import OAI.Probability.InvariantIsing.Arrays.ReplicaContractions namespace OAI /-! Two-replica Gibbs calculus used by the off-diagonal Ward equation. -/ noncomputable section open IsingPerceptron MeasureTheory open scoped BigOperators namespace InvariantIsing variable {S : Type*} [Fintype S] omit [Fintype S] in lemma GibbsReference_pair {w : S → ℝ} (hw : GibbsReference w) : GibbsReference (fun x : S × S => w x.1 * w x.2) := by refine ⟨fun x => mul_nonneg (hw.1 x.1) (hw.1 x.2), ?_⟩ obtain ⟨σ, hσ⟩ := hw.2 exact ⟨(σ, σ), mul_pos hσ hσ⟩ lemma finitePartition_pair (w H : S → ℝ) : finitePartition (fun x : S × S => w x.1 * w x.2) (fun x => H x.1 + H x.2) = finitePartition w H * finitePartition w H := by simp only [finitePartition, Fintype.sum_prod_type, Real.exp_add, Finset.sum_mul_sum] apply Finset.sum_congr rfl intro σ _ apply Finset.sum_congr rfl intro τ _ ring lemma finiteGibbs_pair (w H : S → ℝ) (x : S × S) : finiteGibbs (fun x : S × S => w x.1 * w x.2) (fun x => H x.1 + H x.2) x = finiteGibbs w H x.1 * finiteGibbs w H x.2 := by simp only [finiteGibbs, finitePartition_pair, Real.exp_add, div_eq_mul_inv, mul_inv_rev] ring lemma gibbsAverage_pair (w H : S → ℝ) (A : S → S → ℝ) : gibbsAverage (fun x : S × S => w x.1 * w x.2) (fun x => H x.1 + H x.2) (fun x => A x.1 x.2) = gibbsPairAverage w H A := by simp only [gibbsAverage, Fintype.sum_prod_type, finiteGibbs_pair, gibbsPairAverage] lemma gibbsPairAverage_add (w H : S → ℝ) (A B : S → S → ℝ) : gibbsPairAverage w H (fun σ τ => A σ τ + B σ τ) = gibbsPairAverage w H A + gibbsPairAverage w H B := by simp only [gibbsPairAverage, mul_add, Finset.sum_add_distrib] lemma gibbsPairAverage_fst {w : S → ℝ} (hw : GibbsReference w) (H A : S → ℝ) : gibbsPairAverage w H (fun σ _ => A σ) = gibbsAverage w H A := by calc _ = ∑ σ, finiteGibbs w H σ * A σ * ∑ τ, finiteGibbs w H τ := by simp only [gibbsPairAverage, Finset.mul_sum] apply Finset.sum_congr rfl intro σ _ apply Finset.sum_congr rfl intro τ _ ring _ = _ := by simp only [finiteGibbs_sum hw H, mul_one, gibbsAverage] lemma gibbsPairAverage_swap (w H : S → ℝ) (A : S → S → ℝ) : gibbsPairAverage w H (fun σ τ => A τ σ) = gibbsPairAverage w H A := by unfold gibbsPairAverage rw [Finset.sum_comm] apply Finset.sum_congr rfl intro σ _ apply Finset.sum_congr rfl intro τ _ ring lemma gibbsPairAverage_snd {w : S → ℝ} (hw : GibbsReference w) (H A : S → ℝ) : gibbsPairAverage w H (fun _ τ => A τ) = gibbsAverage w H A := by rw [← gibbsPairAverage_fst hw H A] exact gibbsPairAverage_swap w H (fun σ _ => A σ) lemma gibbsAverage_pair_sum {w : S → ℝ} (hw : GibbsReference w) (H A : S → ℝ) : gibbsAverage (fun x : S × S => w x.1 * w x.2) (fun x => H x.1 + H x.2) (fun x => A x.1 + A x.2) = 2 * gibbsAverage w H A := by rw [gibbsAverage_pair w H (fun σ τ => A σ + A τ), gibbsPairAverage_add, gibbsPairAverage_fst hw, gibbsPairAverage_snd hw] ring /-- The two-replica derivative has one Hamiltonian insertion in each existing replica and two Gibbs normalization subtractions. -/ lemma hasDerivAt_gibbsPairAverage {w : S → ℝ} (hw : GibbsReference w) {H : ℝ → S → ℝ} {A : ℝ → S → S → ℝ} {dH : S → ℝ} {dA : S → S → ℝ} {t : ℝ} (hH : ∀ σ, HasDerivAt (fun s => H s σ) (dH σ) t) (hA : ∀ σ τ, HasDerivAt (fun s => A s σ τ) (dA σ τ) t) : HasDerivAt (fun s => gibbsPairAverage w (H s) (A s)) (gibbsPairAverage w (H t) dA + gibbsPairAverage w (H t) (fun σ τ => A t σ τ * (dH σ + dH τ)) - 2 * gibbsPairAverage w (H t) (A t) * gibbsAverage w (H t) dH) t := by have hd := hasDerivAt_gibbsAverage (GibbsReference_pair hw) (fun x : S × S => (hH x.1).add (hH x.2)) (fun x : S × S => hA x.1 x.2) simp only [Pi.add_apply, gibbsAverage_pair] at hd rw [gibbsAverage_pair w (H t) (fun σ τ => A t σ τ * (dH σ + dH τ)), gibbsAverage_pair w (H t) (fun σ τ => dH σ + dH τ)] at hd rw [gibbsPairAverage_add, gibbsPairAverage_fst hw, gibbsPairAverage_snd hw] at hd convert hd using 1 ring end InvariantIsing end end OAI