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LeanPool.Superdiffusion.Algsuperdiff.Section3.Provider.ErrorComparison.ToLambdas

ToLambdas #

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

Provider: comparing the homogenization error with the ellipticity constants #

This file proves a local version of the lower half of Lemma l.mathcal.E.to.Lambdas of ABK26:

For every m ∈ ℤ, q ∈ [1,∞], s ∈ (0,∞), coefficient field a and scalar σ₀ ∈ (0,∞), (1/2) 𝓔_{s,∞,q}(□_m; a, σ₀)² ≤ max{σ₀^{-1} Λ_{s,q}(□_m;a), σ₀ λ_{s,q}^{-1}(□_m;a)} ≤ 1 + 2 𝓔_{s,∞,q}(□_m;a,σ₀)² + 2^{1/2} 𝓔_{s,∞,q}(□_m;a,σ₀), and moreover, in the case q = 2, 1 + (1/2) 𝓔_{s,∞,2}(□_m;a,σ₀)² ≤ max{σ₀^{-1} Λ_{s,2}(□_m;a), σ₀ λ_{s,2}^{-1}(□_m;a)}.

Scope #

Proved here, for q = 2:

Not proved here: the second inequality of e.bound.Lambdas.by.Es (the upper bound max{…} ≤ 1 + 2𝓔² + 2^{1/2}𝓔), whose printed proof runs through the operator inequalities e.xminusonetimesxminusonesquared.sstar and e.xminusonetimesxminusonesquared.b and an eigenvector analysis of f(x) = x^{-1}(x-1)^2; and the aggregation for general q ∈ [1,∞], whose printed "triangle inequality" step is Minkowski's inequality in L^{q/2} and is available in that direction only for q ≥ 2.

Main results #

References #

Suprema over finite sets #

A value of f on a finite set is below the finsetSupReal supremum.

The doubled response with scalar normalizers #

Reflecting the second half of the normalized probe flips exactly the off-diagonal contributions to the doubled response, so the two responses add up to the diagonal quadratic form of b(U;a) and σ_*^{-1}(U;a).

This is the block-algebra content of the printed identity e.J.by.f for a scalar comparator, written with the scaling parameter c (which will be σ₀^{1/2}).

The sharp one-cube bound of e.J.by.f (ABK26) for a scalar comparator: the normalized doubled response of a unit probe is bounded by the σ₀-weighted coarse ellipticity norms, minus 2.

The one-cube estimate for the public normalized response #

The squared Euclidean norm of a doubled vector splits into the two halves.

The one-cube estimate: for the scalar comparator σ Id, the normalized block-response maximum is bounded by the weighted coarse ellipticity norms minus 2. This is the sharp form of the second inequality of e.J.by.f (ABK26).

Aggregation at q = 2 #