t))) (hg : matTranspose (respg F) = -respg F) :
vecDot (respP (respMean P jStar F t) e) (respqMinus P jStar F t e) = 1

The reciprocal normalizations: p. q^- = 1 (the skew part cancels).

theorem HCPolySupport.HighContrast.Multiscale.vecDot_respP_respqPlus_eq_one {d : ℕ} (P : MeasureTheory.Measure (CoeffSpace d)) (jStar : ℕ) (F : BlockMat d) (t : ℤ) (e : Vec d) (he : vecDot e e = 1) (hM : Matrix.PosDef (respM (respMean P jStar F t))) (hg : matTranspose (respg F) = -respg F) :
vecDot (respP (respMean P jStar F t) e) (respqPlus P jStar F t e) = 1

The reciprocal normalizations: p. q^+ = 1.

Congruence is monotone for the Loewner order.

theorem HCPolySupport.HighContrast.Multiscale.respEJMinus_eq {d : ℕ} (P : MeasureTheory.Measure (CoeffSpace d)) [MeasureTheory.IsProbabilityMeasure P] (jStar : ℕ) (F : BlockMat d) (t : ℤ) (e : Vec d) (he : vecDot e e = 1) (hM : Matrix.PosDef (respM (respMean P jStar F t))) (hg : matTranspose (respg F) = -respg F) (hpath : ∀ (a : CoeffSpace d), respJ (respGrid jStar F) t (respP (respMean P jStar F t) e) (respqMinus P jStar F t e) (respCoeffMinus F a) = 1 / 2 * blockVecDot (respxMinus P jStar F t e) (blockMatVecMul (coarseBlockMatrix (respCell jStar F t) (respCoeffMinus F a)) (respxMinus P jStar F t e)) - vecDot (respP (respMean P jStar F t) e) (respqMinus P jStar F t e)) (hint : ∀ (α β : BlockCoord d), MeasureTheory.Integrable (fun (a : CoeffSpace d) => blockMatEntry (coarseBlockMatrix (respCell jStar F t) (respCoeffMinus F a)) α β) P) (hann : annealedBlockOf P (respCell jStar F t) (respCoeffMinus F) = respEhatMinus P jStar F t) :
respEJMinus P jStar F t e = 1 / 2 * respLsqMinus P jStar F t e - 1

The third conjunct of the response energy and defect estimate, conditional on the three bridge inputs hpath, hint, hann (p.response.transfer).

theorem HCPolySupport.HighContrast.Multiscale.respEJPlus_eq {d : ℕ} (P : MeasureTheory.Measure (CoeffSpace d)) [MeasureTheory.IsProbabilityMeasure P] (jStar : ℕ) (F : BlockMat d) (t : ℤ) (e : Vec d) (he : vecDot e e = 1) (hM : Matrix.PosDef (respM (respMean P jStar F t))) (hg : matTranspose (respg F) = -respg F) (hpath : ∀ (a : CoeffSpace d), respJ (respGrid jStar F) t (respP (respMean P jStar F t) e) (respqPlus P jStar F t e) (respCoeffPlus F a) = 1 / 2 * blockVecDot (respxPlus P jStar F t e) (blockMatVecMul (coarseBlockMatrix (respCell jStar F t) (respCoeffPlus F a)) (respxPlus P jStar F t e)) - vecDot (respP (respMean P jStar F t) e) (respqPlus P jStar F t e)) (hint : ∀ (α β : BlockCoord d), MeasureTheory.Integrable (fun (a : CoeffSpace d) => blockMatEntry (coarseBlockMatrix (respCell jStar F t) (respCoeffPlus F a)) α β) P) (hann : annealedBlockOf P (respCell jStar F t) (respCoeffPlus F) = respEhatPlus P jStar F t) :
respEJPlus P jStar F t e = 1 / 2 * respLsqPlus P jStar F t e - 1

The fourth conjunct of the response energy and defect estimate, conditional on the three bridge inputs.

theorem HCPolySupport.HighContrast.Multiscale.integral_respJ {d : ℕ} (P : MeasureTheory.Measure (CoeffSpace d)) [MeasureTheory.IsProbabilityMeasure P] (qq : Mat d) (u : ℤ) (p r : Vec d) (b : CoeffSpace d → CoeffField d) (X : BlockVec d) (Aann : BlockMat d) (hpath : ∀ (a : CoeffSpace d), respJ qq u p r (b a) = 1 / 2 * blockVecDot X (blockMatVecMul (coarseBlockMatrix (adaptedCell qq u) (b a)) X) - vecDot p r) (hint : ∀ (α β : BlockCoord d), MeasureTheory.Integrable (fun (a : CoeffSpace d) => blockMatEntry (coarseBlockMatrix (adaptedCell qq u) (b a)) α β) P) (hann : annealedBlockOf P (adaptedCell qq u) b = Aann) :
∫ (a : CoeffSpace d), respJ qq u p r (b a) ∂P = 1 / 2 * blockVecDot X (blockMatVecMul Aann X) - vecDot p r

The annealed response at one adapted cell, from the pathwise identity, entrywise integrability, and the annealed-block identification.

theorem HCPolySupport.HighContrast.Multiscale.respTauMinus_eq {d : ℕ} (P : MeasureTheory.Measure (CoeffSpace d)) [MeasureTheory.IsProbabilityMeasure P] (jStar : ℕ) (F : BlockMat d) (s t : ℤ) (e : Vec d) (hpath_s : ∀ (a : CoeffSpace d), respJ (respGrid jStar F) s (respP (respMean P jStar F t) e) (respqMinus P jStar F t e) (respCoeffMinus F a) = 1 / 2 * blockVecDot (respxMinus P jStar F t e) (blockMatVecMul (coarseBlockMatrix (respCell jStar F s) (respCoeffMinus F a)) (respxMinus P jStar F t e)) - vecDot (respP (respMean P jStar F t) e) (respqMinus P jStar F t e)) (hint_s : ∀ (α β : BlockCoord d), MeasureTheory.Integrable (fun (a : CoeffSpace d) => blockMatEntry (coarseBlockMatrix (respCell jStar F s) (respCoeffMinus F a)) α β) P) (hann_s : annealedBlockOf P (respCell jStar F s) (respCoeffMinus F) = respEhatMinus P jStar F s) (hEJ : respEJMinus P jStar F t e = 1 / 2 * respLsqMinus P jStar F t e - 1) (hpq : vecDot (respP (respMean P jStar F t) e) (respqMinus P jStar F t e) = 1) :
respTauMinus P jStar F s t e = 1 / 2 * blockVecDot (respxMinus P jStar F t e) (blockMatVecMul (respEhatMinus P jStar F s) (respxMinus P jStar F t e)) - 1 / 2 * respLsqMinus P jStar F t e

The defect at the minus load, conditional on the bridge inputs at both scales (e.response.energy.and.defect, p.response.transfer).

theorem HCPolySupport.HighContrast.Multiscale.zero_le_respTauMinus {d : ℕ} (P : MeasureTheory.Measure (CoeffSpace d)) (jStar : ℕ) (F : BlockMat d) (s t : ℤ) (e : Vec d) (hLoew : BlockMatLoewnerLE (respEhatMinus P jStar F t) (respEhatMinus P jStar F s)) (hτ : respTauMinus P jStar F s t e = 1 / 2 * blockVecDot (respxMinus P jStar F t e) (blockMatVecMul (respEhatMinus P jStar F s) (respxMinus P jStar F t e)) - 1 / 2 * respLsqMinus P jStar F t e) :
0 ≤ respTauMinus P jStar F s t e

The defect is nonnegative once Ehat_t <= Ehat_s (Annealed.adaptedMean_antitone).

theorem HCPolySupport.HighContrast.Multiscale.respEhatMinus_mono {d : ℕ} (P : MeasureTheory.Measure (CoeffSpace d)) (jStar : ℕ) (F : BlockMat d) (s t : ℤ) (h : BlockMatLoewnerLE (respMean P jStar F t) (respMean P jStar F s)) :
BlockMatLoewnerLE (respEhatMinus P jStar F t) (respEhatMinus P jStar F s)

The congruence Ehat^- = G^t E G is Loewner-monotone in E.