发表机构
CNRS; Laboratory of signals and systems（法国国家科学研究中心; 信号与系统实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文证明了偏斜广义双曲随机变量下机会约束的最终凸性，通过密度α-递减性质获得精确凸重构，并数值验证了可行集凸性与计算可行性。
AI 中文摘要
机会约束广泛用于不确定性优化中。本文旨在证明带有偏斜广义双曲（GH）随机变量的机会约束的最终凸性（EV）。我们证明了GH分布的密度是$\alpha$-递减的，并获得了带有GH分布的可分离联合机会约束的精确凸重构。我们提供了数值结果来计算$\alpha$-递减阈值参数，并展示了可行集的EV以及求解相关优化问题的计算可行性。
英文摘要
Chance constraints are widely used in optimization under uncertainty. This paper aims to show the eventual convexity (EV) of chance constraints with skewed generalized hyperbolic (GH) random variables. We prove that the densities of GH distributions are $α$-decreasing, and obtain exact convex reformulations for separable jointly chance constraints with GH distributions. We provide numerical results to compute the $α$-decreasing threshold parameters and show the EV of the feasible set together with the computational tractability to solve the associated optimization problems.
Journal refOperations Research Letters, 2026
