roseking's picture
Add lean source and binary files (part 127)
135052a verified
Raw History Blame Contribute Delete
6.32 kB
import Mathlib
import OAI.Probability.SKBarriers.Coverage.EnergyTail
import OAI.Probability.SKBarriers.Coverage.FiniteCoverage
import OAI.Probability.SKBarriers.Coverage.WeightedCoverage
namespace OAI
section
section
noncomputable section
open scoped BigOperators
open MeasureTheory ProbabilityTheory Filter Set
namespace SK.Analytic
theorem finiteOverlapMass_mono {n : ℕ} (ν : Config n → ℝ)
(hν : ∀ x, 0 ≤ ν x) {T S : Finset (Config n)} (hTS : T ⊆ S) (q : ℝ) :
finiteOverlapMass ν T q ≤ finiteOverlapMass ν S q := by
unfold finiteOverlapMass
apply le_trans (Finset.sum_le_sum (g := fun x => ∑ y ∈ S,
if q ≤ |overlap x y| then ν x*ν y else 0) (fun x _ => ?_))
· exact Finset.sum_le_sum_of_subset_of_nonneg hTS (fun x _ _ =>
Finset.sum_nonneg (fun y _ => by split_ifs; exact mul_nonneg (hν x) (hν y); rfl))
· exact Finset.sum_le_sum_of_subset_of_nonneg hTS (fun y _ _ => by split_ifs; exact mul_nonneg (hν x) (hν y); rfl)
theorem weightedOverlapMass_conditional_mono {n : ℕ} (ν : Config n → ℝ)
(hν : ∀ x, 0 ≤ ν x) {T S : Finset (Config n)} (hTS : T ⊆ S)
(hT : 0 < finiteMass ν T) (q : ℝ) :
weightedOverlapMass (conditionalWeights ν T) q ≤
(finiteMass ν S/finiteMass ν T)^2*weightedOverlapMass (conditionalWeights ν S) q := by
have hS : 0 < finiteMass ν S := hT.trans_le (finiteMass_mono ν hν hTS)
rw [weightedOverlapMass_conditional,weightedOverlapMass_conditional]
have HE : (finiteMass ν S/finiteMass ν T)^2*(finiteOverlapMass ν S q/(finiteMass ν S)^2) =
finiteOverlapMass ν S q/(finiteMass ν T)^2 := by
field_simp
rw [HE]
exact div_le_div_of_nonneg_right (finiteOverlapMass_mono ν hν hTS q) (sq_nonneg _)
def energyTrim {n : ℕ} (J : Disorder n) (e : ℝ) (S : Finset (Config n)) : Finset (Config n) :=
S.filter (fun x => hamiltonian J x ≤ (n:ℝ)*e)
theorem gibbsMass_energyTrim {n : ℕ} (β e : ℝ) (J : Disorder n) (S : Finset (Config n)) :
finiteMass (gibbs β J) S-gibbsEnergyTail β e J ≤
finiteMass (gibbs β J) (energyTrim J e S) := by
have Hadd := Finset.sum_filter_add_sum_filter_not S
(fun x => hamiltonian J x ≤ (n:ℝ)*e) (gibbs β J)
have Hle : (∑ x ∈ S.filter (fun x => ¬hamiltonian J x ≤ (n:ℝ)*e), gibbs β J x) ≤
gibbsEnergyTail β e J := by
unfold gibbsEnergyTail
simp only [not_le]
rw [Finset.sum_filter]
exact Finset.sum_le_sum_of_subset_of_nonneg (Finset.subset_univ S)
(fun x _ _ => by split_ifs; exact le_of_lt (gibbs_pos β J x); rfl)
change (∑ x ∈ S, gibbs β J x)-gibbsEnergyTail β e J ≤
∑ x ∈ S.filter (fun x => hamiltonian J x ≤ (n:ℝ)*e), gibbs β J x
linarith
theorem partition_ratio_mix {n : ℕ} (β a b : ℝ) (J G : Disorder n) :
partition β (mixDisorder a b (J,G))/partition β J =
∑ x : Config n, gibbs β J x*
Real.exp (β*(a-1)*hamiltonian J x+β*b*hamiltonian G x) := by
unfold partition gibbs
rw [Finset.sum_div]
apply Finset.sum_congr rfl
intro x _
rw [hamiltonian_mix]
rw [div_mul_eq_mul_div,← Real.exp_add]
congr 2
ring
theorem conditionalWeightedPartition {n : ℕ} (ν : Config n → ℝ)
(S : Finset (Config n)) (hS : 0 < finiteMass ν S) (b : ℝ) (G : Disorder n) :
finiteMass ν S*weightedPartition (conditionalWeights ν S) b G =
∑ x ∈ S, ν x*Real.exp (b*hamiltonian G x) := by
unfold weightedPartition conditionalWeights
rw [Finset.mul_sum]
have HE (x : Config n) : finiteMass ν S*((if x ∈ S then ν x/finiteMass ν S else 0)*
Real.exp (b*hamiltonian G x)) =
if x ∈ S then ν x*Real.exp (b*hamiltonian G x) else 0 := by
split_ifs
· field_simp
· ring
simp_rw [HE]
rw [← Finset.sum_filter]
simp only [Finset.filter_mem_eq_inter,Finset.univ_inter]
theorem partition_ratio_mix_lower {n : ℕ} {β a : ℝ} (hβ : 0 ≤ β) (ha : a ≤ 1)
(b e : ℝ) (J G : Disorder n) (T : Finset (Config n))
(hT : 0 < finiteMass (gibbs β J) T)
(henergy : ∀ x ∈ T, hamiltonian J x ≤ (n:ℝ)*e) :
finiteMass (gibbs β J) T*Real.exp (β*(a-1)*(n:ℝ)*e)*
weightedPartition (conditionalWeights (gibbs β J) T) (β*b) G ≤
partition β (mixDisorder a b (J,G))/partition β J := by
rw [partition_ratio_mix]
calc
_ = Real.exp (β*(a-1)*(n:ℝ)*e)*
∑ x ∈ T, gibbs β J x*Real.exp ((β*b)*hamiltonian G x) := by
rw [← conditionalWeightedPartition (gibbs β J) T hT]
ring
_ ≤ ∑ x ∈ T, gibbs β J x*
Real.exp (β*(a-1)*hamiltonian J x+β*b*hamiltonian G x) := by
rw [Finset.mul_sum]
apply Finset.sum_le_sum
intro x hx
rw [Real.exp_add]
have he : β*(a-1)*(n:ℝ)*e ≤ β*(a-1)*hamiltonian J x := by
have HH := mul_le_mul_of_nonpos_left (henergy x hx)
(mul_nonpos_of_nonneg_of_nonpos hβ (sub_nonpos.mpr ha))
nlinarith
have HH := mul_le_mul_of_nonneg_left (Real.exp_le_exp.mpr he)
(mul_nonneg (gibbs_pos β J x).le (Real.exp_nonneg ((β*b)*hamiltonian G x)))
nlinarith
_ ≤ _ := Finset.sum_le_sum_of_subset_of_nonneg (Finset.subset_univ T)
(fun x _ _ => mul_nonneg (gibbs_pos β J x).le (Real.exp_nonneg _))
theorem logPartition_mix_lower {n : ℕ} {β a : ℝ} (hβ : 0 ≤ β) (ha : a ≤ 1)
(b e : ℝ) (J G : Disorder n) (T : Finset (Config n))
(hT : 0 < finiteMass (gibbs β J) T)
(henergy : ∀ x ∈ T, hamiltonian J x ≤ (n:ℝ)*e) :
Real.log (finiteMass (gibbs β J) T)+β*(a-1)*(n:ℝ)*e+
Real.log (weightedPartition (conditionalWeights (gibbs β J) T) (β*b) G) ≤
logPartition β (mixDisorder a b (J,G))-logPartition β J := by
have hw := weightedPartition_pos (conditionalWeights (gibbs β J) T)
(conditionalWeights_nonneg (gibbs β J) (fun x => (gibbs_pos β J x).le) T)
(conditionalWeights_sum (gibbs β J) T hT) (β*b) G
have H := Real.log_le_log (mul_pos (mul_pos hT (Real.exp_pos _)) hw)
(partition_ratio_mix_lower hβ ha b e J G T hT henergy)
rw [Real.log_mul (mul_ne_zero hT.ne' (Real.exp_ne_zero _)) hw.ne',
Real.log_mul hT.ne' (Real.exp_ne_zero _),Real.log_exp,
Real.log_div (partition_pos β (mixDisorder a b (J,G))).ne' (partition_pos β J).ne'] at H
exact H
end SK.Analytic
end
end
end
end OAI