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LeanPool.Superdiffusion.Algsuperdiff.Section4.Provider.Schauder.CubeSchauderAeCutoff

CubeSchauderAeCutoff #

Ported from scottnarmstrong/Superdiffusion at 7f20476385d4af418f110303085ad0740c9eba46. Module imports, support namespace and local option directives adapted for Lean Pool.

Cube Schauder: the cutoff pairing of the a.e. identification #

CubeSchauderAssembly.exists_zeroDatumWitness_of_campanato consumes the almost-everywhere identification Ψ = ∇w of the Campanato slope field with the H¹ weak gradient. The quantitative half of that identification is the statement proved here: for the smooth cutoff η that is 1 on the Euclidean ball of radius 3^j/8 around z and supported in the ball of radius 3^j/4,

  |∫_{□_m} (∂_i w − g_i) η|  =  |∫_{□_m} (w − ℓ) ∂_i η|
                             ≤  C(d)·3^{-j}·‖w − ℓ‖_{L²(W_j)}·|W_j| ,

where ℓ = (c,g) is any affine competitor and W_j = (z+□_j) ∩ □_m. Fed with the affine minimizer of the window and the Campanato datum E(w, W_j) ≤ K√(3^j), the right-hand side is C(d)·K·√(3^j)·|W_j|, which is o(|W_j|) — the ingredient the Lebesgue-point argument needs.

The three components are:

References #

1. The L¹ ≤ L² comparison on a window #

L¹ ≤ L² on a window of finite positive measure.

∫_W |f| ≤ ‖f‖_{L²(W)}·|W|^{1/2}. Proved from the elementary pointwise bound |f| ≤ (t f² + t⁻¹)/2, integrated and optimized at t = |W|^{1/2}/‖f‖_{L²(W)}; the degenerate branch ‖f‖_{L²(W)} = 0 is handled separately.

√(∫_W f²) = ‖f‖_{L²(W)}·√|W| on a window of finite positive measure.

2. The Campanato cutoff #

The Campanato cutoff at z and scale j: CoarseGraining's quantitative ball cutoff, equal to 1 on the Euclidean ball of radius 3^j/8 and supported in the ball of radius 3^j/4.

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    The constant of the cutoff pairing: 16·d·θ', where θ' is the transition profile's derivative bound.

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      Off the Euclidean ball of radius 3^j/2 the cutoff's derivative vanishes.

      The Euclidean ball is contained in the sup-metric cube of the same radius, hence in the translated triadic cube of scale j.