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LeanPool.HighContrastHomogenization.Support.Besov.Poincare.Descendants

Coarse-graining support: Support.Besov.Poincare.Descendants #

Imported from the Apache-2.0 CoarseGraining development at commit c7ddd76c08ade64fed1b8d2ca51be14dfee8deb4.

Descendant-local analytic hypothesis where the local dual mean-zero Poincare estimate is supplied against the finite projection of g on each descendant cube at the exact depth that matches the multiscale corridor's M - j indexing.

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Instances For
    theorem HCPolySupport.descendantsAverage_L2_sum_le_sum_descendantsAverage_L2 {d : ℕ} {ι : Type u_1} (Q : TriadicCube d) (j : ℕ) (s : Finset ι) (A : TriadicCube d → ι → ℝ) (hA : ∀ R ∈ descendantsAtDepth Q j, ∀ i ∈ s, 0 ≤ A R i) :
    (descendantsAverage Q j fun (R : TriadicCube d) => (∑ i ∈ s, A R i) ^ 2) ^ (1 / 2) ≤ ∑ i ∈ s, (descendantsAverage Q j fun (R : TriadicCube d) => A R i ^ 2) ^ (1 / 2)
    theorem HCPolySupport.cubeBesovDepthSeminorm_two_le_sum_shifted_of_local_circ_bound {d : ℕ} (Q : TriadicCube d) (s C : ℝ) (u g : Vec d → ℝ) (j N : ℕ) (hC : 0 ≤ C) (hlocal : ∀ R ∈ descendantsAtDepth Q j, cubeBesovOscillation R 2 u ≤ C * ∑ n ∈ Finset.range (N + 1), cubeBesovCircDepthSeminorm R 1 2 g n) :