roseking's picture
Add lean source and binary files (part 95)
b224933 verified
Raw History Blame Contribute Delete
26 kB
import Mathlib
import OAI.MeasureTheory.Falconer.Estimates.ActualFarBuffer
import OAI.MeasureTheory.Falconer.Geometry.BinBands
import OAI.MeasureTheory.Falconer.Model
import OAI.MeasureTheory.Falconer.Recursion.BranchTransitions
import OAI.MeasureTheory.Falconer.Recursion.InteriorPolicy
import OAI.MeasureTheory.Falconer.Recursion.Ledger
import OAI.MeasureTheory.Falconer.Recursion.PolicyDescriptors
import OAI.MeasureTheory.FalconerCompletion.Angular.PowerDivCap
import OAI.MeasureTheory.FalconerCompletion.Recursion.OccupiedDescendantPath
import OAI.MeasureTheory.FalconerCompletion.Recursion.RecursiveUnit
import OAI.MeasureTheory.FalconerCompletion.Shells.PrefixHighShell
namespace OAI
/-! Recursive policies and quantitative depth estimates. -/
open _root_.OAI.PlanarFalconer
section
section
open Set MeasureTheory
open scoped ComplexConjugate RealInnerProductSpace
namespace GraphLiteralSupport.CurrentFalconer.TreeND
open Set MeasureTheory Filter PlanarFalconer
open scoped ContDiff Classical
variable {n : ℕ}
theorem uniform_recursive_suffix_rule {sig : ℝ} (hsig : 0<sig) {J : ℕ} (hJ : 0<J) :
∃ H : ℝ,0≤H ∧ ∃ N₀ : ℕ,∀ e : RecursiveEnvironmentND n,
N₀≤e.N → e.sig=sig → e.J=J → IsFiniteMeasure e.ρ → 0<e.q →
∀ (K : ℝ),1000*(n+2:ℝ)<K →
∀ (W : Finset (List (EuclideanSpace ℝ (Fin (n+1)) →L[ℝ] EuclideanSpace ℝ (Fin (n+1)))))
(total _offset a t i : ℕ) (Ψ : ℝ → ℝ) (eps : ℝ), 0≤eps → i<e.J → t≤e.N → a+100*e.q<t →
a+t<2*policyDepth e.profile e.N e.q a t →
∀ M : ℝ, (∀ x,e.energies K W total Ψ (.D a (policyDepth e.profile e.N e.q a t) (i+1)) x →
((2:ℝ)^e.N)^(DimensionalPolicy.branchExponentND e.profile (recursiveLoss n e.C) sig sig eps
(.D a t i) .suffix (.D a (policyDepth e.profile e.N e.q a t) (i+1)))*x≤M) →
∀ x,e.energies K W total Ψ (.D a t i) x → x≤H*M := by
obtain ⟨N₀,hsuff⟩ := PositiveDistanceState.uniform_actual_suffix_reduction (d:=n+1) hsig hJ
let H : ℝ := ((bandOffsets (100*(n+2:ℝ))).card:ℝ)*((bandOffsets 3).card:ℝ)
refine ⟨H,by dsimp [H]; positivity,N₀,?_⟩
intro e hN he hJe hfin hq K hK W total offset a t i Ψ eps heps hi ht hw hside M hbound x hx
let := hfin
obtain ⟨X,Y,hX,hY,hox,hoy,hsep,rfl⟩ := hx
let f := e.distanceData a t i X Y
let p := policyDepth e.profile e.N e.q a t
let g := e.distanceData a p (i+1) X Y
let : IsFiniteMeasure g.ρ := hfin
have hf := hsuff f he hJe hN hfin K (by convert hK using 1; push_cast; ring)
(farSeparated_centre ((mem_dyadicPaths a X).mp hX) ((mem_dyadicPaths a Y).mp hY) hsep)
((mem_dyadicPaths a X).mp hX) ((mem_dyadicPaths a Y).mp hY) hq (by exact Nat.succ_le_of_lt hi) ht hw hside
have hreal := ENNReal.toReal_mono (by
apply ENNReal.mul_ne_top
· apply ENNReal.mul_ne_top
· exact ENNReal.mul_ne_top (by simp) (by simp)
· exact ENNReal.ofReal_ne_top
· exact g.coincidence_ne_top) hf
simp only [ENNReal.toReal_mul,ENNReal.toReal_natCast] at hreal
rw [ENNReal.toReal_ofReal (by positivity)] at hreal
have hcap := hbound g.coincidence.toReal (e.distance_mem K W total Ψ a p (i+1) X Y hX hY hox hoy hsep)
have hexp : e.profile p-intervalMin e.profile p t+e.C*sig≤
DimensionalPolicy.branchExponentND e.profile (recursiveLoss n e.C) sig sig eps
(.D a t i) .suffix (.D a p (i+1)) := by
simp only [DimensionalPolicy.branchExponentND,DimensionalPolicy.branchCost,BranchState.start,BranchState.finish]
have hh := mul_le_mul_of_nonneg_right (recursiveLoss_test n e.C) hsig.le
simp only [show BranchLabel.suffix ≠ .low by decide,↓reduceIte]
nlinarith
have hb := weighted_child_cap ENNReal.toReal_nonneg hexp hcap
have hf' : f.coincidence.toReal≤H*(((2:ℝ)^e.N)^(e.profile p-intervalMin e.profile p t+e.C*sig)*g.coincidence.toReal) := by
convert hreal using 1; simp only [H,f,g,RecursiveEnvironmentND.distanceData,RecursiveEnvironmentND.profile,p]; push_cast; ring_nf
exact hf'.trans (mul_le_mul_of_nonneg_left hb (by dsimp [H]; positivity))
end GraphLiteralSupport.CurrentFalconer.TreeND
end
end
section
section
open Set MeasureTheory
open scoped ComplexConjugate RealInnerProductSpace
namespace GraphLiteralSupport.CurrentFalconer.TreeND
open Set MeasureTheory Filter PlanarFalconer
open scoped ContDiff Classical
variable {n : ℕ}
theorem prefix_high_weighted_cap {sig : ℝ} (hsig : 0<sig)
(e : RecursiveEnvironmentND n) (he : e.sig=sig) [IsFiniteMeasure e.ρ]
(a t i offset Order total : ℕ) (W : Finset (List (EuclideanSpace ℝ (Fin (n+1)) →L[ℝ] EuclideanSpace ℝ (Fin (n+1)))))
(X Y : CellPath (n+1)) (K eps D M : ℝ) (hD : 1≤D) (hM : 0≤M)
(Ψ : ℝ → ℝ) (_hiΨ : Integrable Ψ) (_hΨ0 : ∀ r,0≤Ψ r)
(_hq : 0<e.q) (_ht : t≤e.N) (hw : a+100*e.q<t)
(hside : 2*policyDepth e.profile e.N e.q a t≤a+t)
(hchildren : ∀ u l,DimensionalPolicy.BranchStep e.profile e.N e.q offset sig (.D a t i) l u →
∀ x,e.energies K W total Ψ u x →
((2:ℝ)^e.N)^(DimensionalPolicy.branchExponentND e.profile (recursiveLoss n e.C) sig sig eps (.D a t i) l u)*x≤M)
(hclose : ∀ (v : ℕ) (M : ℝ),0≤M →
(∀ x,((e.distanceData a t i X Y).prefixAngularData v).toRecursiveEnvironmentND.energies K W total Ψ
(.H a t (policyDepth e.profile e.N e.q a t) v i) x → x≤M) →
(e.distanceData a t i X Y).prefixHighChildSup Order v Ψ≤D^(4*total)*M)
(j : ℕ) (hj : j∈(e.distanceData a t i X Y).prefixHighIndices) :
((2:ℝ)^e.N)^eps*(e.distanceData a t i X Y).prefixGraphFactor*
(e.distanceData a t i X Y).prefixHighChildSup Order (j+1:ℕ) Ψ≤D^(4*total)*M := by
let p := policyDepth e.profile e.N e.q a t
have hjm := Finset.mem_Ico.mp (Finset.mem_filter.mp hj).1
have hj' := (e.distanceData a t i X Y).prefixHighIndices_spec hj
let u := BranchState.H a t p (j+1) i
let expo := DimensionalPolicy.branchExponentND e.profile (recursiveLoss n e.C) sig sig eps (.D a t i) .high u
have hstep : DimensionalPolicy.BranchStep e.profile e.N e.q offset sig (.D a t i) .high u :=
.high hw hside (Finset.mem_Ioc.mpr (by dsimp [RecursiveEnvironmentND.distanceData] at hjm; omega)) (by simpa only [RecursiveEnvironmentND.distanceData,he] using hj'.2)
have hcap : ∀ x,((e.distanceData a t i X Y).prefixAngularData (j+1)).toRecursiveEnvironmentND.energies K W total Ψ u x → x≤M/((2:ℝ)^e.N)^expo := by
intro y hy
exact (le_div_iff₀ (by positivity : 0<((2:ℝ)^e.N)^expo)).mpr (by
simpa only [mul_comm] using hchildren _ _ hstep y hy)
have hc := hclose (j+1) (M/((2:ℝ)^e.N)^expo) (by positivity) hcap
have hfac : ((2:ℝ)^e.N)^eps*(e.distanceData a t i X Y).prefixGraphFactor=((2:ℝ)^e.N)^(eps+
(-2*(e.profile p-e.profile a)+height e.profile a p+(e.C+36*(n+1:ℝ)+5)*sig)) := by
dsimp [PositiveDistanceState.prefixGraphFactor,RecursiveEnvironmentND.distanceData]
rw [he,←Real.rpow_add (by positivity : 0<(2:ℝ)^e.N)]
rfl
have hexp : eps+(-2*(e.profile p-e.profile a)+height e.profile a p+(e.C+36*(n+1:ℝ)+5)*sig)≤expo := by
dsimp only [expo,u,DimensionalPolicy.branchExponentND,DimensionalPolicy.branchCost,BranchState.start]
simp only [show BranchLabel.high ≠ .low by decide,↓reduceIte]
have hh := mul_le_mul_of_nonneg_right (recursiveLoss_graph n e.C) hsig.le
linarith only [hh]
change ((2:ℝ)^e.N)^eps*(e.distanceData a t i X Y).prefixGraphFactor*(e.distanceData a t i X Y).prefixHighChildSup Order (j+1:ℕ) Ψ≤D^(4*total)*M
change (e.distanceData a t i X Y).prefixHighChildSup Order (j+1:ℕ) Ψ≤D^(4*total)*(M/((2:ℝ)^e.N)^expo) at hc
rw [hfac]
have hmul := mul_le_mul_of_nonneg_left hc (by positivity : 0≤((2:ℝ)^e.N)^(eps+
(-2*(e.profile p-e.profile a)+height e.profile a p+(e.C+36*(n+1:ℝ)+5)*sig)))
apply hmul.trans
rw [mul_left_comm]
exact mul_le_mul_of_nonneg_left (power_div_cap hM hexp) (by positivity)
theorem uniform_recursive_prefix_rule (hn : 1≤n)
{sig K : ℝ} (hsig : 0<sig) (hsig₁ : sig≤1) (hK : 32*(n+2:ℝ)≤K)
(J : ℕ) (hJ : 0<J) (eps T : ℝ) (heps : 0<eps) (hT : 0<T)
(Ψ : ℝ → ℝ) (hΨ : ContDiff ℝ ∞ Ψ) (hsupp : tsupport Ψ⊆Ioo (1/4:ℝ) 4)
(hΨ0 : ∀ r,0≤Ψ r) (hΨ1 : ∀ r∈Icc (1/2:ℝ) 2,1≤Ψ r) (hiΨ : Integrable Ψ) :
∃ Order : ℕ,∀ (W : Finset (List (EuclideanSpace ℝ (Fin (n+1)) →L[ℝ] EuclideanSpace ℝ (Fin (n+1)))))
(B total : ℕ), (∀ L∈W,L.length≤B) → prefixInverseVocabulary n Order⊆W → 2*Order≤total →
∃ H E : ℝ,0≤H ∧ 0≤E ∧ ∃ N₀ : ℕ,∀ e : RecursiveEnvironmentND n,
N₀≤e.N → e.sig=sig → e.J=J →
∀ B₀ s,RegularizedProfileData e.ρ e.N B₀ s sig e.mass →
0<e.q → sig*(e.N:ℝ)≤e.q → ∀ a t i offset, i<e.J → t≤e.N → a+100*e.q<t →
2*policyDepth e.profile e.N e.q a t≤a+t →
∀ M : ℝ,0≤M → ((2:ℝ)^e.N)^(4*(n+2:ℝ)*sig)≤M →
(∀ u l,DimensionalPolicy.BranchStep e.profile e.N e.q offset sig (.D a t i) l u →
∀ x,e.energies K W total Ψ u x →
((2:ℝ)^e.N)^(DimensionalPolicy.branchExponentND e.profile (recursiveLoss n e.C) sig sig eps
(.D a t i) l u)*x≤M) →
∀ x,e.energies K W total Ψ (.D a t i) x → x≤H*((e.N:ℝ)+1)*M+E*((2:ℝ)^e.N)^(-T) := by
obtain ⟨Order,H₀,E,hH₀,hE,N₁,hpref⟩ := uniform_actual_prefix_reduction hn hsig hsig₁ hK J hJ eps T heps hT Ψ hΨ hsupp hΨ0 hΨ1
refine ⟨Order,?_⟩
intro W B total hW hV htotal
obtain ⟨D,hD,N₂,hclose⟩ := uniform_recursive_high_closure W B total Order hW hV htotal hsig
let H := H₀*(2+2*(32*binKernelConstant.toReal)+2*D^(4*total))
refine ⟨H,E,by dsimp [H]; positivity,hE,max N₁ N₂,?_⟩
intro e hN he hJe B₀ s hd hq hslack a t i offset hi ht hw hside M hM hfloor hchildren x hx
let := hd.probability
obtain ⟨X,Y,hX,hY,hox,hoy,hsep,rfl⟩ := hx
let f := e.distanceData a t i X Y
let p := policyDepth e.profile e.N e.q a t
have hap : a≤p := (Nat.le_add_right a e.q).trans (policyDepth_interior e.profile hq (policyBlocks_nonempty hq ht hw)).1
have hb := hpref f (show N₁≤e.N by omega) he hJe hi B₀ s hd hq ht hw hslack hside
((mem_dyadicPaths a X).mp hX) ((mem_dyadicPaths a Y).mp hY) hox hoy hsep
have hloeach (j : ℕ) (hj : j∈f.prefixLowIndices) :
({f with t:=j+1,i:=f.i+1} : PositiveDistanceState (n+1)).coincidence.toReal≤M := by
obtain ⟨hj,hgap⟩ := Finset.mem_filter.mp hj
have hj := Finset.mem_Ico.mp hj
have hstep : DimensionalPolicy.BranchStep e.profile e.N e.q offset sig (.D a t i) .low (.D a (j+1) (i+1)) :=
.low hw (Finset.mem_Ioc.mpr (by dsimp [f,RecursiveEnvironmentND.distanceData] at hj; omega)) (by simpa only [f,RecursiveEnvironmentND.distanceData,he] using hgap)
have hh := hchildren _ _ hstep _ (e.distance_mem K W total Ψ a (j+1) (i+1) X Y hX hY hox hoy hsep)
have hexp : 0≤DimensionalPolicy.branchExponentND e.profile (recursiveLoss n e.C) sig sig eps (.D a t i) .low (.D a (j+1) (i+1)) := by
simpa [DimensionalPolicy.branchExponentND,DimensionalPolicy.branchCost,BranchState.start] using heps.le
have hh' := weighted_child_cap ENNReal.toReal_nonneg hexp hh
simpa only [Real.rpow_zero,one_mul,f,RecursiveEnvironmentND.distanceData] using hh'
have hlocard : f.prefixLowIndices.card≤e.N := by
exact (Finset.card_filter_le _ _).trans (by simpa only [Nat.card_Ico] using (Nat.sub_le f.t f.a).trans ht)
have hlo : (∑ j∈f.prefixLowIndices,({f with t:=j+1,i:=f.i+1} : PositiveDistanceState (n+1)).coincidence.toReal)≤(e.N:ℝ)*M := by
calc
_≤∑ j∈f.prefixLowIndices,M := Finset.sum_le_sum hloeach
_=(f.prefixLowIndices.card:ℝ)*M := by simp
_≤(e.N:ℝ)*M := mul_le_mul_of_nonneg_right (Nat.cast_le.mpr hlocard) hM
have hhieach (j : ℕ) (hj : j∈f.prefixHighIndices) :
((2:ℝ)^e.N)^eps*f.prefixGraphFactor*f.prefixHighChildSup Order (j+1:ℕ) Ψ≤D^(4*total)*M := by
apply prefix_high_weighted_cap hsig e he a t i offset Order total W X Y K eps D M hD hM Ψ hiΨ hΨ0 hq ht hw hside hchildren
· intro v M' hM' hcap
exact hclose f (show N₂≤e.N by omega) he (show IsFiniteMeasure e.ρ from inferInstance) hq hap v Ψ (hiΨ.comp_div (by positivity)) hΨ0 K M' hM' hcap
· exact hj
have hhi : ((2:ℝ)^e.N)^eps*f.prefixGraphFactor*(∑ j∈f.prefixHighIndices,f.prefixHighChildSup Order (j+1:ℕ) Ψ)≤D^(4*total)*(e.N:ℝ)*M := by
rw [Finset.mul_sum]
calc
_≤∑ j∈f.prefixHighIndices,D^(4*total)*M := Finset.sum_le_sum hhieach
_=(f.prefixHighIndices.card:ℝ)*(D^(4*total)*M) := by simp
_≤(e.N:ℝ)*(D^(4*total)*M) := mul_le_mul_of_nonneg_right (Nat.cast_le.mpr (f.prefixHighIndices_card_bound ht)) (by positivity)
_=_ := by ring
have hmain : H₀*(2*((2:ℝ)^e.N)^(4*(n+2:ℝ)*sig)+2*(32*binKernelConstant.toReal)*
(∑ j∈f.prefixLowIndices,({f with t:=j+1,i:=f.i+1} : PositiveDistanceState (n+1)).coincidence.toReal)+
2*((2:ℝ)^e.N)^eps*f.prefixGraphFactor*(∑ j∈f.prefixHighIndices,f.prefixHighChildSup Order (j+1:ℕ) Ψ))≤H*((e.N:ℝ)+1)*M := by
have h₁ := mul_le_mul_of_nonneg_left hfloor (by positivity : 0≤2*H₀)
have h₂ := mul_le_mul_of_nonneg_left hlo (by positivity : 0≤H₀*(2*(32*binKernelConstant.toReal)))
have h₃ := mul_le_mul_of_nonneg_left hhi (by positivity : 0≤2*H₀)
have h₄ : 0≤2*H₀*(e.N:ℝ)*M+H₀*(2*(32*binKernelConstant.toReal))*M+2*H₀*D^(4*total)*M := by positivity
dsimp [H]
nlinarith only [h₁,h₂,h₃,h₄]
exact hb.trans (add_le_add hmain le_rfl)
end GraphLiteralSupport.CurrentFalconer.TreeND
end
end
section
section
open Set MeasureTheory
open scoped ComplexConjugate RealInnerProductSpace
namespace GraphLiteralSupport.CurrentFalconer.TreeND
open Set MeasureTheory Filter PlanarFalconer
open scoped ContDiff Classical
variable {n : ℕ}
theorem uniform_recursive_direct_rule {sig : ℝ} (hsig : 0<sig)
(T : ℝ) (J : ℕ) (hJ : 0<J) (Ψ : ℝ → ℝ) (hΨ : ContDiff ℝ ∞ Ψ)
(hsupp : tsupport Ψ⊆Ioo (1/4:ℝ) 4) (hΨ0 : ∀ r,0≤Ψ r) (hiΨ : Integrable Ψ)
(W : Finset (List (EuclideanSpace ℝ (Fin (n+1)) →L[ℝ] EuclideanSpace ℝ (Fin (n+1)))))
(B total : ℕ) (hW : ∀ L∈W,L.length≤B) :
∃ H E : ℝ,1≤H ∧ 0≤E ∧ ∃ N₀ : ℕ,∀ e : RecursiveEnvironmentND n,
N₀≤e.N → e.sig=sig → e.J=J →
∀ B₀ s,RegularizedProfileData e.ρ e.N B₀ s sig e.mass →
0<e.q → ⌈10*sig*(e.N:ℝ)⌉₊<e.q → sig*(e.N:ℝ)≤e.q → 10*sig*(e.N:ℝ)≤e.q →
∀ a₀ t₀ a v i offset, i<e.J → (BranchState.H a₀ t₀ a v i).Valid e.N sig → a+100*e.q<v →
(BranchState.H a₀ t₀ a v i).nearDepth e.profile e.N e.q sig≤a →
∀ K eps M : ℝ,0≤eps → 0≤M →
(∀ u l,DimensionalPolicy.BranchStep e.profile e.N e.q offset sig (.H a₀ t₀ a v i) l u →
∀ x,e.energies K W total Ψ u x →
((2:ℝ)^e.N)^(DimensionalPolicy.branchExponentND e.profile (recursiveLoss n e.C) sig sig eps
(.H a₀ t₀ a v i) l u)*x≤M) →
∀ x,e.energies K W total Ψ (.H a₀ t₀ a v i) x → x≤H*M+E*((2:ℝ)^e.N)^(-T) := by
obtain ⟨j,hgraph⟩ := uniform_recursive_direct_graph (n:=n) hsig T J hJ Ψ hΨ hsupp hΨ0
obtain ⟨E,hE,N₁,hgraph⟩ := hgraph W B total hW 1 (by norm_num)
obtain ⟨D,hD,hrem⟩ := uniform_recursive_reached_removal W B total hW hsig 1 (by norm_num)
obtain ⟨N₂,hrem⟩ := eventually_atTop.mp hrem
refine ⟨D^(4*total),E,one_le_pow₀ hD,hE,max N₁ N₂,?_⟩
intro e hN he hJe B₀ s hd hq hceil hslack hslack10 a₀ t₀ a v i offset hi hv hw hdir K eps M heps hM hchildren x hx
let := hd.probability
obtain ⟨X,Y,hX,hY,hox,hoy,L₁,L₂,f,g,hf,hg,hfn,hgn,hw₁,hw₂,ht₁,ht₂,rfl⟩ := hx
let r := e.angularData a₀ t₀ a v i X Y
obtain ⟨b,hb,hgraph⟩ := hgraph r (show N₁≤e.N by omega) he hJe hi B₀ s hd hq hceil hslack hslack10
hv.1 hv.2.2.1 hv.2.2.2.1 hv.2.2.2.2 hw hdir hX hY hox hoy
L₁ L₂ f g recursiveUnit recursiveUnit hf hg hfn hgn hw₁ hw₂ ht₁ ht₂
(fun l _ w _ => recursiveUnit_jets l w) (fun l _ w _ => recursiveUnit_jets l w)
let p := descriptorAnchor b
let exponent := DimensionalPolicy.branchExponentND e.profile (recursiveLoss n e.C) sig sig eps
(.H a₀ t₀ a v i) .direct (.H a₀ t₀ p v i)
have hstep : DimensionalPolicy.BranchStep e.profile e.N e.q offset sig
(.H a₀ t₀ a v i) .direct (.H a₀ t₀ p v i) := .direct hw hb
have hRp : 0<((2:ℝ)^e.N)^exponent := by positivity
have hcap : r.reachedChildren p L₁ L₂ f g recursiveUnit recursiveUnit Ψ≤M/((2:ℝ)^e.N)^exponent := by
apply r.reachedChildren_le _ _ _ _ _ _ _ _ _ (by positivity)
intro U V hU hV hou hov
have hh := hchildren _ _ hstep _ (r.successor_energy_mem K W total Ψ p U V hU hV hou hov
L₁ L₂ f g hf hg hfn hgn hw₁ hw₂ ht₁ ht₂)
exact (le_div_iff₀ hRp).mpr (by simpa only [mul_comm] using hh)
have hr := hrem e.N (by omega) r rfl he (show IsFiniteMeasure e.ρ from inferInstance) hq p hb.anchor_gt.le
L₁ L₂ f g recursiveUnit recursiveUnit hf hg hfn hgn hw₁ hw₂ ht₁ ht₂
(fun _ _ => by norm_num [recursiveUnit]) (fun _ _ => by norm_num [recursiveUnit])
Ψ (hiΨ.comp_div (by positivity)) hΨ0
have hr' := hr.trans (mul_le_mul_of_nonneg_left hcap (by positivity : 0≤D^(4*total)))
have heq : r.graphFactor p=((2:ℝ)^e.N)^(-2*(e.profile p-e.profile a)+height e.profile a p+
(e.C+36*(n+1:ℝ)+5)*sig) := by simp only [RecursiveAngularND.graphFactor,r,RecursiveEnvironmentND.angularData,he]; rfl
have hexp : -2*(e.profile p-e.profile a)+height e.profile a p+(e.C+36*(n+1:ℝ)+5)*sig≤exponent := by
unfold exponent DimensionalPolicy.branchExponentND DimensionalPolicy.branchCost
simp only [BranchState.start,show BranchLabel.direct ≠ .low by decide,↓reduceIte]
have hh := mul_le_mul_of_nonneg_right (recursiveLoss_graph n e.C) hsig.le
nlinarith
have hpay : r.graphFactor p*(M/((2:ℝ)^e.N)^exponent)≤M := by
rw [heq]; exact power_div_cap hM hexp
have hgain : r.graphFactor p*r.sameSymbolChildren p L₁ L₂ f g recursiveUnit recursiveUnit Ψ≤D^(4*total)*M := by
calc
_≤r.graphFactor p*(D^(4*total)*(M/((2:ℝ)^e.N)^exponent)) := mul_le_mul_of_nonneg_left hr' (by rw [heq]; positivity)
_=D^(4*total)*(r.graphFactor p*(M/((2:ℝ)^e.N)^exponent)) := by ring
_≤D^(4*total)*M := mul_le_mul_of_nonneg_left hpay (by positivity)
exact hgraph.trans (add_le_add hgain le_rfl)
end GraphLiteralSupport.CurrentFalconer.TreeND
end
end
section
section
open Set MeasureTheory
open scoped ComplexConjugate RealInnerProductSpace
namespace GraphLiteralSupport.CurrentFalconer.TreeND
open Set MeasureTheory Filter PlanarFalconer
open scoped ContDiff Classical
variable {n : ℕ}
theorem uniform_recursive_near_far_rule
(Ψ : ℝ → ℝ) (hΨ : ContDiff ℝ ∞ Ψ) (hsupp : tsupport Ψ⊆Ioo (1/4:ℝ) 4)
(hΨ0 : ∀ r,0≤Ψ r) {sig : ℝ} (hsig : 0<sig) (hsig₁ : sig≤1)
(W : Finset (List (EuclideanSpace ℝ (Fin (n+1)) →L[ℝ] EuclideanSpace ℝ (Fin (n+1)))))
(B total : ℕ) (hW : ∀ L∈W,L.length≤B) {J : ℕ} (hJ : 0<J)
(T : ℝ) (ell : ℕ) (hK : 32*(n+2:ℝ)≤(2:ℝ)^ell) :
∃ H : ℝ,0≤H ∧ ∃ N₀ : ℕ,∀ e : RecursiveEnvironmentND n,
N₀≤e.N → e.sig=sig → e.J=J →
∀ B₀ s,RegularizedProfileData e.ρ e.N B₀ s sig e.mass → 0<e.q →
∀ a₀ t₀ a v i,(BranchState.H a₀ t₀ a v i).Valid e.N sig → a+100*e.q<v →
a<(BranchState.H a₀ t₀ a v i).nearDepth e.profile e.N e.q sig →
∀ eps M : ℝ,0≤eps → 0≤M →
(∀ u l,DimensionalPolicy.BranchStep e.profile e.N e.q (ell+2) sig (.H a₀ t₀ a v i) l u →
∀ x,e.energies ((2:ℝ)^ell) W total Ψ u x →
((2:ℝ)^e.N)^(DimensionalPolicy.branchExponentND e.profile (recursiveLoss n e.C) sig sig eps
(.H a₀ t₀ a v i) l u)*x≤M) →
∀ x,e.energies ((2:ℝ)^ell) W total Ψ (.H a₀ t₀ a v i) x →
x≤H*((e.N:ℝ)+1)*M+((2:ℝ)^e.N)^(-T) := by
obtain ⟨j,D,H,hD,hH,N₁,hnf⟩ := uniform_recursive_near_far hΨ hsupp hΨ0 hsig hsig₁ W B total hW hJ
1 (by norm_num) T (ell+2:ℝ) ell hK
obtain ⟨N₂,hbuffer⟩ := DimensionalPolicy.uniform_far_offset_buffer hsig (ell+2)
let Cnear := (20*(n+2:ℝ)+5)^(2*(n+1))*D^(4*total)
refine ⟨Cnear+H,by dsimp only [Cnear]; positivity,max N₁ N₂,?_⟩
intro e hN he hJe B₀ s hd hq a₀ t₀ a v i hv hw hnear eps M heps hM hchildren x hx
let := hd.probability
obtain ⟨X,Y,hX,hY,hox,hoy,L₁,L₂,f,g,hf,hg,hfn,hgn,hw₁,hw₂,ht₁,ht₂,rfl⟩ := hx
let r := e.angularData a₀ t₀ a v i X Y
let h := (BranchState.H a₀ t₀ a v i).nearDepth e.profile e.N e.q sig
let expn := DimensionalPolicy.branchExponentND e.profile (recursiveLoss n e.C) sig sig eps
(.H a₀ t₀ a v i) .near (.H a₀ t₀ h v i)
let expf := fun b => DimensionalPolicy.branchExponentND e.profile (recursiveLoss n e.C) sig sig eps
(.H a₀ t₀ a v i) .far (.D b v (i+1))
have hhv : h+ell+2<v := by
have hh := hbuffer e.N (by omega) ((BranchState.H a₀ t₀ a v i).descriptors e.profile e.N e.q |>.sup descriptorLength) v a hnear
simpa only [h,BranchState.nearDepth,BranchState.finish,Nat.add_assoc] using hh
have hhN : h+ell+2≤e.N := (Nat.le_of_lt hhv).trans (hv.2.2.1.trans hv.2.2.2.1)
have hdepth : ((h+ell+2:ℕ):ℝ)≤(v:ℝ)-2*(max ((r.retainedList a).sup descriptorLength) (Nat.ceil (10*sig*e.N)):ℕ)+(ell+2:ℝ) := by
let b := sourceFarBuffer e.N (((BranchState.H a₀ t₀ a v i).descriptors e.profile e.N e.q).sup descriptorLength) sig
have hh : h=v-2*b := rfl
have hvb : 2*b≤v := by dsimp only [h] at hh; omega
have hh' : (h:ℝ)=(v:ℝ)-2*(b:ℝ) := by rw [hh,Nat.cast_sub hvb]; push_cast; rfl
change ((h+ell+2:ℕ):ℝ)≤(v:ℝ)-2*(b:ℝ)+(ell+2:ℝ)
push_cast
linarith
have hncap (U V : CellPath (n+1)) (hU : U∈dyadicPaths h) (hV : V∈dyadicPaths h)
(hou : 0<e.ρ (treeCell U)) (hov : 0<e.ρ (treeCell V)) :
(r.successor h U V).energy L₁ L₂ f g recursiveUnit recursiveUnit Ψ≤M/((2:ℝ)^e.N)^expn := by
have hh := hchildren _ _ (.near hw hnear) _ (r.successor_energy_mem ((2:ℝ)^ell) W total Ψ h U V hU hV hou hov
L₁ L₂ f g hf hg hfn hgn hw₁ hw₂ ht₁ ht₂)
exact (le_div_iff₀ (by positivity : 0<((2:ℝ)^e.N)^expn)).mpr (by simpa only [mul_comm] using hh)
have hfcap (b : ℕ) (hb : b∈Finset.Icc (a+1) (h+ell+2)) (U V : CellPath (n+1))
(hU : U∈dyadicPaths b) (hV : V∈dyadicPaths b) (hou : 0<e.ρ (treeCell U)) (hov : 0<e.ρ (treeCell V))
(hs : Attempt4FullFar.farSeparatedGridND b ((2:ℝ)^ell) (fun j => (pathIndex U j:ℤ)) (fun j => (pathIndex V j:ℤ))) :
(e.distanceData b v (i+1) U V).coincidence.toReal≤M/((2:ℝ)^e.N)^(expf b) := by
have hh := hchildren _ _ (.far hw hnear (by simpa only [Nat.add_assoc] using hb)) _
(e.distance_mem ((2:ℝ)^ell) W total Ψ b v (i+1) U V hU hV hou hov hs)
exact (le_div_iff₀ (by positivity : 0<((2:ℝ)^e.N)^(expf b))).mpr (by simpa only [mul_comm] using hh)
have hb := hnf r (show N₁≤e.N by omega) he hJe B₀ s hd hq hv.2.2.2.1 hv.1 hv.2.2.1
h hnear hhN hdepth hX hY hox hoy L₁ L₂ f g recursiveUnit recursiveUnit
(M/((2:ℝ)^e.N)^expn) (fun b => M/((2:ℝ)^e.N)^(expf b)) hf hg hfn hgn hw₁ hw₂ ht₁ ht₂
(fun l _ w _ => recursiveUnit_jets l w) (fun l _ w _ => recursiveUnit_jets l w)
hncap (fun _ _ => by positivity) hfcap
have hdim := mul_le_mul_of_nonneg_right (recursiveLoss_dim n e.C) hsig.le
have hnexp : -2*(e.profile h-e.profile a)+4*(n+2:ℝ)*sig≤expn := by
dsimp only [expn,DimensionalPolicy.branchExponentND,DimensionalPolicy.branchCost,BranchState.start]
simp only [show BranchLabel.near ≠ .low by decide,↓reduceIte]
nlinarith
have hfexp (b : ℕ) : -2*(e.profile b-e.profile a)+4*(n+2:ℝ)*sig≤expf b := by
dsimp only [expf,DimensionalPolicy.branchExponentND,DimensionalPolicy.branchCost,BranchState.start]
simp only [show BranchLabel.far ≠ .low by decide,↓reduceIte]
nlinarith
have hnpay := power_div_cap (N:=e.N) hM hnexp
have hfpay : (∑ b∈Finset.Icc (a+1) (h+ell+2),((2:ℝ)^e.N)^(-2*(e.profile b-e.profile a)+4*(n+2:ℝ)*sig)*
(M/((2:ℝ)^e.N)^(expf b)))≤(e.N:ℝ)*M := by
calc
_≤∑ b∈Finset.Icc (a+1) (h+ell+2),M := Finset.sum_le_sum (fun b _ => power_div_cap hM (hfexp b))
_=((Finset.Icc (a+1) (h+ell+2)).card:ℝ)*M := by simp
_≤(e.N:ℝ)*M := mul_le_mul_of_nonneg_right (Nat.cast_le.mpr (by rw [Nat.card_Icc]; omega)) hM
have hbound : Cnear*(((2:ℝ)^e.N)^(-2*(e.profile h-e.profile a)+4*(n+2:ℝ)*sig)*(M/((2:ℝ)^e.N)^expn))+
H*(∑ b∈Finset.Icc (a+1) (h+ell+2),((2:ℝ)^e.N)^(-2*(e.profile b-e.profile a)+4*(n+2:ℝ)*sig)*(M/((2:ℝ)^e.N)^(expf b)))≤
(Cnear+H)*((e.N:ℝ)+1)*M := by
have hh₁ := mul_le_mul_of_nonneg_left hnpay (by dsimp only [Cnear]; positivity : 0≤Cnear)
have hh₂ := mul_le_mul_of_nonneg_left hfpay hH
have hh₃ : 0≤Cnear*(e.N:ℝ)*M+H*M := by dsimp only [Cnear]; positivity
nlinarith only [hh₁,hh₂,hh₃]
have hb' : (e.angularData a₀ t₀ a v i X Y).energy L₁ L₂ f g recursiveUnit recursiveUnit Ψ ≤
Cnear*(((2:ℝ)^e.N)^(-2*(e.profile h-e.profile a)+4*(n+2:ℝ)*sig)*(M/((2:ℝ)^e.N)^expn))+
H*(∑ b∈Finset.Icc (a+1) (h+ell+2),((2:ℝ)^e.N)^(-2*(e.profile b-e.profile a)+4*(n+2:ℝ)*sig)*(M/((2:ℝ)^e.N)^(expf b)))+
((2:ℝ)^e.N)^(-T) := by
simpa only [r,RecursiveEnvironmentND.angularData,RecursiveAngularND.profile,Cnear,mul_assoc] using hb
exact hb'.trans (add_le_add hbound le_rfl)
end GraphLiteralSupport.CurrentFalconer.TreeND
end
end
end OAI