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Theorem 4.32 — integrating against a marginal

Proved
MongeKantorovichYao.integral_fst_eq_of_mem_transferencePlans

by Lucas · Sep 30, 2026 · Mathlib 0df444a (Lean v4.33.1)

measure-theoryoptimal-transport

Let X,YX,YX,Y be measurable spaces and let π\piπ be a probability measure on X×YX\times YX×Y with marginals μ\muμ and ν\nuν on XXX and YYY respectively. Then for every μ\muμ-integrable f:X→Rf : X\to\mathbb Rf:X→R,

∫Xf(x) dμ(x)=∫X×Yf(x) dπ(x,y).\int_X f(x)\,d\mu(x)=\int_{X\times Y}f(x)\,d\pi(x,y).∫X​f(x)dμ(x)=∫X×Y​f(x)dπ(x,y).

This identity converts the dual objective ∫ψ dμ+∫φ dν\int\psi\,d\mu+\int\varphi\,d\nu∫ψdμ+∫φdν into a single integral against a plan.

Formalization Note The function (x,y)↦f(x)(x,y)\mapsto f(x)(x,y)↦f(x) is the extension f~\tilde ff~​ mentioned in the source; the analogous statement for the second marginal is symmetric.

Preamble
import Mathlib
import Definitions.Def_MongeKantorovichYao_Defs

open MeasureTheory
Formal statement
namespace MongeKantorovichYao

theorem integral_fst_eq_of_mem_transferencePlans {X Y : Type*}
    [MeasurableSpace X] [MeasurableSpace Y]
    (μ : Measure X) (ν : Measure Y) (π : Measure (X × Y)) (hπ : π ∈ transferencePlans μ ν)
    (f : X → ℝ) (hf : Integrable f μ) :
    ∫ x, f x ∂μ = ∫ p, f p.1 ∂π := by sorry

end MongeKantorovichYao
Source
Colin Yao, *Monge–Kantorovich and Transportation Theory* (paper dated September 10, 2023), p. 16, Theorem 4.32
Read-back

What the Lean code literally says, in plain math · Aristotle by Harmonic (same agent as the drafter; non-blind)

Non-blind read-back — not independent testimony. This read-back was written by the same agent (Aristotle, by Harmonic) that drafted the Lean statement, with full knowledge of the source paper and of the intended meaning. It is not a blind audit and must not be mistaken for independent testimony; reviewers should compare the Lean code against the source themselves (or obtain an independent read-back).

Data and hypotheses. Types X,YX,YX,Y with σ-algebras (no topology); measures μ\muμ on XXX, ν\nuν on YYY, π\piπ on X×YX\times YX×Y with π∈Π(μ,ν)\pi\in\Pi(\mu,\nu)π∈Π(μ,ν) (probability measure, first marginal =μ=\mu=μ, second marginal =ν=\nu=ν); a real function fff on XXX that is Bochner-integrable with respect to μ\muμ.

Conclusion. ∫Xf dμ=∫X×Yf(p1) dπ(p)\int_X f\,d\mu=\int_{X\times Y}f(p_1)\,d\pi(p)∫X​fdμ=∫X×Y​f(p1​)dπ(p), where p1p_1p1​ is the first coordinate of ppp (both ordinary real-valued Bochner integrals). Only the first marginal is treated; ν\nuν plays no role beyond membership of π\piπ in Π(μ,ν)\Pi(\mu,\nu)Π(μ,ν).

Human review
  • Endorsed by Shuze Chen · Sep 30, 2026

    Confirmed by the moderator at approval.

  • Endorsed by Lucas · Sep 30, 2026

    Confirmed by the mission captain (proposal self-audit).

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