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Add lean source and binary files (part 104)
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import OAI.NumberTheory.GaussianMoat.CommonBlocks
namespace OAI
namespace GaussianMoat
open scoped BigOperators Classical
open Filter
lemma eventually_length_smoothing :
∀ᶠ X : ℝ in atTop, ∀ p L N : ℝ,
0<p → p≤2*Real.exp (21/20*X) → 0≤L → L≤Real.exp (21/50*X) →
Real.exp (20*X)≤N → L≤N ∧ L/(N+1)≤((1/4)/p)/2 := by
have hh := eventually_mul_exp_le_exp (a := 147/100) (b := 20) (by norm_num) 16
filter_upwards [hh,eventually_ge_atTop (1:ℝ)] with X hh hX
intro p L N hp hp' hL hL' hN
have hNpos : 0<N+1 := by linarith only [hN,Real.exp_pos (20*X)]
have hm := mul_le_mul hp' hL' hL (by positivity : 0≤2*Real.exp (21/20*X))
rw [mul_assoc,←Real.exp_add,show 21/20*X+21/50*X=147/100*X by ring] at hm
refine ⟨hL'.trans ((Real.exp_le_exp.mpr (by linarith only [hX])).trans hN),?_⟩
apply (div_le_iff₀ hNpos).mpr
have he : ((1/4:ℝ)/p)/2=(1/8)/p := by ring
rw [he]
apply (mul_le_mul_iff_left₀ hp).mp
have heq : (1/8/p)*(N+1)*p=(N+1)/8 := by field_simp
rw [heq]
nlinarith only [hm,hh,hN]
theorem eventually_window_batch {D a δ : ℝ} {K : ℕ}
(hD : 1≤D) (ha : 0<a) (ha1 : a≤1) (haδ : 2*a≤δ)
(hgap : a≤(2:ℝ)^K*topCoefficient a) (W w : ℕ) (hw : w<W) :
∀ᶠ m : ℕ in atTop, ∀ J : ℕ → Finset ℕ,
(∀ i<W, ∀ j∈J i, ((100:ℝ)^i*Real.exp m)≤j*Real.log 2 ∧
j*Real.log 2≤21/20*((100:ℝ)^i*Real.exp m) ∧
δ*(2:ℝ)^j/Real.log ((2:ℝ)^(j+1))≤(PrimeLogMass.dyadicBatch j).card) →
(∀ i∈J w, ∀ j∈J w, i<j → i+K≤j) →
∀ j∈J w, ∀ E : ℝ, E≤(topCoefficient a/4)*(2:ℝ)^j/10000 →
Nonempty (BatchCertificate D (commonBlocks a m W J) (windowSmoothing W m)
(dyadicSieve j) (batchWordLength j) E
((topCoefficient a/4)/(10000000*Real.log ((2:ℝ)^j)))) := by
have hδ : 0<δ := by linarith only [ha,haδ]
have hc : 0<topCoefficient a/4 := by exact div_pos (topCoefficient_pos ha) (by norm_num)
have ht := windowScale_tendsto w
have hε : 0 < min (1/1024:ℝ) (Real.exp (-100)/(512*640000)) := by positivity
have herr := eventually_accurate_error (2*(100:ℝ)^w) hε
filter_upwards [eventually_window_bands hD ha.le ha1 hδ W w hw,
eventually_window_tail_bounds hD ha.le ha1 W w hw,
ht.eventually (eventually_window_top hD ha haδ hgap),
ht.eventually (eventually_integer_batch_bounds hD),
ht.eventually eventually_coverage_hazard,ht.eventually eventually_coverage_data_cost,
ht.eventually (eventually_package_displacement hc),
ht.eventually eventually_length_smoothing,herr,ht.eventually (eventually_ge_atTop (1600:ℝ))]
with m hbands htail htop hint hhazard hccost hpcost hsmooth herr hX
intro J hJ hsep j hj E hE
let X : ℝ := (100:ℝ)^w*Real.exp m
let s := dyadicSieve j
have hdata := hJ w hw j hj
have hne : s.primes.Nonempty := dyadic_dense_nonempty hδ hdata.2.2
have hpj := dyadic_prime_bounds j
have hm := dyadic_mean_bounds (by linarith only [hX]) hne hdata.1 hdata.2.1
have hTpos : (0:ℝ)<2^j := by positivity
have hT : Real.exp X≤(2:ℝ)^j := by
rw [←Real.exp_log hTpos,Real.log_pow]
exact Real.exp_le_exp.mpr hdata.1
have hprime : ∀ i : s.SignedIndex, Real.exp X≤(s.signedPrime i:ℝ) ∧
(s.signedPrime i:ℝ)≤2*Real.exp (21/20*X) := by
intro i
have hp := hpj _ (s.enumeration i.2).property
refine ⟨hT.trans hp.1,hp.2.trans ?_⟩
apply mul_le_mul_of_nonneg_left _ (by norm_num)
rw [←Real.exp_log hTpos,Real.log_pow]
exact Real.exp_le_exp.mpr hdata.2.1
obtain ⟨pre,hpre⟩ := windowBlocks_split a m J hw
obtain ⟨hband,hbsize⟩ := htop (J w) pre hsep (hJ w hw) j hj
obtain ⟨qtop⟩ := hband
obtain ⟨hmid,hbot,hbmid⟩ := hbands J hJ j hj
obtain ⟨qmid⟩ := hmid
obtain ⟨qbot⟩ := hbot
obtain ⟨_,_,_,hbotcost,hrestcost⟩ := htail J (fun i hi k hk => (hJ i hi k hk).2.1)
have herrors : (91*accurateGrid m)*s.meanLog≤ min (1/1024:ℝ) (Real.exp (-100)/(512*640000)) := by
have hh := mul_le_mul_of_nonneg_left hm.2 (by have := accurateGrid_pos m; positivity : 0≤91*accurateGrid m)
exact hh.trans (by simpa only [mul_assoc] using herr)
have hcoverage := coverage_from_scalar_guards s D X (windowSmoothing W m)
(middleBlocks a m w J) (accurateBlock m (1/20)) _ _ _ qmid qbot
(by linarith only [hm.1]) (by have := windowSmoothing_pos W m; omega)
(by linarith only [hX]) hprime
(herrors.trans (min_le_right _ _)) (herrors.trans (min_le_left _ _))
(hccost _ _ hbotcost hbmid) (fun i => hhazard _ ((Real.exp_pos X).trans_le (hprime i).1) (hprime i).2)
obtain ⟨hn,hshort,hnL,hnL',hnmax,hLmax⟩ := hint j hdata.1 hdata.2.1
have hN : Real.exp (20*X)≤(windowSmoothing W m:ℝ) := by
apply le_trans _ (windowSmoothing_bounds W m).1
apply Real.exp_le_exp.mpr
have hh := mul_le_mul_of_nonneg_right (pow_le_pow_right₀ (by norm_num : (1:ℝ)≤100) hw.le) (Real.exp_nonneg (m:ℝ))
dsimp only [X]
nlinarith only [hh]
have hNLength : batchWordLength j≤windowSmoothing W m := by
have h := hsmooth ((2:ℝ)^j) (batchWordLength j) (windowSmoothing W m) hTpos
(by
have hh : (2:ℝ)^j≤Real.exp (21/20*X) := by
rw [←Real.exp_log hTpos,Real.log_pow]
exact Real.exp_le_exp.mpr hdata.2.1
linarith only [hh,Real.exp_nonneg (21/20*X)])
(Nat.cast_nonneg _) hLmax hN
exact_mod_cast h.1
refine ⟨{
pre := pre++topBlocks a (J w)
rest := middleBlocks a m w J++accurateBlock m (1/20)
schedule := by simp only [commonBlocks,middleBlocks,hpre,List.append_assoc]
T := (2:ℝ)^j
nonempty := hne
prime_scale := hpj
large := by linarith only [hT,Real.add_one_le_exp X,hX]
mean := hm.1
b := bandSize s topParameter (a*(2:ℝ)^j) topIterations
n := packageRepetitions j
τ := 1/4
A := X/400
η := 1/100
δ := 1/10000
u := fun i => (s.signedPrime i:ℝ)*Real.exp (-X/200)
band := qtop
coverage := hcoverage
cap := qtop.size_cap (by linarith only [hm.1])
repetitions := hn
length := batchWordLength_pos j
floor := by norm_num
gain := by linarith only [hX]
exceptions := fun i => by positivity
short := fun i => hshort.trans_le (hpj _ (s.enumeration i.2).property).1
smooth_length := hNLength
smooth_error := fun i => (hsmooth _ _ _ ((Real.exp_pos X).trans_le (hprime i).1)
(hprime i).2 (Nat.cast_nonneg _) hLmax hN).2
list := ?_
rate := ?_
}⟩
· intro i
have hh := package_list_arithmetic hX hT ((Real.exp_pos X).trans_le (hprime i).1)
(hpj _ (s.enumeration i.2).property).2 hnL
dsimp only [s] at hh
convert hh using 1; congr 2; ring_nf
· apply package_rate_arithmetic (by linarith only [hX]) hTpos hc
(by simpa only [Real.log_pow] using hdata.1)
(by simpa only [Real.log_pow] using hdata.2.1) hm.2
(by convert hbsize using 1; ring) hnL'
(hpcost _ _ _ (Nat.cast_nonneg _) hnmax hrestcost hT) hE
end GaussianMoat
end OAI