import OAI.Probability.FreeIsing.Estimates.BoundedAverageConcentration import OAI.Probability.FreeIsing.Gaussian.InwardGaussianCutMean import OAI.Probability.FreeIsing.Gaussian.InwardProducts import OAI.Probability.FreeIsing.Trees.CutLevelConcentration import OAI.Probability.FreeIsing.Trees.InwardCutProducts namespace OAI noncomputable section open MeasureTheory ProbabilityTheory open scoped NNReal namespace Problem367 open PathSideBranches InwardCutChannels CutContinuationChannels theorem cutContinuationProduct_gaussianInwardConeFactor_le_cut {d k L : ℕ} {beta : ℝ} {μ : Measure (TreeVertex d → IsingSpin)} (hμ : HasFreeIsingLaw (Real.tanh beta) μ) (hbeta : 0 ≤ beta) (t : ℝ≥0) (p : TreeShell d (k + 1)) (w : ReducedTail (cutLastColor p) L) : ∀ᵐ z ∂μ.prod (PlantedSymmetry.gaussianNoiseLaw (TreeVertex d) t), cutContinuationProduct (gaussianInwardConeFactor μ t (Real.tanh beta) z) p w ≤ Real.exp (4 * beta / (1 - Real.tanh beta)) * inwardContinuationProduct μ t (Real.tanh beta) p w (Finset.Ico (k + 1) (k + 1 + L)) z := by have h := inward_attenuation_prod_le_cut hμ hbeta t (appendCutContinuation p w).1 (k + 1) L (by rw [(appendCutContinuation p w).2]) simpa only [cutContinuationProduct_gaussianInwardConeFactor, inwardContinuationProduct_Ico_eq_range] using h theorem average_gaussianInwardConeFactor_le_cut_average {d k : ℕ} {beta : ℝ} {μ : Measure (TreeVertex d → IsingSpin)} (hμ : HasFreeIsingLaw (Real.tanh beta) μ) (hbeta : 0 ≤ beta) (t : ℝ≥0) (I : Finset (TreeShell d (k + 1))) (L : ℕ) : ∀ᵐ z ∂μ.prod (PlantedSymmetry.gaussianNoiseLaw (TreeVertex d) t), (∑ v ∈ cutDescendants I L, rootPathProduct (gaussianInwardConeFactor μ t (Real.tanh beta) z) v.1) / ((cutDescendants I L).card : ℝ) ≤ Real.exp (4 * beta / (1 - Real.tanh beta)) * ((∑ p ∈ I, inwardContinuationAverage μ t (Real.tanh beta) p L (Finset.Ico (k + 1) (k + 1 + L)) z) / (I.card : ℝ)) := by have hθ0 := tanh_nonneg_of_beta_nonneg hbeta have hθ1 := Real.tanh_lt_one beta have hθ : Real.tanh beta ^ 2 ≤ 1 := by nlinarith have hcut : ∀ᵐ z ∂μ.prod (PlantedSymmetry.gaussianNoiseLaw (TreeVertex d) t), ∀ p : TreeShell d (k + 1), ∀ w : ReducedTail (cutLastColor p) L, cutContinuationProduct (gaussianInwardConeFactor μ t (Real.tanh beta) z) p w ≤ Real.exp (4 * beta / (1 - Real.tanh beta)) * inwardContinuationProduct μ t (Real.tanh beta) p w (Finset.Ico (k + 1) (k + 1 + L)) z := ae_all_iff.mpr (fun p => ae_all_iff.mpr (fun w => cutContinuationProduct_gaussianInwardConeFactor_le_cut hμ hbeta t p w)) filter_upwards [hcut] with z hz refine (average_rootPathProduct_le_continuation_average (gaussianInwardConeFactor μ t (Real.tanh beta) z) (fun v => gaussianInwardConeFactor_mem_Icc μ t hθ z v) I L).trans ?_ calc _ ≤ (∑ p ∈ I, (∑ w : ReducedTail (cutLastColor p) L, Real.exp (4 * beta / (1 - Real.tanh beta)) * inwardContinuationProduct μ t (Real.tanh beta) p w (Finset.Ico (k + 1) (k + 1 + L)) z) / (((d - 1) ^ L : ℕ) : ℝ)) / (I.card : ℝ) := by apply div_le_div_of_nonneg_right _ (Nat.cast_nonneg _) apply Finset.sum_le_sum intro p hp apply div_le_div_of_nonneg_right _ (Nat.cast_nonneg _) exact Finset.sum_le_sum (fun w _ => hz p w) _ = _ := by simp only [inwardContinuationAverage, card_reducedTail, ← Finset.mul_sum, mul_div_assoc] theorem measure_gaussian_inward_path_average_ge_le {d k : ℕ} {beta : ℝ} {μ : Measure (TreeVertex d → IsingSpin)} (hμ : HasFreeIsingLaw (Real.tanh beta) μ) (hbeta : 0 ≤ beta) (hd : 2 ≤ d) (t : ℝ≥0) (I : Finset (TreeShell d (k + 1))) (hI : I.Nonempty) (L : ℕ) {τ : ℝ} (hτ : 0 ≤ τ) (hmean : ∀ p ∈ I, (∫ z, inwardContinuationAverage μ t (Real.tanh beta) p L (Finset.Ico (k + 1) (k + 1 + L)) z ∂μ.prod (PlantedSymmetry.gaussianNoiseLaw (TreeVertex d) t)) ≤ τ / 4) : (μ.prod (PlantedSymmetry.gaussianNoiseLaw (TreeVertex d) t)) {z | Real.exp (4 * beta / (1 - Real.tanh beta)) * τ ≤ (∑ v ∈ cutDescendants I L, rootPathProduct (gaussianInwardConeFactor μ t (Real.tanh beta) z) v.1) / ((cutDescendants I L).card : ℝ)} ≤ ENNReal.ofReal (Real.exp (-(I.card : ℝ) * τ ^ 2 / 2)) := by classical let := hμ.isProbabilityMeasure have hθ0 := tanh_nonneg_of_beta_nonneg hbeta have hθ1 := Real.tanh_lt_one beta have hθ : Real.tanh beta ^ 2 ≤ 1 := by nlinarith choose F hF hb heq using (fun p : TreeShell d (k + 1) => exists_inwardContinuationAverage_channel hd μ t hθ p L (Finset.Ico (k + 1) (k + 1 + L))) have hmeanF (p : TreeShell d (k + 1)) (hp : p ∈ I) : (∫ z, F p (globalCone p.1 z) ∂μ.prod (Measure.infinitePi (fun _ : TreeVertex d => gaussianReal 0 t))) ≤ τ / 4 := by simpa only [heq, PlantedSymmetry.gaussianNoiseLaw] using hmean p hp have hcon := hμ.measure_cutLevel_average_ge_le (by linarith : -1 ≤ Real.tanh beta) hθ1.le (k + 1) (fun _ : TreeVertex d => gaussianReal 0 t) F hF hb I hI hτ hmeanF change (μ.prod (PlantedSymmetry.gaussianNoiseLaw (TreeVertex d) t)) {z | τ ≤ (∑ p ∈ I, F p (globalCone p.1 z)) / (I.card : ℝ)} ≤ _ at hcon refine le_trans ?_ hcon apply measure_mono_ae filter_upwards [average_gaussianInwardConeFactor_le_cut_average hμ hbeta t I L] with z hz intro h simp only [heq] at hz exact (mul_le_mul_iff_right₀ (Real.exp_pos _)).mp (h.trans hz) theorem measure_gaussian_inward_restricted_path_average_ge_le_double_exp {d : ℕ} {beta : ℝ} {μ : Measure (TreeVertex d → IsingSpin)} (hμ : HasFreeIsingLaw (Real.tanh beta) μ) (hbeta : 0 ≤ beta) (hd : 3 ≤ d) (hcritical : ((d - 1 : ℕ) : ℝ) * Real.tanh beta ^ 2 = 1) {t : ℝ≥0} (ht : 0 < t) (forbidden : Finset (Fin d)) (hforbidden : forbidden.card ≤ 1) (k L : ℕ) {η ℓ : ℝ} (hη0 : 0 ≤ η) (hℓ : 0 ≤ ℓ) (hη : η ≤ ComponentSignalAttenuation.lossCoefficient (Real.tanh beta) / 4) (hηlog : η ≤ Real.log ((d : ℝ) - 1) / 8) (hlevel : ℓ / 2 - 1 ≤ (k + 1 : ℕ)) (htail : ℓ / 2 ≤ (L : ℝ)) (hlength : 8 * Real.log 2 ≤ ComponentSignalAttenuation.lossCoefficient (Real.tanh beta) * (min (Real.sqrt (t : ℝ)) 1 * ℓ)) : (μ.prod (PlantedSymmetry.gaussianNoiseLaw (TreeVertex d) t)) {z | Real.exp (4 * beta / (1 - Real.tanh beta)) * Real.exp (-(η * min (Real.sqrt (t : ℝ)) 1) * ℓ) ≤ (∑ v ∈ cutDescendants (avoidingTreeCut forbidden k) L, rootPathProduct (gaussianInwardConeFactor μ t (Real.tanh beta) z) v.1) / ((cutDescendants (avoidingTreeCut forbidden k) L).card : ℝ)} ≤ ENNReal.ofReal (Real.exp (-(((d : ℝ) - 1) ^ (ℓ / 4)) / (2 * ((d : ℝ) - 1)))) := by have hθ0 : 0 < Real.tanh beta := by have hn := tanh_nonneg_of_beta_nonneg hbeta by_contra h have hz : Real.tanh beta = 0 := le_antisymm (not_lt.mp h) hn simp [hz] at hcritical have hθ1 := Real.tanh_lt_one beta have hc := (ComponentSignalAttenuation.lossCoefficient_pos hθ0 hθ1).le have hω : 0 ≤ min (Real.sqrt (t : ℝ)) 1 := le_min (Real.sqrt_nonneg _) (by norm_num) have hmean (p : TreeShell d (k + 1)) : (∫ z, inwardContinuationAverage μ t (Real.tanh beta) p L (Finset.Ico (k + 1) (k + 1 + L)) z ∂μ.prod (PlantedSymmetry.gaussianNoiseLaw (TreeVertex d) t)) ≤ Real.exp (-(η * min (Real.sqrt (t : ℝ)) 1) * ℓ) / 4 := by refine (integral_inwardContinuationAverage_le_exp_critical hμ hbeta hd hcritical ht p L).trans ?_ calc _ ≤ Real.exp (-ComponentSignalAttenuation.lossCoefficient (Real.tanh beta) * (min (Real.sqrt (t : ℝ)) 1 * ℓ) / 2) := by apply Real.exp_le_exp.mpr have hm := mul_le_mul_of_nonneg_left htail (mul_nonneg hc hω) nlinarith _ ≤ Real.exp (-η * (min (Real.sqrt (t : ℝ)) 1 * ℓ)) / 4 := attenuation_le_quarter_threshold (mul_nonneg hω hℓ) hη hlength _ = _ := by ring_nf have hq : η * min (Real.sqrt (t : ℝ)) 1 ≤ Real.log ((d : ℝ) - 1) / 8 := (mul_le_of_le_one_right hη0 (min_le_right _ _)).trans hηlog have hd2 : 2 ≤ d := by omega have hdR : (2 : ℝ) ≤ d := by exact_mod_cast hd2 exact (measure_gaussian_inward_path_average_ge_le hμ hbeta hd2 t (avoidingTreeCut forbidden k) (avoidingTreeCut_nonempty hd2 forbidden hforbidden k) L (Real.exp_pos _).le (fun p _ => hmean p)).trans (ENNReal.ofReal_le_ofReal (BoundedAverageConcentration.exponential_card_bound (by linarith) hℓ hq (rpow_le_card_avoidingTreeCut hd2 forbidden hforbidden k hlevel))) end Problem367 end end OAI