import Mathlib import OAI.Probability.LogConcave.JetEstimates.JetMaterial namespace OAI section section noncomputable section namespace LogConcaveSampling open scoped Classical BigOperators NNReal RealInnerProductSpace lemma joint_mean_material {d : ℕ} {F : Point d → ℝ} {lam : ℝ≥0} (hF : Primitive F lam) (x : Point d) {r ρ : ℝ} (hr : 0 < r) (hlam : 0 < lam) (hl : (lam:ℝ)*r^2≤1/2) (h0 : 0≤ρ) (h1 : ρ<1) (i : Fin d) (y : Point d) : JetCalculus.mdir (1,0) jointSpace r (jointMean F x r) (jointMean F x r i) (ρ,y)= JetCalculus.gadj jointSpace (jointScore F x r) (fun k => jointU F x r ((lam:ℝ)*r) (fun _ : Unit => JetCalculus.spaceBasis d k) [()] i) (ρ,y) := by have hm := jointMean_timeSmooth hF x hr.le hl i (ρ,y) (by dsimp; linarith) h1 have ht := joint_mdir_eq_material F x r (hm.differentiableAt (by simp)) change JetCalculus.mdir (1,0) jointSpace r (jointMean F x r) (jointMean F x r i) (ρ,y)=_ at ht rw [ht] change materialScalar F x r (fun t y => inner ℝ (EuclideanSpace.basisFun (Fin d) ℝ i) (conditionalFieldMean F x r t y)) ρ y=_ rw [material_conditionalFieldMean hF x hr hlam hl h0 h1] rw [joint_gadj_slice hF x hr.le hl h0 h1 (fun k => (jointU_timeSmooth hF x hr hlam hl (fun _ : Unit => JetCalculus.spaceBasis d k) [()] (by simp) i (ρ,y) (by dsimp; linarith) h1).differentiableAt (by simp))] rfl lemma joint_mean_outer {d : ℕ} {F : Point d → ℝ} {lam : ℝ≥0} (hF : Primitive F lam) (x : Point d) {r ρ : ℝ} (hr : 0 < r) (hlam : 0 < lam) (hl : (lam:ℝ)*r^2≤1/2) (h0 : 0≤ρ) (h1 : ρ<1) (i : Fin d) (y : Point d) : JetCalculus.mdir (1,0) jointSpace r (jointMean F x r) (jointMean F x r i) (ρ,y)= ((lam:ℝ)*r)*(TensorSum.pure TensorAtom.one).outer F x r ((lam:ℝ)*r) (fun _ => i) (ρ,y) := by rw [joint_mean_material hF x hr hlam hl h0 h1] have hL : (lam:ℝ)*r≠0 := ne_of_gt (mul_pos (by exact_mod_cast hlam) hr) have he : (fun k => jointU F x r ((lam:ℝ)*r) (fun _ : Unit => JetCalculus.spaceBasis d k) [()] i)= fun k p => ((lam:ℝ)*r)*(TensorSum.pure TensorAtom.one).eval F x r ((lam:ℝ)*r) (Sum.elim (fun _ => i) (fun _ => k)) p := by funext k p rw [TensorSum.pure_eval,TensorAtom.one_eval] dsimp only [Sum.elim_inl,Sum.elim_inr] field_simp rw [he,JetCalculus.gadj_scalar_at _ _ (fun k => ((TensorSum.pure TensorAtom.one).timeSmooth hF x hr hlam hl _ (ρ,y) (by dsimp; linarith) h1).differentiableAt (by simp))] rfl theorem mean_material_representation (n : ℕ) : ∃A : TensorSum (Unit ⊕ Unit), A.weightLE (2*n) ∧ ∀{d : ℕ} {F : Point d → ℝ} {lam : ℝ≥0}, Primitive F lam → ∀(x : Point d) {r ρ : ℝ},0 < r → 0 < lam → (lam:ℝ)*r^2≤1/2 → 0≤ρ → ρ<1 → ∀(i : Fin d) (y : Point d), JetCalculus.materialIter (1,0) jointSpace r (jointMean F x r) (n+1) (jointMean F x r i) (ρ,y)= ((lam:ℝ)*r)*A.outer F x r ((lam:ℝ)*r) (fun _ => i) (ρ,y) := by induction n with | zero => refine ⟨TensorSum.pure TensorAtom.one,TensorSum.weightLE_pure _ (by rfl),?_⟩ intro d F lam hF x r ρ hr hlam hl h0 h1 i y exact joint_mean_outer hF x hr hlam hl h0 h1 i y | succ n ih => obtain ⟨A,hA,he⟩ := ih obtain ⟨B,hB,hAB⟩ := A.hasMaterial hA refine ⟨B.add (A.insertOne.scale 1 Polynomial.X),?_,?_⟩ · apply TensorSum.weightLE_add · convert hB using 1 · exact TensorSum.weightLE_mono (TensorSum.weightLE_scale (TensorSum.weightLE_insertOne hA) _ _) (by omega) · intro d F lam hF x r ρ hr hlam hl h0 h1 i y have ho := A.outer_timeSmooth hF x hr hlam hl (fun _ : Unit => i) have hs : JetCalculus.TimeSmooth (fun p => ((lam:ℝ)*r)*A.outer F x r ((lam:ℝ)*r) (fun _ => i) p) := fun p hp0 hp1 => contDiffAt_const.mul (ho p hp0 hp1) change JetCalculus.mdir (1,0) jointSpace r (jointMean F x r) (JetCalculus.materialIter (1,0) jointSpace r (jointMean F x r) (n+1) (jointMean F x r i)) (ρ,y)=_ rw [JetCalculus.mdir_congr_nonneg_time (1,0) jointSpace r (jointMean F x r) (JetCalculus.materialIter_timeSmooth _ _ _ (jointMean_timeSmooth hF x hr.le hl) (jointMean_timeSmooth hF x hr.le hl i) (n+1)) hs (fun ht0 ht1 z => he hF x hr hlam hl ht0 ht1 i z) h0 h1 y, JetCalculus.mdir_const_mul_at _ _ _ _ ((ho (ρ,y) (by dsimp; linarith) h1).differentiableAt (by simp)), TensorSum.outer_material hF x hr hlam hl h0 h1 hAB] end LogConcaveSampling end end end end OAI