import Mathlib import OAI.Probability.SKGap.Localization.UnitBall import OAI.Probability.SKGap.Gaussian.GaussianProductUpperTailLipschitz namespace OAI section noncomputable section open MeasureTheory ProbabilityTheory InformationTheory Real Set open scoped NNReal ENNReal open Filter open scoped Topology noncomputable section open Matrix Real open scoped BigOperators Matrix.Norms.Frobenius ENNReal NNReal noncomputable section open Matrix Real open scoped BigOperators Matrix.Norms.Frobenius NNReal noncomputable section open MeasureTheory ProbabilityTheory Real Set Filter open MeasureTheory.Measure open scoped ENNReal NNReal MeasureTheory Topology open MeasureTheory noncomputable section noncomputable section open MeasureTheory Set NormedSpace open scoped Topology noncomputable section open Matrix Real open scoped BigOperators Matrix.Norms.Frobenius noncomputable section open Set Real open scoped Topology noncomputable section open Matrix Set Filter open scoped Topology Matrix.Norms.Frobenius noncomputable section open Matrix NormedSpace ContinuousLinearMap open scoped Matrix.Norms.Frobenius noncomputable section open Matrix noncomputable section open MeasureTheory ProbabilityTheory Real Set open scoped ENNReal NNReal noncomputable section open MeasureTheory ProbabilityTheory InformationTheory Real Set open scoped NNReal ENNReal noncomputable section open scoped BigOperators open MeasureTheory ProbabilityTheory open Real noncomputable section open scoped BigOperators Topology open Filter Real namespace SKGap section GOEProbability variable {ι : Type*} [Fintype ι] [Nonempty ι] omit [Nonempty ι] in lemma diagonalT_memLp {r : ℝ} (hr : 0 ≤ r) {a : ι → ℝ} (ha : ∀ i, 0 ≤ a i) : MemLp (diagonalT r a) 2 (gaussianCoordinates (MatrixCoordinates ι)) := by apply memLp_sqrt_of_nonneg ((gaussianQuadratic_memLp (diagonalWeights r a)).integrable (by norm_num)) intro g apply Finset.sum_nonneg intro k _ apply mul_nonneg _ (sq_nonneg _) cases k <;> simp only [diagonalWeights] · exact le_rfl · exact mul_nonneg hr (ha _) omit [Nonempty ι] in lemma diagonalT_mean_le {r : ℝ} (hr : 0 ≤ r) {a : ι → ℝ} (ha : ∀ i, 0 ≤ a i) : (∫ g, diagonalT r a g ∂gaussianCoordinates (MatrixCoordinates ι)) ≤ sqrt (r * ∑ i, a i) := by have he (g : MatrixCoordinates ι → ℝ) : (diagonalT r a g)^2 = gaussianQuadratic (diagonalWeights r a) g := by apply sq_sqrt apply Finset.sum_nonneg intro k _ apply mul_nonneg _ (sq_nonneg _) cases k <;> simp only [diagonalWeights] · exact le_rfl · exact mul_nonneg hr (ha _) have h := integral_abs_le_sqrt_sq (diagonalT_memLp hr ha) simp_rw [he, abs_of_nonneg (show 0 ≤ diagonalT r a _ from sqrt_nonneg _)] at h have hm : (∫ g, gaussianQuadratic (diagonalWeights r a) g ∂gaussianCoordinates (MatrixCoordinates ι)) = r * ∑ i, a i := by simpa only [gaussianCoordinates, diagonalWeights_sum] using gaussianQuadratic_mean (diagonalWeights r a) rwa [hm] at h theorem goe_expected_top {j : ℝ} (hj : 0 ≤ j) : (∫ g, diagonalProcess (j/(Fintype.card ι:ℝ)) (fun _ : ι => 1) 1 0 g ∂gaussianCoordinates (MatrixCoordinates ι)) ≤ 2*sqrt j := by let r := j/(Fintype.card ι:ℝ) let a : ι → ℝ := fun _ => 1 have hr : 0 ≤ r := div_nonneg hj (by positivity) have ha : ∀ i, 0 ≤ a i := by intro; norm_num [a] have hc := gaussian_quadraticSup_comparison (continuous_diagonalOffset a 1 0) ((continuous_diagonalSqrt a).comp continuous_subtype_val) hr 1 change (∫ g, diagonalProcess r a 1 0 g ∂gaussianCoordinates (MatrixCoordinates ι)) ≤ (∫ g, diagonalLinearProcess r a 1 0 g ∂gaussianCoordinates (MatrixCoordinates ι)) at hc have hb (g) : diagonalLinearProcess r a 1 0 g ≤ 2*diagonalT r a g := by have h := diagonalLinearProcess_le (A := 1) (b := 0) hr ha (by intro; exact le_rfl) zero_le_one (by norm_num : (0:ℝ) ≤ 1) le_rfl le_rfl (by norm_num) zero_le_one g simpa only [sub_zero, one_pow, sub_self, sqrt_zero, sqrt_one, mul_one, abs_of_nonneg (show 0 ≤ diagonalT r a g from sqrt_nonneg _), zero_add] using h have hi := diagonalT_integrable hr ha calc _ ≤ ∫ g, diagonalLinearProcess r a 1 0 g ∂gaussianCoordinates (MatrixCoordinates ι) := hc _ ≤ ∫ g, 2*diagonalT r a g ∂gaussianCoordinates (MatrixCoordinates ι) := integral_mono (integrable_diagonalLinearProcess r a 1 0) (hi.const_mul 2) hb _ = 2 * ∫ g, diagonalT r a g ∂gaussianCoordinates (MatrixCoordinates ι) := integral_const_mul _ _ _ ≤ 2*sqrt (r * ∑ i, a i) := mul_le_mul_of_nonneg_left (diagonalT_mean_le hr ha) (by norm_num) _ = 2*sqrt j := by have hn : (Fintype.card ι:ℝ) ≠ 0 := by exact_mod_cast Fintype.card_ne_zero simp [a, r, hn] lemma gaussianCoordinates_preserving_neg {κ : Type*} [Fintype κ] : MeasurePreserving (fun g : κ → ℝ => -g) (gaussianCoordinates κ) (gaussianCoordinates κ) := by apply measurePreserving_pi (f := fun _ => fun x : ℝ => -x) (fun _ => gaussianReal 0 1) (fun _ => gaussianReal 0 1) intro i exact ⟨by fun_prop, by simpa using (gaussianReal_map_neg (μ := 0) (v := 1))⟩ theorem goe_top_tail {j ε : ℝ} (hj : 0 < j) (hε : 0 ≤ ε) : (gaussianCoordinates (MatrixCoordinates ι)).real {g | 2*sqrt j+ε < diagonalProcess (j/(Fintype.card ι:ℝ)) (fun _ : ι => 1) 1 0 g} ≤ Real.exp (-ε^2*(Fintype.card ι:ℝ)/(π^2*j)) := by let r := j/(Fintype.card ι:ℝ) let P := diagonalProcess r (fun _ : ι => 1) 1 0 have hn : 0 < (Fintype.card ι:ℝ) := by exact_mod_cast Fintype.card_pos have hr : 0 < r := div_pos hj hn have hL := diagonalProcess_lipschitz (a := fun _ : ι => 1) hr.le (fun _ => zero_le_one) (fun _ => le_rfl) zero_le_one (by norm_num : (0:ℝ) ≤ 1) 0 have hLp : 0 < Real.toNNReal (1*sqrt (2*r)*1) := by rw [Real.toNNReal_pos]; positivity have ht := gaussianProduct_upper_tail_lipschitz hL hLp hε have hm := goe_expected_top (ι := ι) hj.le have hs : {g | 2*sqrt j+ε < P g} ⊆ {g | ε ≤ P g - ∫ g, P g ∂gaussianCoordinates (MatrixCoordinates ι)} := by intro g hg change ε ≤ P g - ∫ g, P g ∂gaussianCoordinates (MatrixCoordinates ι) change _ < P g at hg change (∫ g, P g ∂gaussianCoordinates (MatrixCoordinates ι)) ≤ 2*sqrt j at hm linarith exact (measureReal_mono hs).trans (ht.trans_eq (by congr 1 rw [Real.coe_toNNReal _ (by positivity)] simp only [one_mul, mul_one] rw [sq_sqrt (by positivity)] dsimp [r] field_simp)) end GOEProbability end SKGap end end end end end end end end end end end end end end end end end OAI