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.
Equations
- HCPolySupport.Book.Ch03.ABK26.descendantsAtScaleENNAverage Q k F = (↑(HCPolySupport.descendantsAtScale Q k).card)⁻¹ * ∑ R ∈ HCPolySupport.descendantsAtScale Q k, F R
Instances For
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.
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.
Finite-Lp specialization of the ENNReal triadic tail bound.