Paper IV · Lean source

NormalizedZeroBell.lean

Selected results recorded Coverage and evidence

Compiler/replay evidence is reported for a subset or bounded block. No whole-work coverage is implied.

This viewer reads the retained source; it does not run Lean or assign new claim mappings.

Local imports (2)

KomorebiPaperIV/NormalizedZeroBell.lean62 lines

Original source pathPaper_IV_Verification_Closeout_v1.1.0/formalization/lean/KomorebiPaperIV/NormalizedZeroBell.lean
Skip declarations and read code

Skip source text

1 /-
2 Copyright (c) 2026 Komorebi P. All rights reserved.
3 Authors: Komorebi P.
4 -/
5 import KomorebiPaperIV.NormalizedProduct
6 import KomorebiPaperIV.AnalyticTaylorDifferential
7 
8 namespace Komorebi.PaperIV
9 noncomputable section
10 
11 theorem normalized_zeroMomentSeries {N : ℕ} (hN : 2 ≤ N) (j : ℕ) :
12     zeroMomentSeries (entireNormalizedSection N j) =
13       PowerSeries.derivative ℂ (normalizedResidueNegativeLog N j) := by
14   have hN0 : 0 < N := by omega
15   have hs := entireNormalizedSection_zeroPowerSummable hN0 j
16     ((div_lt_one (by positivity : (0 : ℝ) < N)).mpr (by exact_mod_cast (show 1 < N by omega)))
17   have h := hadamard_taylor_zeroMoment_identity
18     (fun z => (analyticAt_entireNormalizedSection hN0 j z).differentiableAt) hs
19     (by rw [entireNormalizedSection_zero]; exact one_ne_zero)
20     (entireNormalizedSection_hadamard hN j)
21   rw [analyticTaylorSeries_entireNormalizedSection hN0,
22     ← normalizedResidueSectionAlg_eq_normalizedResidueSection] at h
23   have hunit : IsUnit (normalizedResidueSectionAlg (R := ℂ) N j) :=
24     PowerSeries.isUnit_iff_constantCoeff.mpr (by simp)
25   apply hunit.mul_left_cancel
26   rw [h, normalizedResidueNegativeLog, map_neg, mul_neg,
27     mul_derivative_logOf (constantCoeff_normalizedResidueSectionAlg N j)]
28 
29 theorem normalized_zeroPowerSum {N : ℕ} (hN : 2 ≤ N) (j n : ℕ) :
30     zeroPowerSum (entireNormalizedSection N j) (n + 1) =
31       PowerSeries.coeff (n + 1) (normalizedResidueNegativeLog (R := ℂ) N j) * (n + 1 : ℂ) := by
32   have h := congrArg (PowerSeries.coeff n) (normalized_zeroMomentSeries hN j)
33   simpa [zeroMomentSeries, PowerSeries.coeff_derivative] using h
34 
35 theorem bellInput_normalized_zeros {N : ℕ} (hN : 2 ≤ N) (j : ℕ) (r : ℂ) :
36     bellInput (fun m => r * ((m - 1).factorial : ℂ) * zeroPowerSum (entireNormalizedSection N j) m) =
37       PowerSeries.C r * normalizedResidueNegativeLog N j := by
38   ext m
39   cases m with
40   | zero => simp [bellInput, PowerSeries.coeff_zero_eq_constantCoeff]
41   | succ m =>
42     simp only [bellInput, PowerSeries.coeff_mk, Nat.succ_ne_zero, ↓reduceIte,
43       Nat.succ_sub_one, normalized_zeroPowerSum hN, PowerSeries.coeff_C_mul]
44     rw [Nat.factorial_succ, Nat.cast_mul]
45     have hm : (m.factorial : ℂ) ≠ 0 := by exact_mod_cast m.factorial_ne_zero
46     have hs : (m.succ : ℂ) ≠ 0 := by exact_mod_cast Nat.succ_ne_zero m
47     field_simp
48     simp
49 
50 /-- The exact coefficient normalization in Proposition 12.4, in terms of
51 the actual nonzero zeros with their analytic multiplicities. -/
52 theorem residuePowerCoeff_zero_sum_bell {N : ℕ} (hN : 2 ≤ N) (j n : ℕ) (r : ℂ) :
53     residuePowerCoeff N j n r = ((N * n).factorial : ℂ) / (n.factorial : ℂ) *
54       completeExponentialBell n (fun m => r * ((m - 1).factorial : ℂ) *
55         zeroPowerSum (entireNormalizedSection N j) m) := by
56   rw [completeExponentialBell, bellInput_normalized_zeros hN, residuePowerCoeff, blockCoeff,
57     residuePowerSeries, formalExpFamily_eq_exp_subst (constantCoeff_normalizedResidueNegativeLog N j)]
58   have hn : (n.factorial : ℂ) ≠ 0 := by exact_mod_cast n.factorial_ne_zero
59   field_simp
60 
61 end
62 end Komorebi.PaperIV

Back to source controls

Original file and source record

This copy matches the recorded source file. Its publication here does not change the work’s review status.

SHA-256: 2341692ed732d378c439c882c83dce697ea1d9fab660227b4a3cf7a2daab1ddf

Original source on GitHub

Back to Paper IV: Cyclic Analytic Functions, Reciprocal Families, and Spectral Zero Data