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LeanPool.HighContrastHomogenization.Provider.Transport.WhitneySquareWeights

High-contrast homogenization: Provider.Transport.WhitneySquareWeights #

Imported from the Apache-2.0 HighContrastHomogenization development at commit 7a13dbcd8d6609264a713373f5c69ceeac870472.

The square weights of a filling row #

The centered part of the principal cell sum of the grid transport is estimated row by row, and each row contributes through the square weight (ฮฃ_{V โˆˆ ๐’ฑ_r(W;q)} (|V|/|W|)ยฒ)^{1/2}: the finite-range matrix averaging lemma turns a first-moment row bound into a bound on a centered matrix sum only at the cost of replacing the row's weights by their โ„“ยฒ norm. This file proves the two square weights the transport uses, the square-weight bounds for the boundary and bulk cells.

Both are pure counting, and both follow from a single observation: the cells of one row of the filling are aligned cells of the same scale, so they all have the same relative volume, and the sum of squares is that common weight times the row's first-moment sum. The boundary row therefore pays the cross-grid row bound e.two.grid.whitney.volumes, of size 3^{r-j}, once, and the common weight, of size 3^{d(r-j)}, once, for the printed exponent (d+1)(r-j). The bulk row pays only the common weight, the first-moment sum of a disjoint family inside the target being at most one, for the printed exponent -dฮป_j.

The one quantitative ingredient beyond the rows is the determinant ratio of the two grids: the common weight of a scale-r row is |det q|/|det p| ยท 3^{d(r-j)}, and the printed constant C(d,K_hop) must absorb the ratio. It does, because a linear image of the unit cube fits inside a cube of side โˆšd |M|, so |det M| โ‰ค (โˆšd|M|)^d, and the grid ratio dominates |p^{-1}q|. The exhibited constant is

C(d,K_hop) = (6 d^{3/2} K_hop (โˆšd K_hop)^d)^{1/2},

one constant for both displays.

The determinant of a linear map against its operator norm #

The determinant against the operator norm. The image of the unit cube under M lies in the coordinate cube of side โˆšd |M|, and the image has volume |det M|, so |det M| โ‰ค (โˆšd|M|)^d. The dimensional factor is the passage from the Euclidean norm, which the operator norm measures, to the coordinate cube.

The grid ratio is at least one, its base being at least one. Hence any K_hop dominating it is at least one, and the printed constants may be compared across the two square weights.

The common relative volume of a row #

Every cell of a filling row has the same relative volume, in closed form: an aligned scale-a cell of the grid q inside the scale-j target cell of the grid p has relative volume |det q| 3^{ad} / (|det p| 3^{jd}).

theorem HCPolySupport.HighContrast.Transport.sum_sq_relative_volume_eq {d : โ„•} (p q : Mat d) (a j : โ„ค) (y : Vec d) (Z : Finset (Fin d โ†’ โ„ค)) :
โˆ‘ w โˆˆ Z, ((MeasureTheory.volume (adaptedCellAt q a w)).toReal / (MeasureTheory.volume (adaptedCellTranslate p j y)).toReal) ^ 2 = |Matrix.det q| * 3 ^ (a * โ†‘d) / (|Matrix.det p| * 3 ^ (j * โ†‘d)) * โˆ‘ w โˆˆ Z, (MeasureTheory.volume (adaptedCellAt q a w)).toReal / (MeasureTheory.volume (adaptedCellTranslate p j y)).toReal

The sum of squares of a row is the common weight times the row sum. This is the whole content of the two square weights: a row of the filling consists of cells of one scale, so its โ„“ยฒ norm is controlled by its โ„“ยน norm with the common weight as the loss.

theorem HCPolySupport.HighContrast.Transport.relative_volume_le_hop {d : โ„•} {p q : Mat d} (hp : Matrix.PosDef p) {a j : โ„ค} {Khop : โ„} (hK : gridRatio q p โ‰ค Khop) (hd : 1 โ‰ค d) :
|Matrix.det q| * 3 ^ (a * โ†‘d) / (|Matrix.det p| * 3 ^ (j * โ†‘d)) โ‰ค (โˆšโ†‘d * Khop) ^ d * 3 ^ (โ†‘d * (a - j))

The common weight of a row against the grid ratio. The relative volume of a scale-a cell inside the scale-j target is the determinant ratio of the two grids times 3^{d(a-j)}, and the determinant ratio is at most (โˆšd K_hop)^d.

The two square weights #

theorem HCPolySupport.HighContrast.Transport.sum_sq_relative_volume_row_le {d : โ„•} {p q : Mat d} (hd : 1 โ‰ค d) (hp : Matrix.PosDef p) (hq : Matrix.PosDef q) {n j a : โ„ค} (han : a < n) {y : Vec d} {Z : Finset (Fin d โ†’ โ„ค)} (hZ : โ†‘Z = fillingIndex q n (adaptedCellTranslate p j y) a) {Khop : โ„} (hK : gridRatio q p โ‰ค Khop) :
โˆ‘ w โˆˆ Z, ((MeasureTheory.volume (adaptedCellAt q a w)).toReal / (MeasureTheory.volume (adaptedCellTranslate p j y)).toReal) ^ 2 โ‰ค 6 * โ†‘d * โˆšโ†‘d * Khop * (โˆšโ†‘d * Khop) ^ d * 3 ^ ((โ†‘d + 1) * (a - j))

The boundary square weight in squared form. For a row below the starting scale the first-moment sum carries the cross-grid decay 3^{a-j} and the common weight carries 3^{d(a-j)}, for the printed exponent (d+1)(a-j).

theorem HCPolySupport.HighContrast.Transport.sum_sq_relative_volume_row_le_bulk {d : โ„•} {p q : Mat d} (hd : 1 โ‰ค d) (hp : Matrix.PosDef p) (hq : Matrix.PosDef q) {n j a : โ„ค} {y : Vec d} {Z : Finset (Fin d โ†’ โ„ค)} (hZ : โ†‘Z = fillingIndex q n (adaptedCellTranslate p j y) a) {Khop : โ„} (hK : gridRatio q p โ‰ค Khop) :
โˆ‘ w โˆˆ Z, ((MeasureTheory.volume (adaptedCellAt q a w)).toReal / (MeasureTheory.volume (adaptedCellTranslate p j y)).toReal) ^ 2 โ‰ค (โˆšโ†‘d * Khop) ^ d * 3 ^ (โ†‘d * (a - j))

The bulk square weight in squared form. A row is a disjoint family inside the target, so its first-moment sum is at most one and only the common weight survives.

The printed displays #

theorem HCPolySupport.HighContrast.Transport.boundary_square_weights {d : โ„•} {p q : Mat d} (hd : 1 โ‰ค d) (hp : Matrix.PosDef p) (hq : Matrix.PosDef q) {n j a : โ„ค} (han : a < n) {y : Vec d} {Z : Finset (Fin d โ†’ โ„ค)} (hZ : โ†‘Z = fillingIndex q n (adaptedCellTranslate p j y) a) {Khop : โ„} (hK : gridRatio q p โ‰ค Khop) :
โˆš(โˆ‘ w โˆˆ Z, ((MeasureTheory.volume (adaptedCellAt q a w)).toReal / (MeasureTheory.volume (adaptedCellTranslate p j y)).toReal) ^ 2) โ‰ค โˆš(6 * โ†‘d * โˆšโ†‘d * Khop * (โˆšโ†‘d * Khop) ^ d) * 3 ^ ((โ†‘d + 1) * (โ†‘a - โ†‘j) / 2)

The square-weight bound for the boundary cells. At a boundary source scale the โ„“ยฒ norm of the relative volumes of a filling row is at most C(d,K_hop) 3^{(d+1)(r-j)/2}, with

C(d,K_hop) = (6 d^{3/2} K_hop (โˆšd K_hop)^d)^{1/2}.

A boundary scale is one strictly below the scale at which the filling of the target starts; for the filling of a scale-j target with buffer ฮป_j that is exactly the printed condition r < j - ฮป_j.

theorem HCPolySupport.HighContrast.Transport.bulk_square_weights {d : โ„•} {p q : Mat d} (hd : 1 โ‰ค d) (hp : Matrix.PosDef p) (hq : Matrix.PosDef q) {n j lam : โ„ค} {y : Vec d} {Z : Finset (Fin d โ†’ โ„ค)} (hZ : โ†‘Z = fillingIndex q n (adaptedCellTranslate p j y) (j - lam)) {Khop : โ„} (hK : gridRatio q p โ‰ค Khop) :
โˆš(โˆ‘ w โˆˆ Z, ((MeasureTheory.volume (adaptedCellAt q (j - lam) w)).toReal / (MeasureTheory.volume (adaptedCellTranslate p j y)).toReal) ^ 2) โ‰ค โˆš(6 * โ†‘d * โˆšโ†‘d * Khop * (โˆšโ†‘d * Khop) ^ d) * 3 ^ (-โ†‘d * โ†‘lam / 2)

The square-weight bound for the bulk cells. At the bulk source scale j - ฮป the โ„“ยฒ norm of the relative volumes of a filling row is at most C(d,K_hop) 3^{-dฮป/2}, with the same constant

C(d,K_hop) = (6 d^{3/2} K_hop (โˆšd K_hop)^d)^{1/2}

as the boundary display. Unlike the boundary rows this one is unconditional in the scale: it needs only disjointness inside the target, which is why the row at which the filling starts, carrying no cross-grid decay, is still summable.