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LeanPool.Superdiffusion.Algsuperdiff.Section4.Provider.MinimalScale.ZAssembly

ZAssembly #

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

The witness Z = max{Z¹, Z²}, its measurability, and the conditional endpoint #

ABK26, §4.2. The witness is the random minimal scale of the disjunction of the two bad-window families, which is pointwise the maximum of the two legs (Algsuperdiff.Probability.minimalScale). Three things then come for free, for every omega and with no measurability side condition:

The conditional endpoint #

frozen_body_of_slots is a conditional endpoint. Its conditional inputs are exactly

Everything else in the proposition — the Z² leg end to end, the witness, the measurability, the max tail, and both clauses — is unconditional.

The s-exponent #

Every public here reads the regime clause at s⁹, i.e. the frozen e.scale.sep.cond with the single numeral 8 replaced by 9. See ZDensityLeg.lean's module docstring: the centre union bound of the Z² leg needs γ ≤ K(d)⁻¹ c⋆² s⁹ δ² and the printed clause supplies only s⁸, a gap of one power of the free parameter s. This is the ONLY deviation from the printed hypotheses; the tail rate, the tolerance s * Real.sqrt delta, the window, the centre set and both displays are byte-identical to the frozen statement.

References #

1. The witness #

The proposition's random scale Z_m(s,δ) = max{Z¹, Z²}, realised as the random minimal scale of the disjunction of the two bad-window families.

Equations
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Instances For
    theorem Algsuperdiff.Section4.Provider.MinimalScale.zWitness_eq_max {d : ℕ} (M : Section3.ABKModel d) (s delta : ℝ) (hs : 0 < s) (m : ℤ) (bad1 : ℕ → Section3.Cutoff.CutoffSample d → Prop) (omega : Section3.Cutoff.CutoffSample d) :
    zWitness M s delta hs m bad1 omega = max (Probability.minimalScaleEN bad1 omega) (Probability.minimalScaleEN (badDensity M s delta hs m) omega)

    The max assembly, as an identity. The witness is the pointwise maximum of the two legs.

    theorem Algsuperdiff.Section4.Provider.MinimalScale.measurable_zWitness {d : ℕ} (M : Section3.ABKModel d) (s delta : ℝ) (hs : 0 < s) (m : ℤ) (bad1 : ℕ → Section3.Cutoff.CutoffSample d → Prop) (hb1 : ∀ (j : ℕ), MeasurableSet {omega : Section3.Cutoff.CutoffSample d | bad1 j omega}) :
    Measurable (zWitness M s delta hs m bad1)

    Measurable Z (the survey's empty-sweep-1 clause). It reduces to per-window measurability of the two bad families; the density leg is proved in ZScores.lean.

    The tail event of the witness is the union of the two legs' tail events.

    2. Both deterministic clauses #

    theorem Algsuperdiff.Section4.Provider.MinimalScale.clauses_of_zWitness_le {d : ℕ} (M : Section3.ABKModel d) (s delta : ℝ) (hs : 0 < s) (m : ℤ) (bad1 : ℕ → Section3.Cutoff.CutoffSample d → Prop) (hZ1clause : ∀ (omega : Section3.Cutoff.CutoffSample d), ∀ n ≤ m, Probability.minimalScaleEN bad1 omega ≤ ↑(m - n).toNat → cesaroScore M s delta hs n m omega ≤ ENNReal.ofReal delta) (omega : Section3.Cutoff.CutoffSample d) (n : ℤ) (hnm : n ≤ m) (h : zWitness M s delta hs m bad1 omega ≤ ↑(m - n).toNat) :
    cesaroScore M s delta hs n m omega ≤ ENNReal.ofReal delta ∧ densityScore M s delta hs n m omega ≤ ENNReal.ofReal delta

    The two deterministic halves, together. If the witness is at most the window length (m − n).toNat, then both frozen displays hold at the window [n, m]. This holds for every omega: no almost-sure qualifier, no finiteness, no measurability. The density half is unconditional; the Cesàro half is exactly hZ1clause.

    3. The Z² tail in the additive shape #

    theorem Algsuperdiff.Section4.Provider.MinimalScale.measure_tail_badDensity_le_half {d : ℕ} (C : ℝ) (hC : 2 * zTwoConst d ≤ C) (M : Section3.ABKModel d) (s delta : ℝ) (hs : 0 < s) (hs4 : s ≤ 1 / 4) (hd0 : 0 < delta) (hd2 : delta ≤ 1 / 2) (hgam : M.gamma ≤ C⁻¹ ^ 10 * min (Section3.Disorder.cstar M ^ 10) (delta ^ 2 * Section3.Disorder.cstar M ^ 2 * s ^ 9)) (hwin : 8 * M.gamma ≤ s) (m : ℤ) (N : ℕ) :

    The Z² tail at half the prefactor and the un-halved rate — the shape that adds to a Z¹ tail of the same shape and produces the frozen display at the single constant C.

    Length-0 windows are free (measure ≤ 1 ≤ C/2), and for N ≥ 1 the rate num/((C/2)γ) delivered by measure_tail_badDensity_le at C/2 dominates the displayed num/(Cγ).

    4. The max tail at the frozen display #

    theorem Algsuperdiff.Section4.Provider.MinimalScale.measure_tail_zWitness_le {d : ℕ} (C : ℝ) (hC : 2 * zTwoConst d ≤ C) (M : Section3.ABKModel d) (s delta : ℝ) (hs : 0 < s) (hs4 : s ≤ 1 / 4) (hd0 : 0 < delta) (hd2 : delta ≤ 1 / 2) (hgam : M.gamma ≤ C⁻¹ ^ 10 * min (Section3.Disorder.cstar M ^ 10) (delta ^ 2 * Section3.Disorder.cstar M ^ 2 * s ^ 9)) (hwin : 8 * M.gamma ≤ s) (m : ℤ) (bad1 : ℕ → Section3.Cutoff.CutoffSample d → Prop) (hZ1tail : ∀ (N : ℕ), ↑(Section3.Cutoff.cutoffSampleLaw M) {omega : Section3.Cutoff.CutoffSample d | ↑N ≤ Probability.minimalScaleEN bad1 omega} ≤ ENNReal.ofReal (C / 2 * Real.exp (-((↑N - 1) * s ^ 9 * Section3.Disorder.cstar M ^ 2 * delta ^ 2) / (C * M.gamma)))) (N : ℕ) :
    ↑(Section3.Cutoff.cutoffSampleLaw M) {omega : Section3.Cutoff.CutoffSample d | ↑N ≤ zWitness M s delta hs m bad1 omega} ≤ ENNReal.ofReal (C * Real.exp (-((↑N - 1) * s ^ 9 * Section3.Disorder.cstar M ^ 2 * delta ^ 2) / (C * M.gamma)))

    e.scale.sep.bounds, at the frozen prefactor-tail shape. The tail of max{Z¹, Z²} is the sum of the two legs' tails (tail_set_zWitness plus subadditivity), so two tails at prefactor C/2 give one at prefactor C, with the rate untouched.

    5. The conditional endpoint #

    theorem Algsuperdiff.Section4.Provider.MinimalScale.frozen_body_of_slots {d : ℕ} (C : ℝ) (hC : 2 * zTwoConst d ≤ C) (M : Section3.ABKModel d) (s delta : ℝ) (hsr : s ∈ Set.Ioc 0 (1 / 4)) (hdr : delta ∈ Set.Ioc 0 (1 / 2)) (hgam : M.gamma ≤ C⁻¹ ^ 10 * min (Section3.Disorder.cstar M ^ 10) (delta ^ 2 * Section3.Disorder.cstar M ^ 2 * s ^ 9)) (hwin : 8 * M.gamma ≤ s) (m : ℤ) (hs : 0 < s) (bad1 : ℕ → Section3.Cutoff.CutoffSample d → Prop) (hZ1measurable : ∀ (j : ℕ), MeasurableSet {omega : Section3.Cutoff.CutoffSample d | bad1 j omega}) (hZ1tail : ∀ (N : ℕ), ↑(Section3.Cutoff.cutoffSampleLaw M) {omega : Section3.Cutoff.CutoffSample d | ↑N ≤ Probability.minimalScaleEN bad1 omega} ≤ ENNReal.ofReal (C / 2 * Real.exp (-((↑N - 1) * s ^ 9 * Section3.Disorder.cstar M ^ 2 * delta ^ 2) / (C * M.gamma)))) (hZ1clause : ∀ (omega : Section3.Cutoff.CutoffSample d), ∀ n ≤ m, Probability.minimalScaleEN bad1 omega ≤ ↑(m - n).toNat → cesaroScore M s delta hs n m omega ≤ ENNReal.ofReal delta) :
    ∃ (Z : Section3.Cutoff.CutoffSample d → ℕ∞), Measurable Z ∧ (∀ (N : ℕ), ↑(Section3.Cutoff.cutoffSampleLaw M) {omega : Section3.Cutoff.CutoffSample d | ↑N ≤ Z omega} ≤ ENNReal.ofReal (C * Real.exp (-((↑N - 1) * s ^ 9 * Section3.Disorder.cstar M ^ 2 * delta ^ 2) / (C * M.gamma)))) ∧ ∀ᵐ (omega : Section3.Cutoff.CutoffSample d) ∂↑(Section3.Cutoff.cutoffSampleLaw M), ∀ n ≤ m, Z omega ≤ ↑(m - n).toNat → cesaroScore M s delta hs n m omega ≤ ENNReal.ofReal delta ∧ densityScore M s delta hs n m omega ≤ ENNReal.ofReal delta

    The conditional endpoint (abstract Z¹ leg). The full body of the frozen proposition — the witness, its measurability, the prefactor tail and both a.e. displays — from the proposition's own parameter ranges and window, the regime clause at s⁹ (see the module docstring for the s⁸ → s⁹ deviation), and exactly three conditional inputs about the Z¹ leg: hZ1measurable, hZ1tail and hZ1clause.

    The Z² leg, the witness, Measurable Z, the max tail and the density display are all unconditional.

    theorem Algsuperdiff.Section4.Provider.MinimalScale.frozen_body_of_cesaro_tail {d : ℕ} (C : ℝ) (hC : 2 * zTwoConst d ≤ C) (M : Section3.ABKModel d) (s delta : ℝ) (hsr : s ∈ Set.Ioc 0 (1 / 4)) (hdr : delta ∈ Set.Ioc 0 (1 / 2)) (hgam : M.gamma ≤ C⁻¹ ^ 10 * min (Section3.Disorder.cstar M ^ 10) (delta ^ 2 * Section3.Disorder.cstar M ^ 2 * s ^ 9)) (hwin : 8 * M.gamma ≤ s) (m : ℤ) (hs : 0 < s) (hZ1tail : ∀ (N : ℕ), ↑(Section3.Cutoff.cutoffSampleLaw M) {omega : Section3.Cutoff.CutoffSample d | ↑N ≤ Probability.minimalScaleEN (badCesaro M s delta hs m) omega} ≤ ENNReal.ofReal (C / 2 * Real.exp (-((↑N - 1) * s ^ 9 * Section3.Disorder.cstar M ^ 2 * delta ^ 2) / (C * M.gamma)))) :
    ∃ (Z : Section3.Cutoff.CutoffSample d → ℕ∞), Measurable Z ∧ (∀ (N : ℕ), ↑(Section3.Cutoff.cutoffSampleLaw M) {omega : Section3.Cutoff.CutoffSample d | ↑N ≤ Z omega} ≤ ENNReal.ofReal (C * Real.exp (-((↑N - 1) * s ^ 9 * Section3.Disorder.cstar M ^ 2 * delta ^ 2) / (C * M.gamma)))) ∧ ∀ᵐ (omega : Section3.Cutoff.CutoffSample d) ∂↑(Section3.Cutoff.cutoffSampleLaw M), ∀ n ≤ m, Z omega ≤ ↑(m - n).toNat → cesaroScore M s delta hs n m omega ≤ ENNReal.ofReal delta ∧ densityScore M s delta hs n m omega ≤ ENNReal.ofReal delta

    The delivered constant is a single C with 2 · zTwoConst d ≤ C; the frozen ∃ C is obtained by taking C := 2 · max (zTwoConst d) (Wave-D's floor).