Convergence and Bound Computation for Chance Constrained Distributionally Robust Models using Sample Approximation
Optimization and Control
2025-01-17 v3
Abstract
This paper considers a distributionally robust chance constraint model with a general ambiguity set. We show that a sample based approximation of this model converges under suitable sufficient conditions. We also show that upper and lower bounds on the optimal value of the model can be estimated statistically. Specific ambiguity sets are discussed as examples.
Cite
@article{arxiv.2408.12018,
title = {Convergence and Bound Computation for Chance Constrained Distributionally Robust Models using Sample Approximation},
author = {Jiaqi Lei and Sanjay Mehrotra},
journal= {arXiv preprint arXiv:2408.12018},
year = {2025}
}