LargeCubeLinfty #
Ported from scottnarmstrong/Superdiffusion at 7f20476385d4af418f110303085ad0740c9eba46. Module imports, support namespace and local option directives adapted for Lean Pool.
The large-cube L∞ bound: ABK26's e.km.kn.Linfty #
ABK26 upgrades the small-cube estimate e.km.kn.Linfty.smallcube to the large
cube cu_l: for n < m ≤ l,
‖k_m - k_n‖²_{L∞(cu_l)} ≤ O_{Γ₁}(C (l-n) min{γ^{-1}, m-n} 3^{2γm}),
by covering cu_l with translated copies of the small cube and applying the
maximum lemma l.maximums.Gamma.s, which costs the amplitude factor
(log N)^{1/2} with N the number of copies.
As in the small-cube layer, the statement is split into a deterministic part
and a probabilistic part, because the finite increment k_m - k_n = ∑_{k ∈ (n,m]} j_k is not itself a shell and so carries no localCubeControl gauge.
matrixOperatorNorm_finiteShellIncrement_le_largeCubeSupBoundis the deterministic part: at every point ofcu_lthe increment is dominated by the single random variablelargeCubeSupBound l n m, the maximum of the translated small-cube bounds over the covering family.isBigOWith_gammaSigma_one_largeCubeSupBound_sqis the source display for the square of that random variable.
Composing the two gives the source display for every realization: the left-hand
side of e.km.kn.Linfty is the essential supremum over cu_l of the squared
matrix norm of the increment, which is at most largeCubeSupBound l n m ^ 2
pointwise on cu_l.
The covering family is explicit: the shifts are the lattice points of
3^{n-1} ℤ^d with coordinates bounded by 3^{(l-n+1)^+}, and each translated
open cube has scale n. Rounding each coordinate to the nearest lattice point
moves it by at most 3^{n-1}/2 < 3^n/2, so the translated open cubes really
do cover the whole of cu_l, with no boundary exception. The count is
N = (2·3^{l-n+1} + 1)^d, so log N ≤ 3 d (l-n) log 3, which is the source's
(l-n) factor with an explicit dimensional constant.
Deviations from the source, all absorbed by its C(d):
- the covering lattice has spacing
3^{n-1}rather than3^n, which is what makes the open cover exact; this multipliesNby3^dand is absorbed by the constant; - the amplitude factor is
(3 max{1, log N})^{1/2}rather than(log N)^{1/2}, from the proved cardinality-free maximum lemma.
Main definitions #
Algsuperdiff.Section3.Provider.Stream.translatedIncrementSupBoundAlgsuperdiff.Section3.Provider.Stream.subcubeShiftsAlgsuperdiff.Section3.Provider.Stream.largeCubeSupBoundAlgsuperdiff.Section3.Provider.Stream.largeCubeLinftyConst
Main results #
Algsuperdiff.Section3.Provider.Stream.matrixOperatorNorm_finiteShellIncrement_le_largeCubeSupBound
Algsuperdiff.Section3.Provider.Stream.isBigOWith_gammaSigma_largeCubeSupBoundAlgsuperdiff.Section3.Provider.Stream.isBigOWith_gammaSigma_one_largeCubeSupBound_sq
References #
- ABK26,
e.km.kn.Linfty. - ABK26,
e.km.kn.Linfty.smallcube. - ABK26, Lemma
l.maximums.Gamma.s.
The stream increment at an arbitrary base point #
ABK26's e.k.ell.upscales, entrywise at an arbitrary base point z: for
n < m and every matrix entry,
∑_{k = n+1}^m j_k(z)_{il} = O_{Γ₂}(C min{γ^{-1/2}, (m-n)^{1/2}} 3^{γm}),
with an amplitude that does not depend on z.
ABK26's e.k.ell.upscales at an arbitrary base point z:
|(k_m - k_n)(z)| ≤ O_{Γ₂}(C min{γ^{-1/2}, (m-n)^{1/2}} 3^{γm}),
with |·| the exact Euclidean matrix operator norm. This is the translated
form of isBigOWith_gammaSigma_originIncrement; the amplitude is the same,
which is where the stationarity half of (J1) enters through
isBigO_gammaSigma_shell_entry.
The small-cube bound on a translated cube #
The shell sequence translated by a fixed vector.
Equations
Instances For
The random variable dominating ‖k_m - k_n‖_{L∞(z + cu_n)}: the small-cube
bound incrementSupBound of the translated shell sequence.
Equations
- One or more equations did not get rendered due to their size.
Instances For
Translating the shell sequence translates the finite stream increment.
The explicit form of the translated small-cube bound: the increment value at
the base point z plus the geometric factor times the summed first-derivative
gauges on the translated cube.
The small-cube bound is nonnegative.
The deterministic half of ABK26's e.k.ell.upscales.infty on a translated
cube: at every point of z + cu_n the finite stream increment is dominated by
translatedIncrementSupBound z n m.
The value of the finite stream increment at a fixed point is a measurable random variable.
The probabilistic half of ABK26's e.k.ell.upscales.infty on a translated
cube, at the same amplitude as at the origin.
A finite cover of the large cube by translated small cubes #
The per-coordinate lattice radius of the covering family: the shifts range
over the lattice 3^{n-1} ℤ^d with integer coordinates bounded by
3^{(l-n+1)^+}.
Instances For
The lattice point of 3^{n-1} ℤ^d attached to an index.
Equations
- Algsuperdiff.Section3.Provider.Stream.subcubeCenter n p i = 3 ^ (n - 1) * ↑(p i)
Instances For
The covering property: every point of the open cube cu_l lies in one of
the translated open cubes z + cu_n of the family. Rounding each coordinate
to the nearest point of the lattice 3^{n-1} ℤ^d moves it by at most
3^{n-1}/2, which is strictly less than the half-side 3^n/2, so the open
cubes already cover cu_l.
The maximum over the cover #
The single random variable dominating ‖k_m - k_n‖_{L∞(cu_l)}: the maximum
of the translated small-cube bounds over the covering family.
Equations
- One or more equations did not get rendered due to their size.
Instances For
The deterministic half of ABK26's e.km.kn.Linfty: at every point of cu_l
the finite stream increment is dominated by largeCubeSupBound l n m.
The maximum over the cover, with the amplitude factor supplied by the proved cardinality-free maximum lemma.
The source display #
The universal factor produced by the maximum over the cover.
Instances For
The explicit dimensional constant of ABK26's e.km.kn.Linfty.
Equations
Instances For
The count of the covering family is (2·3^{l-n+1} + 1)^d, so the amplitude
factor of the maximum lemma is at most C(d) (l-n). This is the source's
(l-n) factor, with an explicit dimensional constant.
ABK26's e.km.kn.Linfty:
‖k_m - k_n‖²_{L∞(cu_l)} ≤ O_{Γ₁}(C (l-n) min{γ^{-1}, m-n} 3^{2γm}).
The left-hand side is represented, as in the proved small-cube layer, by the
random variable largeCubeSupBound l n m, which dominates the matrix operator
norm of k_m - k_n at every point of cu_l by
matrixOperatorNorm_finiteShellIncrement_le_largeCubeSupBound. The tail
exponent moves from σ = 2 to σ = 1 through the proved power rule
e.powerofGammasigma at p = 2, exactly as in the small-cube display.