A Distributionally Robust Optimization Approach to the NASA Langley Uncertainty Quantification Challenge
Methodology
2020-06-30 v1
Abstract
We study a methodology to tackle the NASA Langley Uncertainty Quantification Challenge problem, based on an integration of robust optimization, more specifically a recent line of research known as distributionally robust optimization, and importance sampling in Monte Carlo simulation. The main computation machinery in this integrated methodology boils down to solving sampled linear programs. We will illustrate both our numerical performances and theoretical statistical guarantees obtained via connections to nonparametric hypothesis testing.
Cite
@article{arxiv.2006.15689,
title = {A Distributionally Robust Optimization Approach to the NASA Langley Uncertainty Quantification Challenge},
author = {Yuanlu Bai and Zhiyuan Huang and Henry Lam},
journal= {arXiv preprint arXiv:2006.15689},
year = {2020}
}
Comments
Published in the Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference