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Proposition 2 (Absolute Mean Spread), pp. 17–18 — Π_z̃𝒫 is exactly the set of Q with Q[fᵀz̃ ≥ θ] = ρ and absolute mean spread ≤ σ

Proved
DRConvexOpt.Spread.proposition_2

by mikedeng1 · Oct 9, 2026 · Mathlib 0df444a (Lean v4.33.1)

absolute-mean-spreadambiguity-setconditional-expectationdistributionally-robust-optimizationp2o-batch-pfp2ap2o-gran-per-chapterp2o-plan-paperp2o-v1

Let f∈RPf\in\mathbb R^Pf∈RP, θ∈R\theta\in\mathbb Rθ∈R, σ≥0\sigma\ge0σ≥0 and ρ∈(0,1)\rho\in(0,1)ρ∈(0,1), and consider the instance of the ambiguity set (4) with auxiliary random variables u~,v~,w~∈R\tilde u,\tilde v,\tilde w\in\mathbb Ru~,v~,w~∈R:

P={P∈P0(RP×R3): EP[w~]=σ, P[f⊤z~=θ+u~−v~, w~≥ρ−1u~+(1−ρ)−1v~, u~,v~≥0]=1, P[f⊤z~≥θ]=ρ}.\mathcal P=\Big\{\mathbb P\in\mathcal P_0(\mathbb R^P\times\mathbb R^3):\ \mathbb E_{\mathbb P}[\tilde w]=\sigma,\ \mathbb P\big[f^\top\tilde z=\theta+\tilde u-\tilde v,\ \tilde w\ge\rho^{-1}\tilde u+(1-\rho)^{-1}\tilde v,\ \tilde u,\tilde v\ge0\big]=1,\ \mathbb P[f^\top\tilde z\ge\theta]=\rho\Big\}.P={P∈P0​(RP×R3): EP​[w~]=σ, P[f⊤z~=θ+u~−v~, w~≥ρ−1u~+(1−ρ)−1v~, u~,v~≥0]=1, P[f⊤z~≥θ]=ρ}.

Then

Πz~P={Q∈P0(RP): EQ[f⊤z~∣f⊤z~≥θ]−EQ[f⊤z~∣f⊤z~<θ]≤σ, Q[f⊤z~≥θ]=ρ}.\Pi_{\tilde z}\mathcal P=\Big\{\mathbb Q\in\mathcal P_0(\mathbb R^P):\ \mathbb E_{\mathbb Q}[f^\top\tilde z\mid f^\top\tilde z\ge\theta]-\mathbb E_{\mathbb Q}[f^\top\tilde z\mid f^\top\tilde z<\theta]\le\sigma,\ \mathbb Q[f^\top\tilde z\ge\theta]=\rho\Big\}.Πz~​P={Q∈P0​(RP): EQ​[f⊤z~∣f⊤z~≥θ]−EQ​[f⊤z~∣f⊤z~<θ]≤σ, Q[f⊤z~≥θ]=ρ}.

In particular every distribution Q0\mathbb Q^0Q0 whose absolute mean spread at θ\thetaθ is at most σ\sigmaσ and with Q0[f⊤z~≥θ]=ρ\mathbb Q^0[f^\top\tilde z\ge\theta]=\rhoQ0[f⊤z~≥θ]=ρ is a marginal of a member of P\mathcal PP. Hence an ambiguity set defined by absolute mean spread information is the projection of an instance of the paper's standardized ambiguity set, and worst-case expectation constraints over it fall within the paper's framework.

Formalization Note The conditional means are elementary conditional expectations, EQ[f⊤z~∣A]=EQ[f⊤z~ 1A]/Q[A]\mathbb E_{\mathbb Q}[f^\top\tilde z\mid A]=\mathbb E_{\mathbb Q}[f^\top\tilde z\,\mathbf 1_A]/\mathbb Q[A]EQ​[f⊤z~∣A]=EQ​[f⊤z~1A​]/Q[A]; both events have probability ρ\rhoρ or 1−ρ1-\rho1−ρ, which are positive because ρ∈(0,1)\rho\in(0,1)ρ∈(0,1), so no division by zero occurs. An outcome is ω=(z,(u,v,w))∈RP×(R×R×R)\omega=(z,(u,v,w))\in\mathbb R^P\times(\mathbb R\times\mathbb R\times\mathbb R)ω=(z,(u,v,w))∈RP×(R×R×R), with u~,v~,w~\tilde u,\tilde v,\tilde wu~,v~,w~ the projections uOf, vOf, wOf; the marginal Πz~P\Pi_{\tilde z}\mathbb PΠz~​P is the push-forward of P\mathbb PP under the first projection. Members of the right-hand set are required to have f⊤z~f^\top\tilde zf⊤z~ integrable, and members of P\mathcal PP to have w~\tilde ww~ integrable: these are the expectations the page writes. The page's "Q0∈Πz~P\mathbb Q^0\in\Pi_{\tilde z}\mathcal PQ0∈Πz~​P" is the immediate consequence of the set identity, which is what is stated.

Preamble
import Mathlib
import Definitions.Def_DRConvexOpt_Spread_Setting
Formal statement
namespace DRConvexOpt.Spread

open MeasureTheory

/-- Proposition 2 (Absolute Mean Spread), pp. 17–18: for `f ∈ ℝ^P`, `θ ∈ ℝ`, `σ ≥ 0` and
`ρ ∈ (0, 1)`, the `z̃`-marginals of the lifted set `𝒫` are exactly the distributions `Q` with
`Q[fᵀz̃ ≥ θ] = ρ` and absolute mean spread at most `σ`. -/
theorem proposition_2 {nP : ℕ} (f : Fin nP → ℝ) (θ σ ρ : ℝ) (hσ : 0 ≤ σ) (hρ0 : 0 < ρ)
    (hρ1 : ρ < 1) :
    spreadSet f θ σ ρ = (fun μ => μ.map Prod.fst) '' liftedSpreadSet f θ σ ρ := by sorry

end DRConvexOpt.Spread
Source
Wiesemann, Kuhn & Sim, Distributionally Robust Convex Optimization, Optimization Online preprint 3757 (version of September 22, 2013), pp. 17–18, Proposition 2 (Absolute Mean Spread); proof pp. 40–41
Human review
  • Endorsed by Shuze Chen · Oct 9, 2026

    Confirmed by the moderator at approval.

  • Endorsed by mikedeng1 · Oct 9, 2026

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

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