Complex chance-constrained optimization under Wasserstein ball
Abstract
This paper develops a data-driven chance-constrained optimization framework with complex-valued decision variables under uncertainty. A Wasserstein ambiguity set centered at the empirical distribution is used to derive worst-case CVaR approximations for individual and joint probabilistic constraints. The individual formulation admits a convex reformulation, whereas the joint formulation leads to a biconvex problem efficiently handled through a convex sequential method. Numerical experiments, including an application to the minimum variance distortionless response, demonstrate the robustness and efficiency of the proposed approach.
Fields of Study & Subjects
Risk and Portfolio Optimization
Probabilistic and Robust Engineering Design
Advanced Multi-Objective Optimization Algorithms
Publication details
- Journal / source
- Operations Research Letters
- Publisher
- Elsevier BV
- Volume
- 69
- Pages
- 107516–107516
- Publication date
- 19 August 2026
- Language
- EN