OAI/Probability/InvariantIsing/Gaussian/GaussianGGBounds.lean

GitHub source file

4 declarations, 0 contain sorry, 0 declared as axioms

Gaussian GG estimates retaining the spectral diagonal error.

  1. lemma OAI.InvariantIsing.randomReplicaAverage_const_mul line 21
  2. lemma OAI.InvariantIsing.randomReplicaAverage_add_bounded line 29
  3. theorem OAI.InvariantIsing.randomReplicaEnergy_full_identity line 50

    Source documentation

    Gaussian integration by parts without imposing a constant diagonal.

    theorem randomReplicaEnergy_full_identity (hν : Measurable ν)
        (A : X → ℕ →₀ ℝ) {B : ℝ} (hA : ∀ x, (A x).sum (fun _ c => c ^ 2) ≤ B)
        (t : ℝ) {r : ℕ} (D : (Fin (r + 1) → X) → ℝ)
        {c : ℝ} (hc : 0 ≤ c) (hD : ∀ σ, |D σ| ≤ c) :
        randomReplicaEnergy P ν A t D = t *
          (randomReplicaAverage P ν A t (fun σ => D σ *
            (∑ i : Fin (r + 1), cylinderCross (A (σ 0)) (A (σ i)))) -
          (r + 1 : ℕ) * randomReplicaAverage P ν A t
            (fun τ : Fin (r + 1 + 1) → X => D (Fin.tail τ) *
              cylinderCross (A ((Fin.tail τ) 0)) (A (τ 0)))) := by
    …
  4. theorem OAI.InvariantIsing.countable_gaussian_gg_bound_variable_diagonal line 88

    Source documentation

    The finite GG error splits into an energy fluctuation and an explicit covariance of the self-overlap. Spectral self-overlaps are not constant before adding their separate deterministic perturbations.

    theorem countable_gaussian_gg_bound_variable_diagonal (hν : Measurable ν)
        (A : X → ℕ →₀ ℝ) {B : ℝ} (hA : ∀ x, (A x).sum (fun _ c => c ^ 2) ≤ B)
        (t b : ℝ) {r : ℕ} (D : (Fin (r + 1) → X) → ℝ)
        {c : ℝ} (hc : 0 ≤ c) (hD : ∀ σ, |D σ| ≤ c)
        (hE : Integrable (fun z : Ω × (ℕ → ℝ) => ∫ x,
          |cylinderField (A x) z.2 - b|
            ∂(ν z.1).tilted (fun x => t * cylinderField (A x) z.2))
          (P.prod gaussianCoordinates)) :
        |t| * |(r + 1 : ℕ) * randomReplicaAverage P ν A t
            (fun τ : Fin (r + 1 + 1) → X => D (Fin.tail τ) *
              cylinderCross (A ((Fin.tail τ) 0)) (A (τ 0))) -
          randomReplicaAverage P ν A t D * randomReplicaAverage P ν A t
    …