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A finite-digit loss bound from summable inverse-branch endpoint errors

Proved
BianchiIX.finite_gauss_branch_loss

by trppppp · Nov 6, 2026 · Mathlib 0df444a (Lean v4.33.1)

bianchi-ix-era-statisticsdynamical-systems

Let N∈NN\in\mathbb NN∈N, 0≤β≤1/20\leq\beta\leq1/20≤β≤1/2, E≥0E\geq0E≥0, and real endpoint sequences Lm,RmL_m,R_mLm​,Rm​ satisfy, for m<Nm<Nm<N,

1m+2<Rm<Lm<1m+1,\frac1{m+2}<R_m<L_m<\frac1{m+1},m+21​<Rm​<Lm​<m+11​, ∣Lm−1/(m+1+2β)∣≤E/(m+1)2,|L_m-1/(m+1+2\beta)|\leq E/(m+1)^2,∣Lm​−1/(m+1+2β)∣≤E/(m+1)2, ∣Rm−1/(m+2−2β)∣≤E/(m+1)2.|R_m-1/(m+2-2\beta)|\leq E/(m+1)^2.∣Rm​−1/(m+2−2β)∣≤E/(m+1)2.

Then the open intervals (Rm,Lm)(R_m,L_m)(Rm​,Lm​) are pairwise disjoint and their complement in (0,1)(0,1)(0,1) is measurable, with

λ((0,1)∖⋃m<N(Rm,Lm))≤1N+1+8β+4E.\lambda\left((0,1)\setminus\bigcup_{m<N}(R_m,L_m)\right) \leq\frac1{N+1}+8\beta+4E.λ((0,1)∖m<N⋃​(Rm​,Lm​))≤N+11​+8β+4E.
Preamble
import Definitions.Def_BianchiIX_Statistics_GaussInverse
import Definitions.Def_BianchiIX_EraReturnData
import Mathlib.Analysis.Calculus.Deriv.Inv
import Mathlib.Analysis.Calculus.Deriv.Add
import Mathlib.Analysis.SpecificLimits.Basic
import Mathlib.Data.List.Induction
import Mathlib.Tactic.FieldSimp
import Mathlib.Tactic.Linarith
import Mathlib.Tactic.Ring
import Mathlib.Algebra.BigOperators.Ring.Finset
import Mathlib.Tactic.Positivity
import Mathlib.MeasureTheory.Measure.Lebesgue.Basic

noncomputable section
open BianchiIX
open Set MeasureTheory
open scoped ENNReal

Formal statement
theorem BianchiIX.finite_gauss_branch_loss {N : ℕ} {β E : ℝ} {L R : ℕ → ℝ}
    (hβ : 0 ≤ β) (hβmax : β ≤ 1 / 2) (hE : 0 ≤ E)
    (horder : ∀ m ∈ Finset.range N, R m < L m)
    (hlower : ∀ m ∈ Finset.range N, 1 / ((m : ℝ) + 2) < R m)
    (hupper : ∀ m ∈ Finset.range N, L m < 1 / ((m : ℝ) + 1))
    (hL : ∀ m ∈ Finset.range N,
      |L m - gaussInv ((m : ℝ) + 1) (2 * β)| ≤ E / ((m : ℝ) + 1) ^ 2)
    (hR : ∀ m ∈ Finset.range N,
      |R m - gaussInv ((m : ℝ) + 1) (1 - 2 * β)| ≤ E / ((m : ℝ) + 1) ^ 2) :
    (↑(Finset.range N) : Set ℕ).PairwiseDisjoint (fun m => Ioo (R m) (L m)) ∧
    MeasurableSet (Ioo (0 : ℝ) 1 \ retainedBranchPhases N L R) ∧
    volume (Ioo (0 : ℝ) 1 \ retainedBranchPhases N L R) ≤
      ENNReal.ofReal (1 / ((N : ℝ) + 1) + 8 * β + 4 * E) := by sorry
Source
https://github.com/l46430640-del/ERA-STATISTICS-FORMAL/blob/d74c241819f896ab8d050b21ace80c8e90eabd9e/BianchiIX/Measure/GaussBranchLoss.lean#L14-L83

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