openAI-math-dataset / lean /OAI /Dynamics /StandardMap /BridgeCritical.lean
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Add lean source and binary files (part 64)
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import OAI.Dynamics.StandardMap.LimitCancellation
namespace OAI
open MeasureTheory Set
open scoped ENNReal BigOperators
open MeasureTheory Set Filter Metric
open scoped ENNReal Topology CompactlySupported Classical
namespace StandardMapEntropy
namespace CriticalScaleSequence
lemma aligned_observation_bound (S : CriticalScaleSequence) {R : ℕ}
(p : ℕ) (s t : Fin R → DyadicTime) (a : Fin R → ℤ) (m : Fin R → ℕ) (hm : ∀j,0< m j)
(hs : ∀j,((2^p:ℕ):ℝ)*(s j:ℝ)=(a j:ℝ))
(ht : ∀j,((2^p:ℕ):ℝ)*(t j:ℝ)=(a j:ℝ)+(m j:ℝ))
(F : (Fin R → ℝ) → ℝ) (L : NNReal) (hF : LipschitzWith L F) (hFz : F 0=0) :
∃ C : ℝ,0≤ C ∧ ∀i l,p+l≤ S.exponent i →
(∫d,arrayObservation s t F d ∂sampleLaw (S.parameter i) (S.positive i).le (2^(p+l)) (by positivity))≤ C*S.epsilon i := by
obtain ⟨C,hC,hb⟩ := S.observation_scaled_bound p a m hm F L hF hFz
refine ⟨C,hC,?_⟩
intro i l hl
rw [integral_observable_sample _ (S.positive i).le _ (by positivity) s t (fun j => a j*2^l) (fun j => m j*2^l)
(fun j => Nat.mul_pos (hm j) (by positivity)) (fun j => aligned_scale p l (s j) (a j) (hs j))
(fun j => aligned_scale_end p l (t j) (a j) (m j) (ht j)) F hF.continuous]
exact hb i l hl
lemma bridge_grid_mean_bounds (S : CriticalScaleSequence) {Rw Rh : ℕ} (B : BridgeGrid Rw Rh)
(Fw : (Fin Rw → ℝ) → ℝ) (Fh : (Fin Rh → ℝ) → ℝ) (L : NNReal)
(hFw : LipschitzWith L Fw) (hFh : LipschitzWith L Fh) (hFwz : Fw 0=0) (hFhz : Fh 0=0) :
∃ C : ℝ,0≤ C ∧ ∀i l,B.p+l≤ S.exponent i →
(∫d,arrayObservation B.sw B.tw Fw d ∂sampleLaw (S.parameter i) (S.positive i).le (2^(B.p+l)) (by positivity))≤ C*S.epsilon i ∧
(∫d,arrayObservation B.sh B.th Fh d ∂sampleLaw (S.parameter i) (S.positive i).le (2^(B.p+l)) (by positivity))≤ C*S.epsilon i := by
obtain ⟨Cw,hCw,hw⟩ := S.aligned_observation_bound B.p B.sw B.tw (fun j => B.aw j+B.b) B.nw B.pw B.hsw B.htw Fw L hFw hFwz
obtain ⟨Ch,hCh,hh⟩ := S.aligned_observation_bound B.p B.sh B.th (fun j => B.ah j+B.b) B.nh B.ph B.hsh B.hth Fh L hFh hFhz
refine ⟨max Cw Ch,hCw.trans (le_max_left _ _),?_⟩
intro i l hl
exact ⟨(hw i l hl).trans (mul_le_mul_of_nonneg_right (le_max_left _ _) (S.epsilon_pos i).le),
(hh i l hl).trans (mul_le_mul_of_nonneg_right (le_max_right _ _) (S.epsilon_pos i).le)⟩
lemma bridge_integral_tendsto (S : CriticalScaleSequence) {Rw Rh : ℕ} (B : BridgeGrid Rw Rh)
(p len : ℕ → ℕ) (hp : Tendsto p atTop atTop)
(hpl : ∀i j,j<len i → p i+j≤ S.exponent i)
(Fw : (Fin Rw → ℝ) → ℝ) (Fh : (Fin Rh → ℝ) → ℝ) (L : NNReal)
(hFw : LipschitzWith L Fw) (hFh : LipschitzWith L Fh) (hFwz : Fw 0=0) (hFhz : Fh 0=0)
(hW0 : ∀d,0≤ arrayObservation B.sw B.tw Fw d) (hH0 : ∀d,0≤ arrayObservation B.sh B.th Fh d)
(T : ℝ) (hT : ∀ᶠ i in atTop,
(∫d,arrayObservation B.sw B.tw Fw d ∂scaleLaw (S.parameter i) (S.positive i).le (p i) (len i) (S.epsilon i))≤ T ∨
(∫d,arrayObservation B.sh B.th Fh d ∂scaleLaw (S.parameter i) (S.positive i).le (p i) (len i) (S.epsilon i))≤ T)
(η : ℝ) (hη : 0<η) (F : DistanceArray → ℝ) (hF : Continuous F) (hF0 : ∀d,0≤ F d)
(D : ℝ) (hD : 0≤ D) (hFW : ∀d,F d≤ D*arrayObservation B.sw B.tw Fw d)
(hzero : ∀d,¬((999/1000:ℝ)*((B.v:ℝ)-(B.u:ℝ))< d.val B.u B.v ∧ η< arrayObservation B.sh B.th Fh d) → F d=0) :
Tendsto (fun i => ∫d,F d ∂scaleLaw (S.parameter i) (S.positive i).le (p i) (len i) (S.epsilon i)) atTop (𝓝 0) := by
obtain ⟨C,hC,hmeans⟩ := S.bridge_grid_mean_bounds B Fw Fh L hFw hFh hFwz hFhz
have hlo : Tendsto (fun i => p i-B.p) atTop atTop := by
apply Filter.tendsto_atTop.2
intro n
filter_upwards [hp.eventually_ge_atTop (n+B.p)] with i hi
omega
have hn : Tendsto (fun i => ((B.N*2^(p i-B.p):ℕ):ℝ)) atTop atTop := by
have hpow : Tendsto (fun i => (2:ℝ)^(p i-B.p)) atTop atTop := (tendsto_pow_atTop_atTop_of_one_lt (by norm_num : (1:ℝ)<2)).comp hlo
simpa only [Nat.cast_mul,Nat.cast_pow,Nat.cast_ofNat] using hpow.const_mul_atTop (show (0:ℝ)<B.N by exact_mod_cast (show 0<B.N from by have := B.large; omega))
have hinv : Tendsto (fun i => 1/((B.N*2^(p i-B.p):ℕ):ℝ)) atTop (𝓝 0) := tendsto_const_nhds.div_atTop hn
have hdelta : ∀ᶠ i in atTop,1/((B.N*2^(p i-B.p):ℕ):ℝ)<η/2 := hinv.eventually_lt_const (by linarith)
have hc := S.parameter_tendsto.eventually eventually_bridgeScalarControl
have hs := S.parameter_tendsto.eventually (eventually_bridge_observation_scalars (L:ℝ))
have hM := (tendsto_growthBase.comp S.parameter_tendsto).eventually_ge_atTop ((2:ℝ)^10)
apply squeeze_zero' (Eventually.of_forall (fun i => integral_nonneg hF0))
· filter_upwards [hp.eventually_ge_atTop B.p,hdelta,hc,hs,hM,hT] with i hi hdi hci hsi hMi hTi
have he : B.p+(p i-B.p)=p i := Nat.add_sub_of_le hi
have hb := B.scale_bound (S.parameter i) hci (p i-B.p) (len i) (S.epsilon i) (S.epsilon_pos i)
Fw Fh L hFw hFh hW0 hH0 hsi.1 hsi.2 hMi η hη hdi F hF D hD hFW hzero C T hC
(fun j hj => by simpa only [←Nat.add_assoc,he] using (hmeans i (p i-B.p+j) (by have := hpl i j hj; omega)).1)
(fun j hj => by simpa only [←Nat.add_assoc,he] using (hmeans i (p i-B.p+j) (by have := hpl i j hj; omega)).2)
(by simpa only [he] using hTi)
simpa only [he] using hb
· have ht := (S.epsilon_tendsto.const_mul T).add (hinv.const_mul 2)
have hh := ht.const_mul ((D*bridgeRealConstant/(η/2))*C)
convert! hh using 1 <;> simp only [mul_zero,add_zero,mul_one_div]
end CriticalScaleSequence
end StandardMapEntropy
end OAI