openAI-math-dataset / lean /OAI /Probability /LogConcave /Analysis /ConditionalPotential.lean
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import Mathlib
import OAI.Probability.LogConcave.Sampling.TiltedExpectation
import OAI.Probability.LogConcave.JetEstimates.Bound
namespace OAI
section
section
noncomputable section
open MeasureTheory Filter
open scoped ENNReal NNReal Topology
section UpperProof
open MeasureTheory ProbabilityTheory Filter
open scoped ENNReal NNReal RealInnerProductSpace Topology
namespace LogConcaveSampling
open MeasureTheory
lemma HasGaussianLowerTail.hasExpMoments {d : ℕ} {H : Point d → ℝ}
(ht : HasGaussianLowerTail H) (hc : Continuous H) : Appell.HasExpMoments (gibbs H) := by
have hi := integrable_tilted H hc continuous_const ht (growth_const (1:ℝ)) 0
simp only [inner_zero_left,zero_sub,smul_eq_mul,mul_one] at hi
obtain ⟨m,hm,C,ht⟩ := ht
intro R
apply integrable_gibbs_of_weighted hc hi
have hct : Continuous (fun x => Real.exp (-H x)*Real.exp (R*‖x‖)) :=
hc.neg.rexp.mul ((continuous_const.mul continuous_norm).rexp)
apply ((integrable_gaussian_envelope d (b := m/2) (by positivity)).const_mul
(Real.exp (C+R^2/(2*m)))).mono'
hct.aestronglyMeasurable
filter_upwards [] with x
rw [norm_mul,Real.norm_eq_abs,abs_of_pos (Real.exp_pos _),Real.norm_eq_abs,
abs_of_pos (Real.exp_pos _),← Real.exp_add,← Real.exp_add]
apply Real.exp_le_exp.mpr
have hyp : R*‖x‖ ≤ (m/2)*‖x‖^2+R^2/(2*m) := by
have hh := sq_nonneg (m*‖x‖-R)
have hd : R^2/(2*m)*(2*m)=R^2 := by field_simp
nlinarith
linarith [ht x]
lemma Primitive.hasExpMoments {d : ℕ} {F : Point d → ℝ} {lam : ℝ≥0}
(hF : Primitive F lam) (x : Point d) {r : ℝ} (hr : 0 ≤ r)
(hl : (lam:ℝ)*r^2 < 1) : Appell.HasExpMoments (gibbs (primitivePotential F x r)) :=
(hF.hasGaussianLowerTail x hr hl).hasExpMoments (hF.continuous_potential x r)
namespace Appell
lemma HasExpMoments.map_lipschitz {E F : Type} [NormedAddCommGroup E]
[NormedAddCommGroup F] [MeasurableSpace E] [BorelSpace E]
[MeasurableSpace F] [BorelSpace F] {μ : Measure E}
(hμ : HasExpMoments μ) {S : E → F} {K : ℝ≥0} (hS : LipschitzWith K S) :
HasExpMoments (μ.map S) := by
intro R
apply (integrable_map_measure ((continuous_const.mul continuous_norm).rexp).aestronglyMeasurable
hS.continuous.aemeasurable).2
apply ((hμ (|R| *K)).const_mul (Real.exp (|R| *‖S 0‖))).mono'
((continuous_const.mul hS.continuous.norm).rexp).aestronglyMeasurable
filter_upwards [] with x
change ‖Real.exp (R*‖S x‖)‖ ≤ _
rw [Real.norm_eq_abs,abs_of_pos (Real.exp_pos _),← Real.exp_add]
apply Real.exp_le_exp.mpr
have hb : ‖S x‖ ≤ K*‖x‖+‖S 0‖ := by
have hh := hS.dist_le_mul x 0
simp only [dist_eq_norm,sub_zero] at hh
have hh' := norm_add_le (S x-S 0) (S 0)
rw [sub_add_cancel] at hh'
linarith
have ht := mul_le_mul_of_nonneg_left hb (abs_nonneg R)
have hr := mul_le_mul_of_nonneg_right (le_abs_self R) (norm_nonneg (S x))
nlinarith
end Appell
end LogConcaveSampling
namespace LogConcaveSampling
open MeasureTheory
open scoped ENNReal
theorem gibbs_map_equiv {d : ℕ} {H : Point d → ℝ} (hH : Continuous H)
(e : Point d ≃ᵐ Point d) {c : ℝ≥0∞} (hc0 : c≠0) (hct : c≠⊤)
(he : (volume : Measure (Point d)).map e=c • volume) :
(gibbs H).map e=gibbs (fun y => H (e.symm y)) := by
have hm : ((volume : Measure (Point d)).withDensity (gibbsDensity H)).map e =
c • volume.withDensity (gibbsDensity (fun y => H (e.symm y))) := by
rw [EulerDensity.map_withDensity_equiv volume e (by unfold gibbsDensity; fun_prop),
he,withDensity_smul_measure]
rfl
have hp : partition H=c*partition (fun y => H (e.symm y)) := by
have hh := congrArg (fun μ : Measure (Point d) => μ Set.univ) hm
simpa only [Measure.map_apply e.measurable MeasurableSet.univ,Set.preimage_univ,
Measure.smul_apply,smul_eq_mul,withDensity_apply _ MeasurableSet.univ,
Measure.restrict_univ,partition] using hh
unfold gibbs
rw [Measure.map_smul _ e.measurable.aemeasurable,hm,hp,smul_smul,
ENNReal.mul_inv (Or.inl hc0) (Or.inl hct)]
congr 1
calc
(c⁻¹*(partition (fun y => H (e.symm y)))⁻¹)*c =
(c⁻¹*c)*(partition (fun y => H (e.symm y)))⁻¹ := by ac_rfl
_ = _ := by rw [ENNReal.inv_mul_cancel hc0 hct,one_mul]
lemma gibbs_map_smul {d : ℕ} {H : Point d → ℝ} (hH : Continuous H)
{s : ℝ} (hs : s≠0) :
(gibbs H).map (fun z => s • z)=gibbs (fun z => H (s⁻¹ • z)) := by
let e := (Homeomorph.smulOfNeZero (α := Point d) s hs).toMeasurableEquiv
have he : (volume : Measure (Point d)).map e =
ENNReal.ofReal |(s^d)⁻¹| • volume := by
convert! Measure.map_addHaar_smul (volume : Measure (Point d)) hs using 1
simp [Point]
have h := gibbs_map_equiv hH e
(by positivity : ENNReal.ofReal |(s^d)⁻¹| ≠ 0)
ENNReal.ofReal_ne_top he
change (gibbs H).map e = _
rw [h]
congr 1
lemma gibbs_map_add {d : ℕ} {H : Point d → ℝ} (hH : Continuous H) (v : Point d) :
(gibbs H).map (fun z => v+z)=gibbs (fun z => H (z-v)) := by
let e := (Homeomorph.addLeft v).toMeasurableEquiv
have he : (volume : Measure (Point d)).map e = (1:ℝ≥0∞) • volume := by
change (volume : Measure (Point d)).map (fun z => v+z) = _
simpa only [one_smul] using (measurePreserving_add_left (volume : Measure (Point d)) v).map_eq
change (gibbs H).map e = _
rw [gibbs_map_equiv hH e (by norm_num) (by norm_num) he]
congr 1
funext z
apply congrArg H
change -v+z=z-v
abel
lemma gibbs_map_affine {d : ℕ} {H : Point d → ℝ} (hH : Continuous H)
(v : Point d) {s : ℝ} (hs : s≠0) :
(gibbs H).map (fun z => v+s • z)=gibbs (fun z => H (s⁻¹ • (z-v))) := by
change (gibbs H).map ((fun z => v+z) ∘ (fun z => s • z)) = _
rw [← Measure.map_map (show Measurable (fun z : Point d => v+z) by fun_prop)
(show Measurable (fun z : Point d => s • z) by fun_prop),gibbs_map_smul hH hs]
exact gibbs_map_add (show Continuous (fun z : Point d => H (s⁻¹ • z)) by fun_prop) v
end LogConcaveSampling
namespace LogConcaveSampling
open MeasureTheory
open scoped NNReal RealInnerProductSpace
def conditionalPotential {d : ℕ} (F : Point d → ℝ) (x : Point d)
(r ρ : ℝ) (y z : Point d) : ℝ :=
‖z-ρ • y‖^2/(2*(1-ρ^2)) + F (x+r • z)
lemma probability_time {ρ : ℝ} (hρ0 : 0≤ρ) (hρ1 : ρ<1) :
0 < 1-ρ^2 ∧ 1-ρ^2 ≤ 1 := by
constructor
· nlinarith
· nlinarith [sq_nonneg ρ]
lemma conditionalPotential_pullback {d : ℕ} (F : Point d → ℝ) (x : Point d)
(r ρ : ℝ) (y z : Point d) (ha : 0<1-ρ^2) :
primitivePotential F (x+r • (ρ • y)) (r*Real.sqrt (1-ρ^2))
((Real.sqrt (1-ρ^2))⁻¹ • (z-ρ • y)) = conditionalPotential F x r ρ y z := by
have hs : Real.sqrt (1-ρ^2) ≠ 0 := (Real.sqrt_pos.mpr ha).ne'
have hv : x+r • (ρ • y)+(r*Real.sqrt (1-ρ^2)) •
((Real.sqrt (1-ρ^2))⁻¹ • (z-ρ • y))=x+r • z := by
rw [← mul_smul r ρ y,smul_smul,mul_assoc,mul_inv_cancel₀ hs,mul_one,smul_sub]
rw [smul_smul]
abel
unfold primitivePotential conditionalPotential
rw [hv,norm_smul,Real.norm_eq_abs,mul_pow,sq_abs,inv_pow,Real.sq_sqrt ha.le]
congr 1
field_simp
lemma conditionalLaw_map {d : ℕ} {F : Point d → ℝ} {lam : ℝ≥0}
(hF : Primitive F lam) (x : Point d) (r ρ : ℝ) (y : Point d) (ha : 0<1-ρ^2) :
(gibbs (primitivePotential F (x+r • (ρ • y)) (r*Real.sqrt (1-ρ^2)))).map
(fun z => ρ • y+Real.sqrt (1-ρ^2) • z) =
gibbs (conditionalPotential F x r ρ y) := by
rw [gibbs_map_affine (hF.continuous_potential _ _)
_ (Real.sqrt_pos.mpr ha).ne']
congr 1
funext z
exact conditionalPotential_pullback F x r ρ y z ha
lemma conditional_smallness {lam : ℝ≥0} {r ρ : ℝ}
(hl : (lam:ℝ)*r^2 ≤ 1/2) (ha : 0<1-ρ^2) (ha1 : 1-ρ^2≤1) :
(lam:ℝ)*(r*Real.sqrt (1-ρ^2))^2 ≤ 1/2 := by
rw [mul_pow,Real.sq_sqrt ha.le]
have hn : 0≤(lam:ℝ)*r^2 := mul_nonneg lam.coe_nonneg (sq_nonneg r)
calc
(lam:ℝ)*(r^2*(1-ρ^2))=((lam:ℝ)*r^2)*(1-ρ^2) := by ring
_ ≤ ((lam:ℝ)*r^2)*1 := mul_le_mul_of_nonneg_left ha1 hn
_ ≤ 1/2 := by simpa using hl
lemma conditionalLaw_probability {d : ℕ} {F : Point d → ℝ} {lam : ℝ≥0}
(hF : Primitive F lam) (x : Point d) {r ρ : ℝ} (hr : 0≤r)
(hl : (lam:ℝ)*r^2 ≤ 1/2) (hρ0 : 0≤ρ) (hρ1 : ρ<1) (y : Point d) :
IsProbabilityMeasure (gibbs (conditionalPotential F x r ρ y)) := by
obtain ⟨ha,ha1⟩ := probability_time hρ0 hρ1
have hs := conditional_smallness hl ha ha1
have hsmall : (lam:ℝ)*(r*Real.sqrt (1-ρ^2))^2 < 1 := by linarith
have hrad : 0≤r*Real.sqrt (1-ρ^2) := mul_nonneg hr (Real.sqrt_nonneg _)
have : IsProbabilityMeasure (gibbs (primitivePotential F (x+r • (ρ • y))
(r*Real.sqrt (1-ρ^2)))) := probability_gibbs_of_partition
(partition_pos_of_continuous (hF.continuous_potential _ _)).ne'
(partition_ne_top_of_integrable (hF.integrable_exp_neg_potential _ hrad hsmall))
rw [← conditionalLaw_map hF x r ρ y ha]
infer_instance
lemma conditionalLaw_hasPoincare {d : ℕ} {F : Point d → ℝ} {lam : ℝ≥0}
(hF : Primitive F lam) (x : Point d) {r ρ : ℝ} (hr : 0≤r)
(hl : (lam:ℝ)*r^2 ≤ 1/2) (hρ0 : 0≤ρ) (hρ1 : ρ<1) (y : Point d) :
HasPoincare (gibbs (conditionalPotential F x r ρ y)) ((Real.pi^2/2)*(1-ρ^2)) := by
obtain ⟨ha,ha1⟩ := probability_time hρ0 hρ1
have hs := conditional_smallness hl ha ha1
have hsmall : (lam:ℝ)*(r*Real.sqrt (1-ρ^2))^2 < 1 := by linarith
have hrad : 0≤r*Real.sqrt (1-ρ^2) := mul_nonneg hr (Real.sqrt_nonneg _)
have : IsProbabilityMeasure (gibbs (primitivePotential F (x+r • (ρ • y))
(r*Real.sqrt (1-ρ^2)))) := probability_gibbs_of_partition
(partition_pos_of_continuous (hF.continuous_potential _ _)).ne'
(partition_ne_top_of_integrable (hF.integrable_exp_neg_potential _ hrad hsmall))
let K : ℝ≥0 := ⟨Real.sqrt (1-ρ^2),Real.sqrt_nonneg _⟩
have hK : LipschitzWith K (fun z : Point d => ρ • y+Real.sqrt (1-ρ^2) • z) := by
apply LipschitzWith.of_dist_le_mul
intro u v
simp only [dist_eq_norm,add_sub_add_left_eq_sub,← smul_sub,norm_smul,
Real.norm_eq_abs,abs_of_nonneg (Real.sqrt_nonneg _)]
rfl
have hB : 0≤(Real.pi^2/8)*(2/(1-(lam:ℝ)*(r*Real.sqrt (1-ρ^2))^2)) := by positivity
have hp := (hF.hasPoincare (x+r • (ρ • y)) hrad hsmall).map hB
(show ContDiff ℝ 1 (fun z : Point d => ρ • y+Real.sqrt (1-ρ^2) • z) by fun_prop) hK
rw [conditionalLaw_map hF x r ρ y ha] at hp
apply hp.mono
change (Real.pi^2/8)*(2/(1-(lam:ℝ)*(r*Real.sqrt (1-ρ^2))^2)) *
(Real.sqrt (1-ρ^2))^2 ≤ _
rw [Real.sq_sqrt ha.le]
apply mul_le_mul_of_nonneg_right _ ha.le
have hd : 0<1-(lam:ℝ)*(r*Real.sqrt (1-ρ^2))^2 := sub_pos.mpr hsmall
have hb : 2/(1-(lam:ℝ)*(r*Real.sqrt (1-ρ^2))^2) ≤ 4 := by
apply (div_le_iff₀ hd).mpr
linarith
nlinarith [mul_le_mul_of_nonneg_left hb (by positivity : (0:ℝ)≤Real.pi^2/8)]
end LogConcaveSampling
end UpperProof
end
end
end
end OAI