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p. 12 β€” the ambiguity set 𝒫 is contained in every outer approximation 𝒫^j

Proved
DRConvexOpt.InfConv.subset_outer

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

ambiguity-setdistributionally-robust-optimizationouter-approximationp2o-batch-pfp2ap2o-gran-per-chapterp2o-plan-paperp2o-v1

Let P\mathcal PP be the standardized ambiguity set (4) and let {Ij}j∈J\{\mathcal I_j\}_{j\in\mathcal J}{Ij​}j∈J​ be a partition of the index set of its confidence sets, given by a block map. Then for every block jjj,

PβŠ†Pj,\mathcal P \subseteq \mathcal P^j,PβŠ†Pj,

where Pj\mathcal P^jPj imposes only the probability conditions P[(z~,u~)∈Ci]∈[pβ€Ύi,pβ€Ύi]\mathbb P[(\tilde z,\tilde u) \in \mathcal C_i] \in [\underline p_i,\overline p_i]P[(z~,u~)∈Ci​]∈[p​i​,p​i​] for i∈Iji \in \mathcal I_ji∈Ij​, together with the expectation condition of P\mathcal PP.

This inclusion is what makes the naΓ―ve approximation (6) conservative, and it is the first inequality in the proof of Theorem 3.

Formalization Note The statement holds for every block map (surjective or not), without any of the standing conditions (C1)–(C3).

Preamble
import Mathlib
import Definitions.Def_DRConvexOpt_InfConv_Setting
Formal statement
namespace DRConvexOpt.InfConv

open MeasureTheory Matrix Filter Topology

/-- p. 12: the ambiguity set 𝒫 is a subset of each outer approximation 𝒫^j. -/
theorem subset_outer {nP nQ nK nI nJ : β„•} (d : AmbData nP nQ nK nI)
    (blk : Fin (nI + 1) β†’ Fin nJ) (j : Fin nJ) :
    ambiguitySet d βŠ† outerSet d blk j := by sorry

end DRConvexOpt.InfConv
Source
Wiesemann, Kuhn & Sim, Distributionally Robust Convex Optimization, Optimization Online preprint 3757 (version of September 22, 2013), p. 12, sentence following the definition of 𝒫^j
Human review
  • Endorsed by Shuze Chen Β· Oct 9, 2026

    Confirmed by the moderator at approval.

  • Endorsed by mikedeng1 Β· Oct 9, 2026

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

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