Documentation

LeanPool.Superdiffusion.Algsuperdiff.Section4.Provider.MinimalScale.StepTwoDTwo

StepTwoDTwo #

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

Step 2 of the kicking lemma: the D₂ half of e.kick.Dees.Dees #

ABK26, §4.2, l.minimal.scale.sep, Step 2. The manuscript's D₂ is

D₂(k) = c⋆^{−1/2} s^{−1} γ^{1/2} Σ_{l ≥ k} 3^{(2−γ)k} ‖∇j_l‖_{W^{1,∞}(□_k)}

and Step 2 proves the display e.D2k.kicked

avsum_{k=n}^m D₂(k) ≤ C c⋆^{−1/2}s^{−1}γ^{1/2}
                        + 𝒪_{Γ₂}( C c⋆^{−1/2}s^{−1}γ^{1/2}(m−n)^{−1/2} ) .

The three ingredients #

The rearrangement, and the two corrections it respects #

The manuscript rearranges Σ_{k=n}^m Σ_{l≥k} into Σ_{l≥n} Σ_{k=n}^{m∧l} and then majorizes the inner k-sum. Two corrections are built into the route:

The window arithmetic itself is the carrier-free Algsuperdiff.Probability.window_geom_tail_rearrange_le, applied at α = 1 − γ.

Why the endpoint is almost sure #

The manuscript's inner sum is an infinite series over the field index l; the anchor forms it in ℝ≥0∞. The real reading .toReal is dominated by the majorant series only where that series converges, and convergence holds almost surely — proved, not assumed, from the per-layer first moments (LayerBrackets.ae_wsumE_ne_top). The anchor's clause (i) is itself an almost sure statement, so nothing is lost.

Independence #

Step 2 applies p.concentration to the layer family {3^{(2−γ)l}‖∇j_l‖}, which is genuinely independent: each layer is a functional of its own shell and the single-shell sigma-fields are mutually independent (LayerBrackets.iIndepFun_gradLayer). The manuscript's own justification ("by the independence of the sequence {j_n}") is exactly this.

References #

1. The (1−γ) rate #

On the frozen window γ ≤ 1/4 the Step-2 rate is at least 3/4.

The geometric closure constant is antitone in the rate.

The absolute bound on the Step-2 geometric closure: it is at most the numeral geomTailConst (3/4), with no γ left in it.

2. The anchor-shaped inner sum of D₂(k) #

The field indices l ≥ k, enumerated by the offset l − k ∈ ℕ.

Equations
Instances For

    The inner sum of D₂(k), formed in ℝ≥0∞ exactly as the anchor annular_decomposition forms it: Σ_{l ≥ k} 3^{(2−γ)k}‖∇j_l‖_{W^{1,∞}(□_k)}.

    Equations
    • One or more equations did not get rendered due to their size.
    Instances For

      The real-valued inner sum; at the samples where the ℝ≥0∞ sum diverges this is 0, which only strengthens every upper-tail statement about it.

      Equations
      Instances For

        The inner sum as a SeriesTail weighted series: the weight is constant in the offset and the atom is the cross-scale gauge.

        3. The majorant layer series #

        The geometric gradient weight at one minimal-scale depth.

        Equations
        Instances For

          The Step-2 majorant series at scale k: the geometrically weighted sum of the layer brackets at their own scales, from k upwards. This is the right-hand side of the manuscript's rearranged display.

          Equations
          • One or more equations did not get rendered due to their size.
          Instances For

            The majorant series in the real tsum spelling consumed by the window arithmetic of Algsuperdiff.Probability.WindowRearrange.

            The pointwise majorization, in ℝ≥0∞, unconditional. This is the manuscript's cube comparison summed over the field indices.

            The real form of the majorization, at the samples where the majorant series converges.

            4. Almost sure convergence of the majorant series #

            The majorant series converges almost surely, at every scale. Proved from the per-layer first moments; the manuscript never states the convergence and never needs to, because it is automatic.

            5. The Step-2 window rearrangement #

            theorem Algsuperdiff.Section4.Provider.MinimalScale.cesaro_gradInner_le {d : ℕ} (M : Section3.ABKModel d) {n m : ℤ} (hnm : n ≤ m) {omega : Section3.Cutoff.CutoffSample d} (hfin : ∀ (k : ℤ), Proportion.wsumE (fun (p : ℕ) => Proportion.atomG1a M (k + ↑p)) (gradWeight M) omega ≠ ⊤) :
            ∑ k ∈ Finset.Icc n m, gradInner M k omega ≤ Probability.geomTailConst (1 - M.gamma) * (∑ l ∈ Finset.Icc n m, Proportion.atomG1a M l omega + gradSeries M (m + 1) omega)

            The Step-2 window rearrangement. At every sample point where the majorant series converges,

            Σ_{k=n}^m (inner sum of D₂(k)) ≤ Cgeo ( Σ_{l=n}^m Y_l + Σ_{p≥0} 3^{−(1−γ)p} Y_{m+1+p} )
            

            with Cgeo = geomTailConst (1−γ) = (1 − 3^{−(1−γ)})⁻¹ and Y_l the layer bracket at its own scale.

            6. The Γ₂ tail of the off-window series #

            The off-window layer series has a Γ₂ tail at a constant amplitude: Σ_{l≥m} 3^{(2−γ)(m−l)}Y_l ≤ 𝒪_{Γ₂}(C). This is the e.Gamma.sigma.triangle step of Step 2, at the proved geometric-weight closure Algsuperdiff.Probability.weightedTsum_isBigOWith (L4); no pointwise summability of the layer series is assumed.

            7. The Step-2 conclusion #

            The fluctuation constant of Step 2, in units of the D₂ prefactor. Both constants are absolute numerals: no γ, no s, no dimension.

            Equations
            • One or more equations did not get rendered due to their size.
            Instances For

              For a window n < m, the Cesàro average of the inner sums of D₂ splits into a deterministic level and a Γ₂ fluctuation of scale stepTwoConst · (m−n)^{−1/2}.

              theorem Algsuperdiff.Section4.Provider.MinimalScale.exists_D2k_kicked {d : ℕ} (M : Section3.ABKModel d) {n m : ℤ} (hnm : n < m) {pref : ℝ} (hpref : 0 ≤ pref) :

              e.D2k.kicked, at an arbitrary nonnegative prefactor. At pref = c⋆^{−1/2}s^{−1}γ^{1/2} — the prefactor the annular decomposition puts in front of the inner sum — this is the printed display

              avsum_{k=n}^m D₂(k) ≤ C c⋆^{−1/2}s^{−1}γ^{1/2}
                                      + 𝒪_{Γ₂}( C c⋆^{−1/2}s^{−1}γ^{1/2}(m−n)^{−1/2} )
              

              with C = max(stepTwoDetConst, stepTwoConst), both absolute numerals. The prefactor is kept abstract so that the final stitch may supply it in whatever spelling the D-decomposition delivers.