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
ProvedDRConvexOpt.Spread.proposition_2Let , , and , and consider the instance of the ambiguity set (4) with auxiliary random variables :
Then
In particular every distribution whose absolute mean spread at is at most and with is a marginal of a member of . 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, ; both events have probability or , which are positive because , so no division by zero occurs. An outcome is , with the projections uOf, vOf, wOf; the marginal is the push-forward of under the first projection. Members of the right-hand set are required to have integrable, and members of to have integrable: these are the expectations the page writes. The page's "" is the immediate consequence of the set identity, which is what is stated.
import Mathlib import Definitions.Def_DRConvexOpt_Spread_Setting
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
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.