Chance constraint value-at-risk bound
ProvedDRCVRP.RCI.prob_le_iff_valueAtRisk_lechance-constraintsp2o-batch-p200bp2o-gran-per-chapterp2o-plan-paperp2o-v1value-at-risk
Let be a probability distribution on , let be a real-valued measurable function of the demand vector, and let . Write for the -quantile of . Then for every threshold ,
This equivalence turns an individual chance constraint into a deterministic bound on a quantile, and underlies the definition of the demand estimator.
Formalization Note The probability bound is written ENNReal.ofReal (1 - ε) ≤ P {q | X q ≤ τ}; VaR is MultistageStochastic.valueAtRisk P X (1 - ε).
Preamble
import Mathlib import Definitions.Def_MultistageStochastic_RiskFunctional open MeasureTheory
Formal statement
namespace DRCVRP.RCI
/-- §3, p. 720: for a random variable `X̃` governed by a probability distribution `ℚ` and
`ε ∈ (0,1)`, `ℚ[X̃ ≤ τ] ≥ 1 - ε ⟺ ℚ-VaR_{1-ε}[X̃] ≤ τ` for every threshold `τ`. -/
theorem prob_le_iff_valueAtRisk_le {n : ℕ} (P : Measure (Fin n → ℝ)) [IsProbabilityMeasure P]
(X : (Fin n → ℝ) → ℝ) (hX : Measurable X) (ε : ℝ) (hε0 : 0 < ε) (hε1 : ε < 1) (τ : ℝ) :
ENNReal.ofReal (1 - ε) ≤ P {q | X q ≤ τ} ↔
MultistageStochastic.valueAtRisk P X (1 - ε) ≤ τ := by sorry
end DRCVRP.RCI
Source
Ghosal and Wiesemann, The Distributionally Robust Chance-Constrained Vehicle Routing Problem, Oper. Res. 68(3) (2020) 716–732, §3, p. 720 (unlabelled display after the definition of VaR)
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.