import OAI.Probability.InvariantIsing.Haar.HaarFrobeniusGeometry import OAI.Probability.InvariantIsing.Haar.HaarGroupTopology namespace OAI /-! Haar convolution by a positive polynomial kernel preserves Lipschitz constants. -/ noncomputable section open Matrix MvPolynomial MeasureTheory Set open scoped BigOperators namespace InvariantIsing lemma continuous_haarKernelBase {N : ℕ} (U : SpecialOrthogonal N) : Continuous (haarKernelBase U) := by unfold haarKernelBase apply continuous_const.add apply continuous_finsetSum intro i _ apply continuous_finsetSum intro j _ exact continuous_const.mul ((continuous_apply j).comp ((continuous_apply i).comp continuous_subtype_val)) def haarKernelMass {N : ℕ} (μ : Measure (SpecialOrthogonal N)) (n : ℕ) : ℝ := ∫ V, (haarKernelBase 1 V)^n ∂μ def haarKernelApprox {N : ℕ} (μ : Measure (SpecialOrthogonal N)) (f : SpecialOrthogonal N → ℝ) (n : ℕ) : MatrixPolynomial N := (haarKernelMass μ n)⁻¹ • haarKernelAverage μ f n lemma haarKernelMass_pos {N : ℕ} (hN : 0 < N) (μ : Measure (SpecialOrthogonal N)) [IsProbabilityMeasure μ] [μ.IsMulLeftInvariant] (n : ℕ) : 0 < haarKernelMass μ n := by apply integral_pos_of_integrable_nonneg_nonzero (x := (1 : SpecialOrthogonal N)) ((continuous_haarKernelBase 1).pow n) (continuous_haar_integrable μ _ ((continuous_haarKernelBase 1).pow n)) (fun V => pow_nonneg (haarKernelBase_nonneg 1 V) n) change (haarKernelBase 1 1)^n ≠ 0 rw [haarKernelBase_self] exact (pow_pos (mul_pos (by norm_num) (Nat.cast_pos.mpr hN)) n).ne' lemma haarKernelAverage_convolution {N : ℕ} (μ : Measure (SpecialOrthogonal N)) [IsFiniteMeasure μ] [μ.IsMulLeftInvariant] (f : SpecialOrthogonal N → ℝ) (hf : Continuous f) (n : ℕ) (U : SpecialOrthogonal N) : haarPolynomialValue (haarKernelAverage μ f n) U = ∫ V, f (U*V)*(haarKernelBase 1 V)^n ∂μ := by rw [haarKernelAverage_value μ f hf] have h := integral_mul_left_eq_self (μ := μ) (fun V => f V*(haarKernelBase U V)^n) U rw [← h] apply integral_congr_ae exact ae_of_all μ fun V => by dsimp only rw [show haarKernelBase U (U*V) = haarKernelBase 1 V by simpa only [mul_one] using haarKernelBase_mul_left U 1 V] theorem haarKernelApprox_lipschitz {N : ℕ} (hN : 0 < N) (μ : Measure (SpecialOrthogonal N)) [IsProbabilityMeasure μ] [μ.IsMulLeftInvariant] (f : SpecialOrthogonal N → ℝ) (hf : Continuous f) (L : ℝ) (hL : ∀ U V, |f U-f V| ≤ L*frobeniusDistance U V) (n : ℕ) (U V : SpecialOrthogonal N) : |haarPolynomialValue (haarKernelApprox μ f n) U- haarPolynomialValue (haarKernelApprox μ f n) V| ≤ L*frobeniusDistance U V := by have hZ := haarKernelMass_pos hN μ n let k (W : SpecialOrthogonal N) := (haarKernelBase 1 W)^n have hk : Continuous k := (continuous_haarKernelBase 1).pow n have hi (U : SpecialOrthogonal N) : Integrable (fun W => f (U*W)*k W) μ := continuous_haar_integrable μ _ ((hf.comp (continuous_const.mul continuous_id)).mul hk) have he (U : SpecialOrthogonal N) : haarPolynomialValue (haarKernelApprox μ f n) U = (haarKernelMass μ n)⁻¹*(∫ W, f (U*W)*k W ∂μ) := by simp only [haarKernelApprox,haarPolynomialValue,map_smul,smul_eq_mul] change (haarKernelMass μ n)⁻¹*haarPolynomialValue (haarKernelAverage μ f n) U = _ rw [haarKernelAverage_convolution μ f hf] rw [he U,he V,← mul_sub,abs_mul,abs_of_pos (inv_pos.mpr hZ),← integral_sub (hi U) (hi V)] apply (inv_mul_le_iff₀ hZ).mpr calc |∫ W, (f (U*W)*k W-f (V*W)*k W) ∂μ| ≤ ∫ W, |f (U*W)*k W-f (V*W)*k W| ∂μ := abs_integral_le_integral_abs _ ≤ ∫ W, (L*frobeniusDistance U V)*k W ∂μ := by apply integral_mono · exact ((hi U).sub (hi V)).abs · exact (continuous_haar_integrable μ _ hk).const_mul _ · intro W dsimp only rw [← sub_mul,abs_mul,abs_of_nonneg (pow_nonneg (haarKernelBase_nonneg 1 W) n)] apply mul_le_mul_of_nonneg_right _ (pow_nonneg (haarKernelBase_nonneg 1 W) n) exact (hL (U*W) (V*W)).trans_eq (by rw [frobeniusDistance_mul_right]) _ = haarKernelMass μ n*(L*frobeniusDistance U V) := by rw [integral_const_mul,mul_comm] rfl end InvariantIsing end end OAI