openAI-math-dataset / lean /OAI /Probability /FreeIsing /Stochastic /BinaryFusionIntegral.lean
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Add lean source and binary files (part 122)
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import OAI.Probability.FreeIsing.Gaussian.BinaryFusion
namespace OAI
noncomputable section
open MeasureTheory
namespace Problem367
theorem ae_binaryFusion_bounds {mu nu : Measure ℝ}
[IsProbabilityMeasure mu] [IsProbabilityMeasure nu]
(hu : ∀ᵐ u ∂mu, |u| < 1) (hv : ∀ᵐ v ∂nu, |v| < 1) :
∀ᵐ p ∂mu.prod nu, |p.1| < 1 ∧ |p.2| < 1 := by
have hu' : ∀ᵐ p ∂mu.prod nu, |p.1| < 1 :=
measurePreserving_fst.quasiMeasurePreserving.tendsto_ae hu
have hv' : ∀ᵐ p ∂mu.prod nu, |p.2| < 1 :=
measurePreserving_snd.quasiMeasurePreserving.tendsto_ae hv
exact hu'.and hv'
theorem integrable_binaryFusion_integrand {mu nu : Measure ℝ}
[IsProbabilityMeasure mu] [IsProbabilityMeasure nu]
(hu : ∀ᵐ u ∂mu, |u| < 1) (hv : ∀ᵐ v ∂nu, |v| < 1) :
Integrable (fun p : ℝ × ℝ => (p.1 + p.2) ^ 2 / (1 + p.1 * p.2))
(mu.prod nu) := by
refine (integrable_const (2 : ℝ)).mono' ?_ ?_
· apply Measurable.aestronglyMeasurable
fun_prop
filter_upwards [ae_binaryFusion_bounds hu hv] with p hp
rw [Real.norm_eq_abs, abs_of_nonneg
(div_nonneg (sq_nonneg _) (binaryFusion_denominator_pos hp.1 hp.2).le)]
exact binaryFusion_integrand_le_two hp.1 hp.2
theorem integrable_binaryFusionLoss {mu nu : Measure ℝ}
[IsProbabilityMeasure mu] [IsProbabilityMeasure nu]
(hu : ∀ᵐ u ∂mu, |u| < 1) (hv : ∀ᵐ v ∂nu, |v| < 1) :
Integrable (fun p : ℝ × ℝ => binaryFusionLoss p.1 p.2) (mu.prod nu) := by
refine (integrable_const (2 : ℝ)).mono' ?_ ?_
· unfold binaryFusionLoss
apply Measurable.aestronglyMeasurable
fun_prop
· filter_upwards [ae_binaryFusion_bounds hu hv] with p hp
rw [Real.norm_eq_abs, abs_of_nonneg (binaryFusionLoss_nonneg hp.1 hp.2)]
have h := binaryFusionLoss_le_sum_sq hp.1 hp.2
have hu' := (sq_lt_one_iff_abs_lt_one p.1).mpr hp.1
have hv' := (sq_lt_one_iff_abs_lt_one p.2).mpr hp.2
linarith
theorem integral_binaryFusion_symmetric_square {mu nu : Measure ℝ}
[IsProbabilityMeasure mu] [IsProbabilityMeasure nu]
(hu : ∀ᵐ u ∂mu, |u| < 1) (hv : ∀ᵐ v ∂nu, |v| < 1)
(hsym : Measure.map (fun v : ℝ => -v) nu = nu) :
(∫ p : ℝ × ℝ, (p.1 + p.2) ^ 2 / (1 + p.1 * p.2) ∂mu.prod nu) =
(∫ u : ℝ, u ^ 2 ∂mu) + (∫ v : ℝ, v ^ 2 ∂nu) -
∫ p : ℝ × ℝ, binaryFusionLoss p.1 p.2 ∂mu.prod nu := by
let R : (ℝ × ℝ) ≃ᵐ (ℝ × ℝ) :=
(MeasurableEquiv.refl ℝ).prodCongr (MeasurableEquiv.neg ℝ)
have hR : MeasurePreserving R (mu.prod nu) (mu.prod nu) :=
(MeasurePreserving.id mu).prod ⟨measurable_neg, hsym⟩
have hi := integrable_binaryFusion_integrand hu hv
have hin : Integrable (fun p : ℝ × ℝ =>
(p.1 - p.2) ^ 2 / (1 - p.1 * p.2)) (mu.prod nu) := by
have h := (hR.integrable_comp hi.aestronglyMeasurable).mpr hi
change Integrable (fun p : ℝ × ℝ =>
(p.1 + -p.2) ^ 2 / (1 + p.1 * -p.2)) (mu.prod nu) at h
simpa [sub_eq_add_neg] using h
have heq : (∫ p : ℝ × ℝ, (p.1 - p.2) ^ 2 / (1 - p.1 * p.2) ∂mu.prod nu) =
∫ p : ℝ × ℝ, (p.1 + p.2) ^ 2 / (1 + p.1 * p.2) ∂mu.prod nu := by
have h := hR.integral_comp'
(fun p : ℝ × ℝ => (p.1 + p.2) ^ 2 / (1 + p.1 * p.2))
change (∫ p : ℝ × ℝ, (p.1 + -p.2) ^ 2 / (1 + p.1 * -p.2) ∂mu.prod nu) = _ at h
simpa [sub_eq_add_neg] using h
have hiu : Integrable (fun p : ℝ × ℝ => p.1 ^ 2) (mu.prod nu) := by
refine (integrable_const (1 : ℝ)).mono' (by fun_prop) ?_
filter_upwards [ae_binaryFusion_bounds hu hv] with p hp
simpa only [Real.norm_eq_abs, abs_of_nonneg (sq_nonneg p.1)] using
((sq_lt_one_iff_abs_lt_one p.1).mpr hp.1).le
have hiv : Integrable (fun p : ℝ × ℝ => p.2 ^ 2) (mu.prod nu) := by
refine (integrable_const (1 : ℝ)).mono' (by fun_prop) ?_
filter_upwards [ae_binaryFusion_bounds hu hv] with p hp
simpa only [Real.norm_eq_abs, abs_of_nonneg (sq_nonneg p.2)] using
((sq_lt_one_iff_abs_lt_one p.2).mpr hp.2).le
have hl := integrable_binaryFusionLoss hu hv
have hiuv : Integrable (fun p : ℝ × ℝ => p.1 ^ 2 + p.2 ^ 2) (mu.prod nu) := hiu.add hiv
have hsum : (∫ p : ℝ × ℝ, p.1 ^ 2 + p.2 ^ 2 ∂mu.prod nu) =
(∫ p : ℝ × ℝ, p.1 ^ 2 ∂mu.prod nu) +
∫ p : ℝ × ℝ, p.2 ^ 2 ∂mu.prod nu := integral_add hiu hiv
have hscalar :
(∫ p : ℝ × ℝ,
((p.1 + p.2) ^ 2 / (1 + p.1 * p.2) +
(p.1 - p.2) ^ 2 / (1 - p.1 * p.2)) / 2 ∂mu.prod nu) =
∫ p : ℝ × ℝ, p.1 ^ 2 + p.2 ^ 2 - binaryFusionLoss p.1 p.2 ∂mu.prod nu := by
apply integral_congr_ae
filter_upwards [ae_binaryFusion_bounds hu hv] with p hp
exact binaryFusion_symmetric_square hp.1 hp.2
rw [integral_div, integral_add hi hin, heq,
integral_sub hiuv hl, hsum,
integral_fun_fst (μ := mu) (ν := nu) (fun u : ℝ => u ^ 2),
integral_fun_snd (μ := mu) (ν := nu) (fun v : ℝ => v ^ 2)] at hscalar
simp only [probReal_univ, one_smul] at hscalar
linarith
theorem integral_binaryFusionLoss_nonneg {mu nu : Measure ℝ}
[IsProbabilityMeasure mu] [IsProbabilityMeasure nu]
(hu : ∀ᵐ u ∂mu, |u| < 1) (hv : ∀ᵐ v ∂nu, |v| < 1) :
0 ≤ ∫ p : ℝ × ℝ, binaryFusionLoss p.1 p.2 ∂mu.prod nu := by
apply integral_nonneg_of_ae
filter_upwards [ae_binaryFusion_bounds hu hv] with p hp
exact binaryFusionLoss_nonneg hp.1 hp.2
theorem integral_binaryFusion_le_sum_sq {mu nu : Measure ℝ}
[IsProbabilityMeasure mu] [IsProbabilityMeasure nu]
(hu : ∀ᵐ u ∂mu, |u| < 1) (hv : ∀ᵐ v ∂nu, |v| < 1)
(hsym : Measure.map (fun v : ℝ => -v) nu = nu) :
(∫ p : ℝ × ℝ, (p.1 + p.2) ^ 2 / (1 + p.1 * p.2) ∂mu.prod nu) ≤
(∫ u : ℝ, u ^ 2 ∂mu) + (∫ v : ℝ, v ^ 2 ∂nu) := by
rw [integral_binaryFusion_symmetric_square hu hv hsym]
exact sub_le_self _ (integral_binaryFusionLoss_nonneg hu hv)
private theorem integrable_sq_of_ae_abs_lt_one {mu : Measure ℝ}
[IsProbabilityMeasure mu] (hu : ∀ᵐ u ∂mu, |u| < 1) :
Integrable (fun u : ℝ => u ^ 2) mu := by
refine (integrable_const (1 : ℝ)).mono' (by fun_prop) ?_
filter_upwards [hu] with u hu
simpa only [Real.norm_eq_abs, abs_of_nonneg (sq_nonneg u)] using
((sq_lt_one_iff_abs_lt_one u).mpr hu).le
theorem integral_binaryFusionLoss_lower_bound {mu nu : Measure ℝ}
[IsProbabilityMeasure mu] [IsProbabilityMeasure nu] {theta : ℝ}
(hu : ∀ᵐ u ∂mu, |u| ≤ theta) (hv : ∀ᵐ v ∂nu, |v| ≤ theta)
(htheta : theta < 1) :
2 * (1 - theta ^ 2) * (∫ u : ℝ, u ^ 2 ∂mu) * (∫ v : ℝ, v ^ 2 ∂nu) ≤
∫ p : ℝ × ℝ, binaryFusionLoss p.1 p.2 ∂mu.prod nu := by
have hu1 : ∀ᵐ u ∂mu, |u| < 1 := hu.mono (fun _ h => lt_of_le_of_lt h htheta)
have hv1 : ∀ᵐ v ∂nu, |v| < 1 := hv.mono (fun _ h => lt_of_le_of_lt h htheta)
have hiu := integrable_sq_of_ae_abs_lt_one hu1
have hiv := integrable_sq_of_ae_abs_lt_one hv1
have hip := hiu.mul_prod hiv
have hl := integrable_binaryFusionLoss hu1 hv1
have hu' : ∀ᵐ p ∂mu.prod nu, |p.1| ≤ theta :=
measurePreserving_fst.quasiMeasurePreserving.tendsto_ae hu
have hv' : ∀ᵐ p ∂mu.prod nu, |p.2| ≤ theta :=
measurePreserving_snd.quasiMeasurePreserving.tendsto_ae hv
have hb : (∫ p : ℝ × ℝ, (2 * (1 - theta ^ 2)) * (p.1 ^ 2 * p.2 ^ 2) ∂mu.prod nu) ≤
∫ p : ℝ × ℝ, binaryFusionLoss p.1 p.2 ∂mu.prod nu := by
apply integral_mono_ae (hip.const_mul _) hl
filter_upwards [hu', hv'] with p hpu hpv
simpa only [mul_assoc] using binaryFusionLoss_lower_bound hpu hpv htheta
rw [integral_const_mul,
integral_prod_mul (μ := mu) (ν := nu) (fun u : ℝ => u ^ 2) (fun v : ℝ => v ^ 2)] at hb
simpa only [mul_assoc] using hb
theorem integral_binaryFusion_le_sum_sq_sub_loss {mu nu : Measure ℝ}
[IsProbabilityMeasure mu] [IsProbabilityMeasure nu] {theta : ℝ}
(hu : ∀ᵐ u ∂mu, |u| ≤ theta) (hv : ∀ᵐ v ∂nu, |v| ≤ theta)
(htheta : theta < 1) (hsym : Measure.map (fun v : ℝ => -v) nu = nu) :
(∫ p : ℝ × ℝ, (p.1 + p.2) ^ 2 / (1 + p.1 * p.2) ∂mu.prod nu) ≤
(∫ u : ℝ, u ^ 2 ∂mu) + (∫ v : ℝ, v ^ 2 ∂nu) -
2 * (1 - theta ^ 2) * (∫ u : ℝ, u ^ 2 ∂mu) * (∫ v : ℝ, v ^ 2 ∂nu) := by
have hu1 : ∀ᵐ u ∂mu, |u| < 1 := hu.mono (fun _ h => lt_of_le_of_lt h htheta)
have hv1 : ∀ᵐ v ∂nu, |v| < 1 := hv.mono (fun _ h => lt_of_le_of_lt h htheta)
rw [integral_binaryFusion_symmetric_square hu1 hv1 hsym]
exact sub_le_sub_left (integral_binaryFusionLoss_lower_bound hu hv htheta) _
end Problem367
end
end OAI