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LeanPool.HighContrastHomogenization.Support.Book.Ch03.ABK26.LocalCoarseGrainingAggregation

Coarse-graining support: Support.Book.Ch03.ABK26.LocalCoarseGrainingAggregation #

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

Finite-p local coarse-graining aggregation algebra #

This file contains the elementary ENNReal power identities used to assemble the finite-p local coarse-graining estimate. It has no PDE content.

The normalized ENNReal average over descendants at a prescribed physical triadic scale.

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    At an admissible physical scale, the physical-scale average is precisely the canonical depth-descendant average.

    Raising the running-scale negative Besov seminorm to the finite exponent recovers its unrooted depth-energy series.

    Raising the weighted local symmetric-energy aggregation to the finite exponent exposes its exact running physical-scale series.

    Normalized ENNReal descendant averages compose exactly across one triadic generation.

    Normalized ENNReal descendant averages compose at arbitrary finite depths.

    theorem HCPolySupport.Book.Ch03.ABK26.geometricDiscount_rpow_neg_inv_le_twentyFive_mul_inv {delta alpha : ℝ} (hdelta : 0 < delta) (hdelta_le : delta ≤ 1) (halpha : 2 ≤ alpha) :
    (Ch02.geometricDiscount delta alpha).rpow (-1 / alpha) ≤ 25 * delta⁻¹

    The rooted geometric-tail loss is bounded by a single inverse gap, with a constant independent of the finite exponent.

    The preceding uniform tail bound specialized to a finite Lp exponent.

    theorem HCPolySupport.Book.Ch03.ABK26.ENNReal_tsum_triadic_rpow_root_le_twentyFive_mul_inv {delta alpha : ℝ} (hdelta : 0 < delta) (hdelta_le : delta ≤ 1) (halpha : 2 ≤ alpha) :
    (∑' (j : ℕ), ENNReal.ofReal (Real.rpow 3 (-delta * alpha)) ^ j) ^ (1 / alpha) ≤ ENNReal.ofReal (25 * delta⁻¹)

    ENNReal geometric-series form of the exponent-uniform rooted tail bound. The ratio is the triadic decay at gap delta.

    Finite-Lp specialization of the ENNReal triadic tail bound.