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Additivity of the Brownian Itô Integral

Proved
LevyStochCalc.Brownian.Ito.stochasticIntegralBrownian_add_readable

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

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

This statement establishes additivity of the L² Brownian Itô integral in its integrand. Let (Ω,P)(Ω,P)(Ω,P) be a probability space, let WWW be a Brownian motion under PPP, and let FℱF be a filtration for which WWW is Brownian. Let H1,H2:Ω×R→RH₁,H₂:Ω×ℝ→ℝH1​,H2​:Ω×R→R be jointly measurable and progressively measurable. Assume that, for every T>0T>0T>0, the squared-energy integrals of H1H₁H1​, H2H₂H2​, and H1+H2H₁+H₂H1​+H2​ are finite in [0,∞][0,∞][0,∞].

For every T>0T>0T>0, the integral of the pointwise sum agrees PPP-almost everywhere with the sum of the two integrals:

stochasticIntegralBrownian(H1+H2,T)=stochasticIntegralBrownian(H1,T)+stochasticIntegralBrownian(H2,T).\texttt{stochasticIntegralBrownian}(H₁+H₂,T)=\texttt{stochasticIntegralBrownian}(H₁,T)+\texttt{stochasticIntegralBrownian}(H₂,T).stochasticIntegralBrownian(H1​+H2​,T)=stochasticIntegralBrownian(H1​,T)+stochasticIntegralBrownian(H2​,T).

Here each displayed energy is an iterated ENNReal integral of the squared nnnorm, and all measurability, progressive-measurability, and finiteness hypotheses are explicit. The result is the basic linearity lemma used to obtain finite-sum and scalar-multiplication identities.

Original Lean declaration header

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

theorem stochasticIntegralBrownian_add {H₁ H₂ : Ω → ℝ → ℝ}
    (hm₁ : Measurable (Function.uncurry H₁)) (hm₂ : Measurable (Function.uncurry H₂))
    (hp₁ : Probability.ProgressivelyMeasurable ℱ H₁)
    (hp₂ : Probability.ProgressivelyMeasurable ℱ H₂)
    (hq₁ : ∀ T, 0 < T → ∫⁻ ω, ∫⁻ s in Set.Icc (0 : ℝ) T,
      (‖H₁ ω s‖₊ : ℝ≥0∞) ^ 2 ∂volume ∂P < ⊤)
    (hq₂ : ∀ T, 0 < T → ∫⁻ ω, ∫⁻ s in Set.Icc (0 : ℝ) T,
      (‖H₂ ω s‖₊ : ℝ≥0∞) ^ 2 ∂volume ∂P < ⊤)
    (hma : Measurable (Function.uncurry fun ω s => H₁ ω s + H₂ ω s))
    (hpa : Probability.ProgressivelyMeasurable ℱ fun ω s => H₁ ω s + H₂ ω s)
    (hqa : ∀ T, 0 < T → ∫⁻ ω, ∫⁻ s in Set.Icc (0 : ℝ) T,
      (‖H₁ ω s + H₂ ω s‖₊ : ℝ≥0∞) ^ 2 ∂volume ∂P < ⊤)
    {T : ℝ} (hT : 0 < T) :
    stochasticIntegralBrownian W ℱ hℱ (fun ω s => H₁ ω s + H₂ ω s) hma hpa hqa T
      =ᵐ[P] fun ω => stochasticIntegralBrownian W ℱ hℱ H₁ hm₁ hp₁ hq₁ T ω
        + stochasticIntegralBrownian W ℱ hℱ H₂ hm₂ hp₂ hq₂ T ω

Module navigation

Module: LevyStochCalc/Brownian/ItoLinear.

Definition modules: LevyStochCalc/Brownian/ItoL2CompletionMaster, LevyStochCalc/Brownian/Filtered, LevyStochCalc/Brownian/Construction, LevyStochCalc/Probability/Progressive.

Supporting theorems: ae_eq_add_of_sq_distances, lintegral_energy_lt_top_of_bound, lintegral_energy_parallelogram.

Previous: ae_eq_add_of_sq_distances · Next: ae_eq_const_mul_of_sq_distances.

Source excerpt, lines 257–272 · Frozen source 408915f.

Preamble
import Definitions.Def_KLSModular_LevyStochCalc_Brownian_ItoLinear

/-
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/Brownian/ItoLinear.lean; Apache-2.0; provenance in formalization/vendor/vendor-lock.json of the KLS repository)
open MeasureTheory ProbabilityTheory
open scoped NNReal ENNReal
universe u
variable {Ω : Type u} [MeasurableSpace Ω] {P : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure P]
variable (W : LevyStochCalc.Brownian.BrownianMotion P)
  (ℱ : MeasureTheory.Filtration ℝ ‹MeasurableSpace Ω›) (hℱ : LevyStochCalc.Brownian.IsBrownianFiltration W ℱ)
include hℱ
Formal statement

theorem LevyStochCalc.Brownian.Ito.stochasticIntegralBrownian_add_readable {H₁ H₂ : Ω → ℝ → ℝ}
    (hm₁ : Measurable (Function.uncurry H₁)) (hm₂ : Measurable (Function.uncurry H₂))
    (hp₁ : Probability.ProgressivelyMeasurable ℱ H₁)
    (hp₂ : Probability.ProgressivelyMeasurable ℱ H₂)
    (hq₁ : ∀ T, 0 < T → ∫⁻ ω, ∫⁻ s in Set.Icc (0 : ℝ) T,
      (‖H₁ ω s‖₊ : ℝ≥0∞) ^ 2 ∂volume ∂P < ⊤)
    (hq₂ : ∀ T, 0 < T → ∫⁻ ω, ∫⁻ s in Set.Icc (0 : ℝ) T,
      (‖H₂ ω s‖₊ : ℝ≥0∞) ^ 2 ∂volume ∂P < ⊤)
    (hma : Measurable (Function.uncurry fun ω s => H₁ ω s + H₂ ω s))
    (hpa : Probability.ProgressivelyMeasurable ℱ fun ω s => H₁ ω s + H₂ ω s)
    (hqa : ∀ T, 0 < T → ∫⁻ ω, ∫⁻ s in Set.Icc (0 : ℝ) T,
      (‖H₁ ω s + H₂ ω s‖₊ : ℝ≥0∞) ^ 2 ∂volume ∂P < ⊤)
    {T : ℝ} (hT : 0 < T) :
    stochasticIntegralBrownian W ℱ hℱ (fun ω s => H₁ ω s + H₂ ω s) hma hpa hqa T
      =ᵐ[P] fun ω => stochasticIntegralBrownian W ℱ hℱ H₁ hm₁ hp₁ hq₁ T ω
        + stochasticIntegralBrownian W ℱ hℱ H₂ hm₂ hp₂ hq₂ T ω := by sorry
Source
Lean source: https://github.com/cgarryZA/LevyStochCalc/blob/984dbf0b790031eac05d56fe5a84b65e15e1e932/LevyStochCalc/Brownian/ItoLinear.lean#L255-L292 (repository human-ai-mathematics/kls-lean, commit 408915f, module `LevyStochCalc.Brownian.ItoLinear`, declaration `LevyStochCalc.Brownian.Ito.stochasticIntegralBrownian_add`; 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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