OAI/GroupTheory/PolycyclicRecognition/Analysis/AffineSheetAgreement.lean

GitHub source file

5 declarations, 0 contain sorry, 0 declared as axioms

Local agreement identifies affine sheet profiles on the whole domain

  1. theorem OAI.PolycyclicRecognition.affine_height_profiles_eq_of_open_agreement line 16

    Source documentation

    Agreement above any nonempty open set identifies both the label and the offset of affine height profiles. Surjectivity makes the height map open.

    theorem affine_height_profiles_eq_of_open_agreement
        {V W Z : Type*} [NormedAddCommGroup V] [NormedSpace ℝ V] [FiniteDimensional ℝ V]
        [NormedAddCommGroup W] [NormedSpace ℝ W] [FiniteDimensional ℝ W]
        [NormedAddCommGroup Z] [NormedSpace ℝ Z]
        (q : V →L[ℝ] W) (hq : Function.Surjective q)
        (A B : W →L[ℝ] Z) (b c : Z)
        (U O : Set V) (hO : IsOpen O) (hne : O.Nonempty) (hOU : O ⊆ U)
        (f g : V → Z)
        (hf : ∀ z ∈ U, f z = A (q z) + b) (hg : ∀ z ∈ U, g z = B (q z) + c)
        (heq : ∀ z ∈ O, f z = g z) :
        A = B ∧ b = c ∧ ∀ z ∈ U, f z = g z := by
    …
  2. theorem OAI.PolycyclicRecognition.mem_support_map_of_continuous line 37

    Source documentation

    A continuous image of a supported sample is supported by its image law.

    theorem mem_support_map_of_continuous
        {X Y : Type*} [TopologicalSpace X] [TopologicalSpace Y]
        [MeasurableSpace X] [MeasurableSpace Y] [BorelSpace X] [BorelSpace Y]
        (μ : ProbabilityMeasure X) (f : X → Y) (hf : Continuous f)
        {x : X} (hx : x ∈ (μ : Measure X).support) :
        f x ∈ (ProbabilityMeasure.map μ f : Measure Y).support := by
    …
  3. theorem OAI.PolycyclicRecognition.sampled_projection_tendsto line 54

    Source documentation

    Projection of a weak limit of sampled pairs is the weak limit of the projected samples, with no change of sampling law.

    theorem sampled_projection_tendsto
        {A X Y : Type*} [MeasurableSpace A]
        [TopologicalSpace X] [MeasurableSpace X] [BorelSpace X]
        [TopologicalSpace Y] [MeasurableSpace Y] [BorelSpace Y]
        (μ : ℕ → ProbabilityMeasure A) (p : ℕ → A → X) (hp : ∀ n, Measurable (p n))
        (ν : ProbabilityMeasure X)
        (hν : Tendsto (fun n => ProbabilityMeasure.map (μ n) (p n)) atTop (𝓝 ν))
        (f : X → Y) (hf : Continuous f) :
        Tendsto (fun n => ProbabilityMeasure.map (μ n) (f ∘ p n)) atTop
          (𝓝 (ProbabilityMeasure.map ν f)) := by
    …
  4. theorem OAI.PolycyclicRecognition.supported_profile_pair_eq_of_ae_discrepancy line 72

    Source documentation

    A uniform vanishing discrepancy for sampled profile pairs is inherited by every pair in the support of a weak limit.

    theorem supported_profile_pair_eq_of_ae_discrepancy
        {V W A : Type*} [TopologicalSpace V] [LocallyCompactSpace V]
        [PseudoMetricSpace W] [MeasurableSpace A]
        (S : Set C(V, W)) [MeasurableSpace (S × S)] [BorelSpace (S × S)]
        [HasOuterApproxClosed (S × S)]
        (samples : ℕ → ProbabilityMeasure A) (pair : ℕ → A → S × S)
        (hp : ∀ n, Measurable (pair n)) (ν : ProbabilityMeasure (S × S))
        (hν : Tendsto (fun n => ProbabilityMeasure.map (samples n) (pair n)) atTop (𝓝 ν))
        (z : V) (ε : ℕ → ℝ) (hε : Tendsto ε atTop (𝓝 0))
        (hbound : ∀ᶠ n in atTop, ∀ᵐ a ∂(samples n : Measure A),
          dist ((pair n a).1.val z) ((pair n a).2.val z) ≤ ε n)
        {f : S × S} (hf : f ∈ (ν : Measure (S × S)).support) :
    …
  5. theorem OAI.PolycyclicRecognition.supported_profile_pair_eq_of_uniform_discrepancy line 98

    Source documentation

    Pointwise sample bounds imply the almost-everywhere version.

    theorem supported_profile_pair_eq_of_uniform_discrepancy
        {V W A : Type*} [TopologicalSpace V] [LocallyCompactSpace V]
        [PseudoMetricSpace W] [MeasurableSpace A]
        (S : Set C(V, W)) [MeasurableSpace (S × S)] [BorelSpace (S × S)]
        [HasOuterApproxClosed (S × S)]
        (samples : ℕ → ProbabilityMeasure A) (pair : ℕ → A → S × S)
        (hp : ∀ n, Measurable (pair n)) (ν : ProbabilityMeasure (S × S))
        (hν : Tendsto (fun n => ProbabilityMeasure.map (samples n) (pair n)) atTop (𝓝 ν))
        (z : V) (ε : ℕ → ℝ) (hε : Tendsto ε atTop (𝓝 0))
        (hbound : ∀ᶠ n in atTop, ∀ a,
          dist ((pair n a).1.val z) ((pair n a).2.val z) ≤ ε n)
        {f : S × S} (hf : f ∈ (ν : Measure (S × S)).support) :
    …