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LeanPool.Superdiffusion.Algsuperdiff.Section3.Provider.Diffusivity.AsymptoticsAssembly

AsymptoticsAssembly #

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

The diffusivity asymptotics, assembled from the two printed recurrence displays #

This module is a conditional Provider A. It wires the already-proved deterministic flow spine

DisplayBridge -> SubLandmarkDisplays -> FlowGates -> EndpointSandwich

into the exact conclusion carried by Algsuperdiff.Frozen.Section3.diffusivity_asymptotics, taking the two printed displays e.what.do.we.have of l.approximate.recurrence.formula as the single disclosed pair of hypotheses hforward/hreverse.

What is NOT supplied here #

hforward and hreverse are the printed conclusion of the source node l.approximate.recurrence.formula, two-sided closure (delivering e.what.do.we.have). Their whole analytic content -- fresh-shell Dirichlet and Neumann correctors, the Calderon--Zygmund L^8 bounds, the mesoscopic oscillation estimate, the localized variational split, the good-event coefficient switch and the two one-sided Jensen legs -- is untouched.

The display constant is a parameter, and it must be #

This module takes the second option, and that choice is its central design decision. The flow constant that the assembly can offer necessarily grows with that C: the calibration hypothesis of FlowGates is quadratic in the substituted shell constant, so no single Cflow serves every C. The display constant is therefore carried as the leading parameter Crec of this theorem, ahead of the existential, and the disclosed binders are stated at the manuscript's own reading

Erec = Crec * E^2 * |log gamma|^2 ,      Frec = Crec .

This is the only structural addition to the frozen premise list; every premise of the frozen statement is carried verbatim, and no premise of it is weakened, strengthened, reordered or dropped. The parameter does not survive into the frozen conclusion: a consumer holding the two displays at the manuscript's own constant C(d) instantiates Crec := C(d) (or max (C(d)) 1) and thereby recovers the frozen ∃ Cflow statement exactly, so Crec is discharged at the point of consumption.

The flow constant is derived here, at the current integration constant #

FlowGates assumes its calibration

256 * recurrenceIntegrationConstant ^ 2 * (Cshell * (1 + Cshell + cstarPlus))
  <= Cflow ^ 2

rather than proving it. The witness is

Cflow = 16 * recurrenceIntegrationConstant *
          (1 + Cshell * (1 + Cshell + cstarPlusUpperBound d)) ,
Cshell = 974 * Crec ,

a function of the dimension d and of the display constant Crec only. The model-dependent cstarPlus M is replaced, before M is quantified, by the dimension-only envelope Provider.Disorder.cstarPlus_le_cstarPlusUpperBound, and the calibration is monotone in that slot, so the calibration is available at the genuine Disorder.cstarPlus M for every standing model.

Where the shell factor 974 comes from #

DisplayBridge.sigmaBar_le_thirtySix_mul_sigmaBar produces K = 36, but only from an induction state valid at the upper endpoint m, and the frozen premise supplies S(m_0 - 1, E) only. The resulting absolute constant is 974 = 1 + 972 + 1, where 972 is 324 * cstar * log 3 under cstar <= 3/2 and log 3 <= 2, and the last 1 is the display error Crec E^2 |log gamma|^2 gamma <= 1 in the regime.

The m_star_star floor and the shell budget #

The disclosed binders quantify over mStarStar M <= n only -- exactly the range delivers. The scale window bound is the consumer's narrow (m: real) <= (n: real) + cstar gamma^{-1}, not the source's wider h <= 6 cstar gamma^{-1}; the narrower window is the weaker hypothesis here, and it is what the integration engine demands.

The regime premises #

The frozen premise M.gamma <= ((E : real)^{-1})^10 is stronger than the printed cgamma <= E^{-5}; the weaker forms the spine needs are derived from it where required, never assumed separately. The landmark premise on m0 is carried and is not consumed by this route.

The scale gate on m0 (-bin statement change) #

That landmark premise is mStarStar M < m0, not the printed m0 in (mstar, infty) cap Z.

References #

Axiom provenance #

#print axioms Algsuperdiff.Section3.Provider.Diffusivity.diffusivity_asymptotics_of_recurrence_displays reports [propext, Classical.choice, Quot.sound] and nothing else -- the three kernel axioms only, with no incomplete-proof axiom in the list.

The derived flow constant #

The flow constant offered by the assembly, at dimension d and display constant Crec. It is computed fresh against the current recurrenceIntegrationConstant (see the module header, finding), and it depends on no model datum: the model-dependent cstarPlus M is replaced by the dimension-only Provider.Disorder.cstarPlusUpperBound d.

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Instances For

    Scalar arithmetic for the quotient bound #

    The two lemmas of this section are pure real algebra: no rpow, exp or log occurs in their statements, so the nonlinear arithmetic used to prove them never meets a transcendental atom.

    The geometric ratio on the admissible window #

    The quotient bound from the forward display #

    The assembled statement #

    theorem Algsuperdiff.Section3.Provider.Diffusivity.diffusivity_asymptotics_of_recurrence_displays (d : ℕ) (Crec : ℝ) (hCrec : 1 ≤ Crec) :
    ∃ (Cflow : ℝ), 0 < Cflow ∧ ∀ (M : ABKModel d) (m0 : ℤ) (E : { E : ℝ // 1 ≤ E }), mStarStar M < m0 → Frozen.Section3.inductionState M (m0 - 1) E → Cflow * (Disorder.cstar M)⁻¹ ≤ ↑E → M.gamma ≤ (↑E)⁻¹ ^ 10 → (∀ (n m : ℤ), mStarStar M ≤ n → m ≤ m0 → n ≤ m → ↑m ≤ ↑n + Disorder.cstar M * M.gamma⁻¹ → ↑(Annealed.sigmaBar M m) * (↑(Annealed.sigmaBar M n))⁻¹ ≤ 1 + RecurrenceIntegration.recurrenceIncrement (Disorder.cstar M) M.gamma n m * (↑(Annealed.sigmaBar M n))⁻¹ ^ 2 + Crec * ↑E ^ 2 * |Real.log M.gamma| ^ 2 * M.gamma) → (∀ (n m : ℤ), mStarStar M ≤ n → m ≤ m0 → n ≤ m → ↑m ≤ ↑n + Disorder.cstar M * M.gamma⁻¹ → ↑(Annealed.sigmaBar M n) * (↑(Annealed.sigmaBar M m))⁻¹ ≤ 1 - RecurrenceIntegration.recurrenceIncrement (Disorder.cstar M) M.gamma n m * (↑(Annealed.sigmaBar M n))⁻¹ ^ 2 + Crec * (↑m - ↑n) ^ 2 * (↑(Annealed.sigmaBar M n))⁻¹ ^ 4 * 3 ^ (4 * M.gamma * ↑m) + Crec * ↑E ^ 2 * |Real.log M.gamma| ^ 2 * M.gamma) → (∀ m ≤ m0, |↑(Annealed.sigmaBar M m) - √(M.nu ^ 2 + Disorder.cstar M * M.gamma⁻¹ * 3 ^ (2 * M.gamma * ↑m))| ≤ Cflow * (Disorder.cstar M)⁻¹ * ↑E * √M.gamma * |Real.log M.gamma| * ↑(Annealed.sigmaBar M m)) ∧ 1 / 4 * max (Disorder.cstar M * M.gamma⁻¹ * 3 ^ (2 * M.gamma * ↑m0)) (M.nu ^ 2) ≤ ↑(Annealed.sigmaBar M m0) ^ 2 ∧ ↑(Annealed.sigmaBar M m0) ^ 2 ≤ 4 * max (Disorder.cstar M * M.gamma⁻¹ * 3 ^ (2 * M.gamma * ↑m0)) (M.nu ^ 2)

    The diffusivity asymptotics, from the two printed recurrence displays.

    hforward and hreverse are disclosed hypotheses, not results. They are the printed conclusion of l.approximate.recurrence.formula, which this repository does not prove; see the module header for what is and is not supplied here, for the reason the display constant must be a parameter, and for the freshly derived calibration behind Cflow.