StoppedRGODepth: mathematical reading route

Measurable positive source variance, actual finite-program construction, pathwise depth, kernel-support induction and terminal almost-everywhere output identity.

Braces mark parameters Lean can infer; square brackets request structures such as a measurable space or probability measure. Named hypotheses are mathematical premises, not facts established by this declaration. Section parameters are described in the mathematical hypotheses above; the module link retains their exact source context.

theorem stopped_rgo_depth (μ : Measure E) [IsProbabilityMeasure μ]
    (η : PS → ℝ) (hηm : Measurable η) (κ c q Δ γ C : ℝ)
    (hκ : 1 ≤ κ) (hc : 0 < c) (hc1 : c < 1/4)
    (hη : ∀ s, 0 < η s ∧ η s ≤ c) (hd : 0 < Module.finrank ℝ E) (hq : 2 ≤ q)
    (hΔ : 0 < Δ) (hΔ1 : Δ ≤ 1/2) (hγ : 0 < γ) (hγ1 : γ ≤ 1) (hC : 8 ≤ C)
    (s0 : PS) (Q Lterm : Kernel PS E) [IsMarkovKernel Q] [IsMarkovKernel Lterm] :
    let K := fun r : ℝ => (1+r)/(κ⁻¹+r)
    let τ := fun r : ℝ => if 2 ≤ K r then K r else c
    let a := fun s : PS => (η s+τ s.1)/(1+s.1)
    let L := q+Real.log (K s0.1*Module.finrank ℝ E*q/Δ)
    let B := γ/(Real.sqrt (Module.finrank ℝ E*L)+L)
    let J := Nat.ceil (C*Real.log (Real.exp 1*K s0.1/B))
    ∃ (F : PS × E → PS) (P : Kernel PS PS) (R : ℕ → Kernel PS E),
      (∀ s y, ((F (s,y)).1 : ℝ) = (s.1 : ℝ)+(a s)⁻¹ ∧
        (F (s,y)).2 =
         (((s.1 : ℝ)+(a s)⁻¹)⁻¹ • ((s.1 : ℝ) • s.2.1+(a s)⁻¹ • y),
          s.2.2.1+1,fun n => Nat.casesOn n y s.2.2.2)) ∧
      IsMarkovKernel P ∧ (∀ n, IsMarkovKernel (R n)) ∧
      (∀ s, P s = if B⁻¹ ≤ (s.1 : ℝ) then Measure.dirac s else
        (Q s).map (fun y => F (s,y))) ∧
      (∀ n, R n = Lterm ∘ₖ (P^n)) ∧
      0 < B ∧ (P^J) s0 {s | B⁻¹ ≤ (s.1 : ℝ)} = 1 ∧
      (∀ m, R (J+m) s0 = R J s0) ∧
      (J : ℝ) ≤ (3*C+(C/2)*Real.log (1/γ))*L := by
  classical
  let statePath (G : PS × E → PS) (s : PS) (ys : ℕ → E) : ℕ → PS :=
    Nat.rec s (fun n x => G (x,ys n))
  have statePath_shift (G : (PS) × E → (PS)) (n : ℕ) (s : (PS)) (ys : ℕ → E) :
      statePath G s ys (n+1) = statePath G (G (s,ys 0)) (fun k => ys (k+1)) n := by
    induction n with
    | zero => rfl
    | succ n ih =>
      change G (statePath G s ys (n+1),ys (n+1)) =
        G (statePath G (G (s,ys 0)) (fun k => ys (k+1)) n,ys (n+1))
      rw [ih]
  have path_endpoint_mass (G : (PS) × E → (PS)) (hG : Measurable G)
      (Q : Kernel (PS) E) [IsMarkovKernel Q] (P : Kernel (PS) (PS)) [IsMarkovKernel P]
      (hP : ∀ s, P s = (Q s).map (fun y => G (s,y)))
      (D : Set (PS)) (hD : MeasurableSet D) (n : ℕ) (s : (PS))
      (hpaths : ∀ ys : ℕ → E, statePath G s ys n ∈ D) [Nonempty E] :
      (P^n) s D = 1 := by
    have hp (k : ℕ) : IsMarkovKernel (P^k) := by
      induction k with
      | zero => change IsMarkovKernel Kernel.id; infer_instance
      | succ k ih =>
        let := ih
        rw [pow_succ]
        change IsMarkovKernel ((P^k) ∘ₖ P)
        infer_instance
    induction n generalizing s with
    | zero =>
      classical
      have hs := hpaths (fun _ => Classical.choice inferInstance)
      change Measure.dirac s D = 1
      exact Measure.dirac_apply_of_mem hs
    | succ n ih =>
      let := hp n
      rw [pow_succ]
      change ((P^n) ∘ₖ P) s D = 1
      rw [Kernel.comp_apply' _ _ _ hD,hP,
        lintegral_map ((P^n).measurable_coe hD)
          (show Measurable (fun y : E => G (s,y)) from hG.comp measurable_prodMk_left)]
      have hall (y : E) : (P^n) (G (s,y)) D = 1 := by
        apply ih
        intro ys
        have hh := hpaths (fun k => Nat.casesOn k y ys)
        rw [statePath_shift] at hh
        exact hh
      simp_rw [hall]
      simp
  have absorbed_precision_path (b : (PS) → ℝ) (η : (PS) → ℝ) (τ : ℝ → ℝ)
      (F : (PS) × E → (PS)) (threshold : ℝ)
      (hF : ∀ s y, b (F (s,y)) = b s + ((η s + τ (b s))/(1+b s))⁻¹)
      (s0 : (PS)) (ys : ℕ → E) :
      let G : (PS) × E → (PS) := fun p => if threshold ≤ b p.1 then p.1 else F p
      let x := statePath G s0 ys
      let r : ℕ → ℝ := Nat.rec (b s0) (fun n r => r + ((η (x n)+τ r)/(1+r))⁻¹)
      ∀ n, threshold ≤ b (x n) ∨ b (x n) = r n := by
    classical
    intro G x r n
    induction n with
    | zero => exact Or.inr rfl
    | succ n ih =>
      have hx : x (n+1) = G (x n,ys n) := rfl
      by_cases hs : threshold ≤ b (x n)
      · left
        rw [hx,show G (x n,ys n) = x n from if_pos hs]
        exact hs
      · right
        have heq := ih.resolve_left hs
        rw [hx,show G (x n,ys n) = F (x n,ys n) from if_neg hs,hF]
        change b (x n) + ((η (x n)+τ (b (x n)))/(1+b (x n)))⁻¹ =
          r n + ((η (x n)+τ (r n))/(1+r n))⁻¹
        rw [heq]
  have pathwise_log_depth (b : (PS) → ℝ) (η : (PS) → ℝ) (F : (PS) × E → (PS))
      (κ c q Δ γ C : ℝ) (d : ℕ) (s0 : (PS))
      (hκ : 1 ≤ κ) (hc : 0 < c) (hc1 : c < 1/4) (hb0 : 0 ≤ b s0)
      (hη : ∀ s, 0 < η s ∧ η s ≤ c) (hd : 0 < d) (hq : 2 ≤ q)
      (hΔ : 0 < Δ) (hΔ1 : Δ ≤ 1/2) (hγ : 0 < γ) (hγ1 : γ ≤ 1) (hC : 8 ≤ C) :
      let K := fun r : ℝ => (1+r)/(κ⁻¹+r)
      let τ := fun r : ℝ => if 2 ≤ K r then K r else c
      let L := q+Real.log (K (b s0)*d*q/Δ)
      let B := γ/(Real.sqrt (d*L)+L)
      let J := Nat.ceil (C*Real.log (Real.exp 1*K (b s0)/B))
      (∀ s y, b (F (s,y)) = b s + ((η s+τ (b s))/(1+b s))⁻¹) →
      ∀ ys : ℕ → E,
        let G : (PS) × E → (PS) := fun p => if B⁻¹ ≤ b p.1 then p.1 else F p
        B⁻¹ ≤ b (statePath G s0 ys J) := by
    classical
    intro K τ L B J hF ys G
    let x := statePath G s0 ys
    let r : ℕ → ℝ := Nat.rec (b s0) (fun n r => r + ((η (x n)+τ r)/(1+r))⁻¹)
    have hh := AutoSamplingTheory.ExampleCases.SmoothedPicardHMC.LogarithmicDepth.terminal_depth
      (η := fun n => η (x n)) hκ hc hc1 hb0 (fun n => hη (x n)) hd hq hΔ hΔ1 hγ hγ1 hC
    change 2 ≤ L ∧ (0 < B ∧ B ≤ 1) ∧ 0 < J ∧ 0 < (r J)⁻¹ ∧ (r J)⁻¹ ≤ B ∧ _ at hh
    obtain ⟨hL,hB,hJ,hrpos,hrB,hJbound⟩ := hh
    have hr : 0 < r J := inv_pos.mp hrpos
    have hbJ : B⁻¹ ≤ r J := by
      rw [inv_eq_one_div]
      apply (div_le_iff₀ hB.1).2
      have hm : 1 ≤ B * r J := (div_le_iff₀ hr).1 (by simpa [one_div] using hrB)
      simpa [mul_comm] using hm
    have hinv := absorbed_precision_path b η τ F B⁻¹ hF s0 ys J
    change B⁻¹ ≤ b (x J) ∨ b (x J) = r J at hinv
    rcases hinv with h | h
    · exact h
    · change B⁻¹ ≤ b (x J)
      rw [h]
      exact hbJ
  have source_variance (b : (PS) → ℝ) (hb : Measurable b) (hb0 : ∀ s, 0 ≤ b s)
      (η : (PS) → ℝ) (hη : Measurable η) (hη0 : ∀ s, 0 < η s) (κ c : ℝ) (hc : 0 < c) :
      let K := fun s => (1+b s)/(κ⁻¹+b s)
      let τ := fun s => if 2 ≤ K s then K s else c
      let a := fun s => (η s+τ s)/(1+b s)
      Measurable a ∧ ∀ s, 0 < a s := by
    intro K τ a
    have hK : Measurable K := by dsimp [K]; fun_prop
    have hτ : Measurable τ := Measurable.ite
      (measurableSet_le measurable_const hK) hK measurable_const
    refine ⟨(hη.add hτ).div (measurable_const.add hb),?_⟩
    intro s
    have ht : 0 < τ s := by
      dsimp only [τ]
      split_ifs with h
      · linarith
      · exact hc
    exact div_pos (add_pos (hη0 s) ht) (by linarith [hb0 s])
  have finite_output_stability (P : Kernel (PS) (PS)) [IsMarkovKernel P]
      (L : Kernel (PS) E) [IsMarkovKernel L] (R : ℕ → Kernel (PS) E)
      (hR : ∀ n, R n = L ∘ₖ (P^n)) (D : Set (PS)) (hD : MeasurableSet D)
      (hstop : ∀ n s, s ∈ D → R n s = L s) (J : ℕ) (s : (PS))
      (hmass : (P^J) s D = 1) : ∀ m, R (J+m) s = R J s := by
    have hp (k : ℕ) : IsMarkovKernel (P^k) := by
      induction k with
      | zero => change IsMarkovKernel Kernel.id; infer_instance
      | succ k ih =>
        let := ih
        rw [pow_succ]
        change IsMarkovKernel ((P^k) ∘ₖ P)
        infer_instance
    let := hp J
    have hae : ∀ᵐ t ∂((P^J) s), t ∈ D := (mem_ae_iff_prob_eq_one hD).2 hmass
    intro m
    let := hp m
    rw [hR (J+m),Nat.add_comm J m,pow_add]
    change (L ∘ₖ ((P^m) ∘ₖ (P^J))) s = R J s
    rw [← Kernel.comp_assoc,← hR m,hR J,Kernel.comp_apply,Kernel.comp_apply]
    apply Measure.bind_congr_right
    filter_upwards [hae] with t ht
    exact hstop m t ht
  intro K τ a L B J
  have hb : Measurable (fun s : PS => (s.1 : ℝ)) := by fun_prop
  have hva := source_variance (fun s : PS => (s.1 : ℝ)) hb (fun s => s.1.coe_nonneg)
    η hηm (fun s => (hη s).1) κ c hc
  change Measurable a ∧ (∀ s, 0 < a s) at hva
  obtain ⟨ha,ha0⟩ := hva
  have hdepth := AutoSamplingTheory.ExampleCases.SmoothedPicardHMC.LogarithmicDepth.terminal_depth
    (r₀ := (s0.1 : ℝ)) (η := fun _ => c) hκ hc hc1 s0.1.coe_nonneg
    (fun _ => ⟨hc,le_rfl⟩) hd hq hΔ hΔ1 hγ hγ1 hC
  change 2 ≤ L ∧ (0 < B ∧ B ≤ 1) ∧ 0 < J ∧ _ ∧ _ ∧
    (J : ℝ) ≤ (3*C+(C/2)*Real.log (1/γ))*L at hdepth
  have hB := hdepth.2.1.1
  have hJB := hdepth.2.2.2.2.2
  let threshold : ℝ≥0 := ⟨B⁻¹,le_of_lt (inv_pos.mpr hB)⟩
  let F : PS × E → PS := fun p =>
      (⟨(p.1.1 : ℝ)+(a p.1)⁻¹, add_nonneg p.1.1.coe_nonneg (le_of_lt (inv_pos.mpr (ha0 p.1)))⟩,
       ((p.1.1 : ℝ)+(a p.1)⁻¹)⁻¹ • ((p.1.1 : ℝ) • p.1.2.1+(a p.1)⁻¹ • p.2),
       p.1.2.2.1+1, fun n => Nat.casesOn n p.2 p.1.2.2.2)
  have hF : Measurable F := by
    apply Measurable.prodMk
    · exact Measurable.subtype_mk (by fun_prop)
    · apply Measurable.prodMk
      · fun_prop
      · apply Measurable.prodMk
        · fun_prop
        · apply measurable_pi_lambda
          intro n
          cases n <;> fun_prop
  obtain ⟨T,H,hT,hH,hTf,hHf,hprog⟩ :=
    AutoSamplingTheory.ExampleCases.SmoothedPicardHMC.FiniteRGOProgram.finite_rgo_program μ a ha ha0 threshold
  obtain ⟨P,R,hP,hR,hPf,hzero,hrec,hstop,hpath,hideal⟩ := hprog Q Lterm inferInstance inferInstance
  let := hP
  let D : Set PS := {s | B⁻¹ ≤ (s.1 : ℝ)}
  have hD : MeasurableSet D := measurableSet_le measurable_const hb
  let G : PS × E → PS := fun p => if B⁻¹ ≤ (p.1.1 : ℝ) then p.1 else F p
  have hG : Measurable G := Measurable.ite
    (measurableSet_le measurable_const (hb.comp measurable_fst)) measurable_fst hF
  have hcompare (s : PS) : (threshold ≤ s.1) ↔ B⁻¹ ≤ (s.1 : ℝ) := Iff.rfl
  have hPf' (s : PS) : P s = if B⁻¹ ≤ (s.1 : ℝ) then Measure.dirac s else
      (Q s).map (fun y => F (s,y)) := by
    rw [hPf]
    rfl
  have hPmap (s : PS) : P s = (Q s).map (fun y => G (s,y)) := by
    rw [hPf']
    by_cases hs : B⁻¹ ≤ (s.1 : ℝ)
    · simp only [if_pos hs,G]
      simp [Measure.map_const]
    · simp only [if_neg hs,G]
  have hmass : (P^J) s0 D = 1 := by
    apply path_endpoint_mass G hG Q P hPmap D hD J s0
    intro ys
    exact pathwise_log_depth (fun s : PS => (s.1 : ℝ)) η F κ c q Δ γ C
      (Module.finrank ℝ E) s0 hκ hc hc1 s0.1.coe_nonneg hη hd hq hΔ hΔ1 hγ hγ1 hC
      (fun _ _ => rfl) ys
  have hRstep (n : ℕ) : R (n+1) = R n ∘ₖ P := by
    ext s t ht
    rw [Kernel.comp_apply,hPf,hrec]
    by_cases hs : threshold ≤ s.1
    · rw [if_pos hs,if_pos hs,Measure.dirac_bind (R n).measurable,hstop n s hs]
    · rw [if_neg hs,if_neg hs]
      change ((Q s).bind (fun y => R n (F (s,y)))) t =
        (((Q s).map (fun y => F (s,y))).bind (R n)) t
      rw [Measure.bind_apply ht (show AEMeasurable (fun y : E => R n (F (s,y))) (Q s)
          from ((R n).measurable.comp (hF.comp measurable_prodMk_left)).aemeasurable),
        Measure.bind_apply ht (R n).aemeasurable]
      exact (lintegral_map ((R n).measurable_coe ht)
        (show Measurable (fun y : E => F (s,y)) from hF.comp measurable_prodMk_left)).symm
  have hRp (n : ℕ) : R n = Lterm ∘ₖ (P^n) := by
    induction n with
    | zero =>
      change R 0 = Lterm ∘ₖ Kernel.id
      rw [Kernel.comp_id,hzero]
    | succ n ih =>
      rw [hRstep,ih,pow_succ]
      exact Kernel.comp_assoc Lterm (P^n) P
  refine ⟨F,P,R,fun _ _ => ⟨rfl,rfl⟩,hP,hR,hPf',hRp,hB,hmass,?_,hJB⟩
  exact finite_output_stability P Lterm R hRp D hD
    (fun n s hs => hstop n s ((hcompare s).2 hs)) J s0 hmass


end AutoSamplingTheory.ExampleCases.SmoothedPicardHMC.StoppedRGODepth

Exact module and namespace context