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Eventual convexity for separable chance constraints with skewed generalized hyperbolic random variables

  • Heng Zhang
  • Abdel Lisser
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Abstract

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.

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Cite

BibTeXRIS
BibTeX
@misc{zhang2026eventual,
  title = {{Eventual convexity for separable chance constraints with skewed generalized hyperbolic random variables}},
  author = {Heng Zhang and Abdel Lisser},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.15403},
  eprint = {2609.15403},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.15403},
}
RIS
TY  - UNPB
TI  - Eventual convexity for separable chance constraints with skewed generalized hyperbolic random variables
AU  - Heng Zhang
AU  - Abdel Lisser
PY  - 2026
DA  - 2026/09/14
PB  - arXiv
DO  - 10.48550/arxiv.2609.15403
UR  - https://arxiv.org/abs/2609.15403
AB  - 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.
ER  -