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LeanPool.ViscositySolutionTheory.AleksandrovDifferentiability.Statements.Cube.Stieltjes.Controls

Ported for Lean Pool: imports relocated and code adapted to Lean/Mathlib v4.34.0.

Concrete Stieltjes endpoint and mass controls #

Each coordinate slice satisfies the source Stieltjes endpoint remainder estimates.

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    Exact endpoint identities for the clamped source-local right-derivative Stieltjes slices.

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      Endpoint comparison bounds for the clamped source-local right-derivative Stieltjes slices.

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        Slice-level endpoint comparisons imply slice-level endpoint control with constant 2, provided the coordinate slices are convex on the source interval.

        Slice-level symmetric-remainder estimate for the clamped source-local right-derivative Stieltjes measures. This is the remaining one-dimensional analytic input before endpoint control.

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          Slice-level symmetric-remainder estimates imply slice-level endpoint control with constant 2.

          Ambient convexity supplies the concrete slicewise endpoint identities.

          Concrete convex endpoint comparisons imply concrete endpoint control with constant 2.

          Symmetric-remainder estimates for the concrete convex source-local Stieltjes slice family.

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            Concrete convex source-local symmetric-remainder estimates imply concrete endpoint control with constant 2.

            The clamped source-local Stieltjes endpoint input implies the abstract one-dimensional endpoint-control input expected by Cube.Maximal.

            Ambient convexity supplies concrete slicewise endpoint control with constant 2.

            The concrete convex source-local Stieltjes endpoint input implies the abstract one-dimensional endpoint-control input expected by Cube.Maximal.

            Fubini plus clamped source-local Stieltjes source mass bounds give the per-coordinate bad-set estimate for localized maximal slices.

            Fubini plus clamped source-local Stieltjes source jump bounds give the per-coordinate bad-set estimate for localized maximal slices.

            Fubini plus symmetric endpoint bounds for clamped source-local Stieltjes functions give the per-coordinate bad-set estimate for localized maximal slices.

            Fubini plus endpoint bounds for the ordinary right derivatives of clamped source-local slices give the per-coordinate bad-set estimate for localized maximal slices.

            theorem AleksandrovDifferentiability.CoordinateSliceMaximalFubiniEstimate.coordinate_bound_of_sourceRightDerivConvexSecantBounds {n : ℕ} {u : SourceCubeSpace n → ℝ} {hmono : ∀ (i : Fin n) ⦃y : SourceCubeSpace n⦄, y ∈ sourceOpenCube n 1 → MonotoneOn (rightDeriv (lineRestriction u (coordinateLineBase i y) (sourceCoordinateVector i))) (Set.Icc (-2) 2)} {Cmax Ccoord osc t : ℝ} (hfubini : CoordinateSliceMaximalFubiniEstimate (coordinateSliceSourceRightDerivStieltjesMeasureFamily u hmono) (2 * Cmax) Ccoord osc t) (hconv : ∀ (i : Fin n) ⦃y : SourceCubeSpace n⦄, y ∈ sourceOpenCube n 1 → ConvexOn ℝ (Set.Ioo (-3) 3) (lineRestriction u (coordinateLineBase i y) (sourceCoordinateVector i))) (hleft : ∀ (i : Fin n) ⦃y : SourceCubeSpace n⦄, y ∈ sourceOpenCube n 1 → ∀ (ε : ℝ), 0 < ε → ∃ s ∈ Set.Ioo (-3) (-2), -osc - ε ≤ slope (lineRestriction u (coordinateLineBase i y) (sourceCoordinateVector i)) s (-2)) (hright : ∀ (i : Fin n) ⦃y : SourceCubeSpace n⦄, y ∈ sourceOpenCube n 1 → ∀ (ε : ℝ), 0 < ε → ∃ s ∈ Set.Ioo 2 3, slope (lineRestriction u (coordinateLineBase i y) (sourceCoordinateVector i)) 2 s ≤ osc + ε) (hmax : LocalizedMaximalEstimateStatement Cmax) (ht : 0 < t) (i : Fin n) :

            Fubini plus convexity and endpoint secant bounds for clamped source-local slices give the per-coordinate bad-set estimate for localized maximal slices.

            theorem AleksandrovDifferentiability.CoordinateSliceMaximalTotalBadSetEstimate.of_sourceRightDerivConvexSecantBounds {n : ℕ} {u : SourceCubeSpace n → ℝ} {hmono : ∀ (i : Fin n) ⦃y : SourceCubeSpace n⦄, y ∈ sourceOpenCube n 1 → MonotoneOn (rightDeriv (lineRestriction u (coordinateLineBase i y) (sourceCoordinateVector i))) (Set.Icc (-2) 2)} {Cmax Ccoord Cmeasure t : ℝ} (hdiff : MeasureTheory.volume (sourceOpenCube n 1 \ firstOrderDifferentiabilitySet u) = 0) (hconv : ∀ (i : Fin n) ⦃y : SourceCubeSpace n⦄, y ∈ sourceOpenCube n 1 → ConvexOn ℝ (Set.Ioo (-3) 3) (lineRestriction u (coordinateLineBase i y) (sourceCoordinateVector i))) (hleft : ∀ (i : Fin n) ⦃y : SourceCubeSpace n⦄, y ∈ sourceOpenCube n 1 → ∀ (ε : ℝ), 0 < ε → ∃ s ∈ Set.Ioo (-3) (-2), -sourceCubeOscillation n u - ε ≤ slope (lineRestriction u (coordinateLineBase i y) (sourceCoordinateVector i)) s (-2)) (hright : ∀ (i : Fin n) ⦃y : SourceCubeSpace n⦄, y ∈ sourceOpenCube n 1 → ∀ (ε : ℝ), 0 < ε → ∃ s ∈ Set.Ioo 2 3, slope (lineRestriction u (coordinateLineBase i y) (sourceCoordinateVector i)) 2 s ≤ sourceCubeOscillation n u + ε) (hmax : LocalizedMaximalEstimateStatement Cmax) (ht : 0 < t) (hfubini : CoordinateSliceMaximalFubiniEstimate (coordinateSliceSourceRightDerivStieltjesMeasureFamily u hmono) (2 * Cmax) Ccoord (sourceCubeOscillation n u) t) (hbound : ↑n * ENNReal.ofReal (Ccoord * sourceCubeOscillation n u / t) ≤ ENNReal.ofReal (Cmeasure * sourceCubeOscillation n u / t)) :

            Total bad-set estimate for clamped source-local Stieltjes slices, using convexity and endpoint secant bounds for the coordinate restrictions to supply the source mass estimate.

            Total bad-set estimate for the concrete convex source-local Stieltjes slice family. Ambient convexity supplies slice convexity and right-derivative monotonicity; the remaining assumptions are the source endpoint secant estimates and the Fubini/weak-type bookkeeping.

            Total bad-set estimate for the concrete convex source-local Stieltjes slice family from ambient convexity, boundedness, the localized maximal theorem, and the Fubini bookkeeping.

            Total bad-set estimate for the concrete convex source-local Stieltjes slice family from ambient convexity, boundedness, the localized maximal theorem, and the Fubini bookkeeping, with the finite-union constant supplied as a real coefficient inequality.