Lemma 1, p.11 — the identity (9) for
ProvedOLPDual.lemma_1online-linear-programmingoperations-researchp2o-gran-per-chapterp2o-plan-paperp2o-strengthenedp2o-v1sample-average-approximationstochastic-programming
Let and be as in (7) and (8). For points and ,
where the expectation is taken with respect to . The two terms play the role of the first- and second-order terms of a Taylor expansion of the non-differentiable function ; this is an identity of Knight's type.
Differences from the source:
- generalization: p ≥ 0 and p* the optimal solution → arbitrary p and pstar in ℝ^m; reason: the proof on p.31 uses neither; the printed lemma is the special case
- reading: identity (9) "holds regardless of the distribution 𝒫" (p.11) → Assumption 1 is a hypothesis; reason: it is the standing assumption of §3 and makes every expectation finite
- encoding: u = (r, a) → u = (a, r); reason: order of the pair only; same law
Preamble
import Mathlib import Definitions.Def_OLPDual_Model open MeasureTheory ProbabilityTheory open scoped RealInnerProductSpace
Formal statement
namespace OLPDual
/-- **Lemma 1**, Li–Ye, arXiv:1909.05499v5, p.11 (PDF 11), display (9):
`f(p) − f(p*) = ∇f(p*)ᵀ(p − p*) + E[∫_{aᵀp}^{aᵀp*} (I(r > v) − I(r > aᵀp*)) dv]`.
**Formalization Note** The paper states it for `p ≥ 0` and the optimal `p*`; the proof (p.31) uses
neither, so `p` and `pstar` are arbitrary here. Assumption 1 makes every integral finite. -/
theorem lemma_1 (Θ : Model) {m : ℕ} (d : E m) (law : Measure (E m × ℝ))
[IsProbabilityMeasure law] (hA1 : Assumption1 Θ d law) (p pstar : E m) :
f d law p - f d law pstar =
⟪gradf d law pstar, p - pstar⟫ + ∫ u, knightTerm u.1 u.2 p pstar ∂law := by sorry
end OLPDual
Source
Li–Ye, Online Linear Programming: Dual Convergence, New Algorithms, and Regret Bounds, arXiv:1909.05499v5 (19 Apr 2021), Lemma 1, display (9), p.11; proof p.31
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.