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Continuous Supremum from Dyadic Running Maxima

Proved
LevyStochCalc.Probability.iSup_enorm_eq_iSup_dyadicRunMax_readable

by Lazyfill · Oct 26, 2026 · Mathlib 0df444a (Lean v4.33.1)

functional-inequalitieskls-conjecturekls-modularkls-readablelog-concavevendored-levystochcalc

This vendored theorem identifies a continuous-time supremum with dyadic running maxima.

Let T≥0T\geq0T≥0, fix an outcome ω\omegaω, and suppose that the path s↦Ms(ω)s\mapsto M_s(\omega)s↦Ms​(ω) is right-continuous at every time. Then

sup⁡t∈[0,T]∥Mt(ω)∥ENNReal=sup⁡n∈N∥dyadicRunMax⁡(M,T,n,ω)∥ENNReal.\sup_{t\in[0,T]}\|M_t(\omega)\|_{\mathrm{ENNReal}} = \sup_{n\in\mathbb N} \|\operatorname{dyadicRunMax}(M,T,n,\omega)\|_{\mathrm{ENNReal}}.t∈[0,T]sup​∥Mt​(ω)∥ENNReal​=n∈Nsup​∥dyadicRunMax(M,T,n,ω)∥ENNReal​.

This is the pathwise bridge from discrete dyadic maximal estimates to a continuous-time supremum estimate.

Formalization Note. The supremum over [0,T][0,T][0,T] is represented by an iSup over the subtype Set.Icc 0 T.

Original Lean declaration header

Within namespace LevyStochCalc.Probability. Shown in its original module context. Proof omitted.

theorem iSup_enorm_eq_iSup_dyadicRunMax (hT : 0 ≤ T) {ω : Ω}
    (hcad : ∀ t : ℝ, Filter.Tendsto (fun s => M s ω) (nhdsWithin t (Set.Ioi t))
      (nhds (M t ω))) :
    (⨆ t : Set.Icc (0 : ℝ) T, (‖M (t : ℝ) ω‖₊ : ℝ≥0∞))
      = ⨆ n, (‖dyadicRunMax M T n ω‖₊ : ℝ≥0∞)

Module navigation

Module: LevyStochCalc/Probability/DoobContinuous.

Supporting theorems: enorm_dyadicRunMax, tendsto_dyadicTime_dyadicIndex.

Previous: tendsto_dyadicTime_dyadicIndex · Next: lintegral_sq_dyadicRunMax_le.

Source excerpt, lines 208–212 · Frozen source 408915f.

Preamble
import Definitions.Def_KLSModular_LevyStochCalc_Probability_DoobContinuous

/-
Copyright (c) 2026 Christian Garry. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Christian Garry
-/
-- Vendored from https://github.com/cgarryZA/LevyStochCalc at commit 984dbf0b790031eac05d56fe5a84b65e15e1e932 (upstream path LevyStochCalc/Probability/DoobContinuous.lean; Apache-2.0; provenance in formalization/vendor/vendor-lock.json of the KLS repository)
open scoped NNReal ENNReal
variable {Ω : Type*} {mΩ : MeasurableSpace Ω}
variable {M : ℝ → Ω → ℝ} {T : ℝ}
Formal statement

theorem LevyStochCalc.Probability.iSup_enorm_eq_iSup_dyadicRunMax_readable (hT : 0 ≤ T) {ω : Ω}
    (hcad : ∀ t : ℝ, Filter.Tendsto (fun s => M s ω) (nhdsWithin t (Set.Ioi t))
      (nhds (M t ω))) :
    (⨆ t : Set.Icc (0 : ℝ) T, (‖M (t : ℝ) ω‖₊ : ℝ≥0∞))
      = ⨆ n, (‖dyadicRunMax M T n ω‖₊ : ℝ≥0∞) := by sorry
Source
Lean source: https://github.com/cgarryZA/LevyStochCalc/blob/984dbf0b790031eac05d56fe5a84b65e15e1e932/LevyStochCalc/Probability/DoobContinuous.lean#L206-L241 (repository human-ai-mathematics/kls-lean, commit 408915f, module `LevyStochCalc.Probability.DoobContinuous`, declaration `LevyStochCalc.Probability.iSup_enorm_eq_iSup_dyadicRunMax`; the repository is private at upload time). Vendored from https://github.com/cgarryZA/LevyStochCalc at commit 984dbf0b790031eac05d56fe5a84b65e15e1e932 (Apache-2.0; upstream copyright notice retained in the Lean text).

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