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Theorem 3, p. 13 — (6) ⟹ (7) ⟹ (3) for the naïve approximation, the infimal convolution bound and the distributionally robust constraint; equivalence if J = 1

Proved
DRConvexOpt.InfConv.theorem_3

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

conservative-approximationdistributionally-robust-optimizationinfimal-convolutionp2o-batch-pfp2ap2o-gran-per-chapterp2o-plan-paperp2o-v1weak-nesting

Let P\mathcal PP be the standardized ambiguity set (4) with confidence sets Ci={(z,u):Ciz+Diu≼Kici}\mathcal C_i = \{(z,u) : C_iz + D_iu \preccurlyeq_{\mathcal K_i} c_i\}Ci​={(z,u):Ci​z+Di​u≼Ki​​ci​}, i∈Ii \in \mathcal Ii∈I, and assume:

  1. every Ki\mathcal K_iKi​ is a proper cone and 0≤p‾i≤p‾i≤10 \le \underline p_i \le \overline p_i \le 10≤p​i​≤p​i​≤1;
  2. (C1): CI\mathcal C_ICI​ is bounded and p‾I=p‾I=1\underline p_I = \overline p_I = 1p​I​=p​I​=1;
  3. (C2): some P∈P\mathbb P \in \mathcal PP∈P has P[(z~,u~)∈Ci]∈(p‾i,p‾i)\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​) whenever p‾i<p‾i\underline p_i < \overline p_ip​i​<p​i​;
  4. (C3): v(x,z)=max⁡l (Slz+sl)⊤x+tl⊤z+tlv(x,z) = \max_{l}\,(S_lz + s_l)^\top x + \mathbf t_l^\top z + t_lv(x,z)=maxl​(Sl​z+sl​)⊤x+tl⊤​z+tl​;
  5. {Ij}j∈J\{\mathcal I_j\}_{j\in\mathcal J}{Ij​}j∈J​ is a partition of I\mathcal II (all blocks nonempty) satisfying (N′), with outer approximations Pj\mathcal P^jPj;
  6. (added) the uniform first-moment bound: for each jjj there is MjM_jMj​ with EP∥z~∥≤Mj\mathbb E_{\mathbb P}\|\tilde z\| \le M_jEP​∥z~∥≤Mj​ for all P∈Pj\mathbb P\in\mathcal P^jP∈Pj.

Then for every x∈RNx \in \mathbb R^Nx∈RN and w∈Rw \in \mathbb Rw∈R, the naïve approximation (6), the infimal convolution bound (7) and the distributionally robust constraint (3),

(6) min⁡j∈Jsup⁡P∈PjEP[v(x,z~)]≤w,(7) inf⁡(y,δ)∈Γ(x)∑j∈Jδjsup⁡P∈PjEP[v(yj/δj,z~)]≤w,(3) EP[v(x,z~)]≤w  ∀P∈P,\text{(6)}\ \min_{j\in\mathcal J}\sup_{\mathbb P\in\mathcal P^j}\mathbb E_{\mathbb P}[v(x,\tilde z)]\le w,\qquad \text{(7)}\ \inf_{(y,\delta)\in\Gamma(x)}\sum_{j\in\mathcal J}\delta_j\sup_{\mathbb P\in\mathcal P^j}\mathbb E_{\mathbb P}[v(y_j/\delta_j,\tilde z)]\le w,\qquad \text{(3)}\ \mathbb E_{\mathbb P}[v(x,\tilde z)]\le w\ \ \forall\mathbb P\in\mathcal P,(6) j∈Jmin​P∈Pjsup​EP​[v(x,z~)]≤w,(7) (y,δ)∈Γ(x)inf​j∈J∑​δj​P∈Pjsup​EP​[v(yj​/δj​,z~)]≤w,(3) EP​[v(x,z~)]≤w  ∀P∈P,

satisfy

(6)⟹(7)⟹(3),\text{(6)} \Longrightarrow \text{(7)} \Longrightarrow \text{(3)},(6)⟹(7)⟹(3),

and if J=1J = 1J=1, then (3) implies both (7) and (6), so all three are equivalent.

Both (6) and (7) are thus conservative surrogates for (3) when the nesting condition (N) of Theorem 1 fails, and (7) is never looser than (6).

Formalization Note The uniform first-moment bound is not in the paper. The printed proof of (6) ⇒\Rightarrow⇒ (7) uses that sup⁡P∈PjEP[v(0,z~)]\sup_{\mathbb P\in\mathcal P^j}\mathbb E_{\mathbb P}[v(0,\tilde z)]supP∈Pj​EP​[v(0,z~)] is finite, which fails in general: with P=1P = 1P=1, C1=[0,12]\mathcal C_1 = [0,\tfrac12]C1​=[0,21​] (probability ≥12\ge \tfrac12≥21​), C2=[0,1]\mathcal C_2 = [0,1]C2​=[0,1] (probability 1), the singleton partition, v(x,z)=zv(x,z) = zv(x,z)=z and w=1w = 1w=1, (6) holds while (7) fails because P1\mathcal P^1P1 contains 12δ0+12δM\tfrac12\delta_0 + \tfrac12\delta_M21​δ0​+21​δM​ for every MMM. The bound holds, for example, when every block contains a bounded confidence set with p‾i=1\underline p_i = 1p​i​=1. Indices are 0-based: I\mathcal II is Fin (nI + 1), CI\mathcal C_ICI​ is Fin.last nI, J\mathcal JJ is Fin nJ, and the partition is a surjective block map. Distributions in P\mathcal PP and Pj\mathcal P^jPj have finite first moments. (6) and (7) are EReal-valued; (3) is in its ∀\forall∀-form. The hypotheses (N′), (C1), proper cones and 0≤p‾i≤p‾i≤10\le\underline p_i\le\overline p_i\le10≤p​i​≤p​i​≤1 are the paper's setting and are not used by the proof.

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

open MeasureTheory Matrix Filter Topology

/-- Theorem 3, p. 13: under (C1)–(C3) and (N′), with the disclosed added assumption
`UnifFirstMoment`, (6) ⟹ (7) ⟹ (3), and the reverse implications hold if `J = 1`.
Indices are 0-based; `C_I` is `d.conf (Fin.last nI)`; the partition is given by the surjective
block map `blk`, with `𝒥 = Fin nJ`. -/
theorem theorem_3 {nP nQ nK nI nN nL nJ : ℕ} [NeZero nL]
    (d : AmbData nP nQ nK nI) (v : PWAff nN nP nL)
    (blk : Fin (nI + 1) → Fin nJ) (hblk : Function.Surjective blk)
    (hK : ∀ i, IsProperCone (d.K i))
    (hp : ∀ i, 0 ≤ d.plo i ∧ d.plo i ≤ d.phi i ∧ d.phi i ≤ 1)
    (hC1 : Bornology.IsBounded (d.conf (Fin.last nI)) ∧ d.plo (Fin.last nI) = 1 ∧
      d.phi (Fin.last nI) = 1)
    (hC2 : ∃ μ ∈ ambiguitySet d, ∀ i, d.plo i < d.phi i →
      μ.real (d.conf i) ∈ Set.Ioo (d.plo i) (d.phi i))
    (hN' : WeakNesting d blk) (hFM : UnifFirstMoment d blk)
    (x : Fin nN → ℝ) (w : ℝ) :
    (naiveBound d blk v x ≤ (w : EReal) → infConvBound d blk v x ≤ (w : EReal)) ∧
    (infConvBound d blk v x ≤ (w : EReal) →
      ∀ μ ∈ ambiguitySet d, ∫ ω, v.eval x ω.1 ∂μ ≤ w) ∧
    (nJ = 1 → (∀ μ ∈ ambiguitySet d, ∫ ω, v.eval x ω.1 ∂μ ≤ w) →
      infConvBound d blk v x ≤ (w : EReal) ∧ naiveBound d blk v x ≤ (w : EReal)) := by sorry

end DRConvexOpt.InfConv
Source
Wiesemann, Kuhn & Sim, Distributionally Robust Convex Optimization, Optimization Online preprint 3757 (version of September 22, 2013), p. 13, Theorem 3 (proof p. 37)
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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