Moment Relaxations for Data-Driven Wasserstein Distributionally Robust Optimization
Optimization and Control
2025-05-27 v1
Abstract
We propose moment relaxations for data-driven Wasserstein distributionally robust optimization problems. Conditions are identified to ensure asymptotic consistency of such relaxations for both single-stage and two-stage problems, together with examples that illustrate their necessity. Numerical experiments are also included to illustrate the proposed relaxations.
Cite
@article{arxiv.2505.19278,
title = {Moment Relaxations for Data-Driven Wasserstein Distributionally Robust Optimization},
author = {Shixuan Zhang and Suhan Zhong},
journal= {arXiv preprint arXiv:2505.19278},
year = {2025}
}
Comments
25 pages