Distributionally Robust Optimization: A Review
Optimization and Control
2022-10-25 v1 Machine Learning
Machine Learning
Abstract
The concepts of risk-aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. Statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these concepts. A modeling framework, called distributionally robust optimization (DRO), has recently received significant attention in both the operations research and statistical learning communities. This paper surveys main concepts and contributions to DRO, and its relationships with robust optimization, risk-aversion, chance-constrained optimization, and function regularization.
Keywords
Cite
@article{arxiv.1908.05659,
title = {Distributionally Robust Optimization: A Review},
author = {Hamed Rahimian and Sanjay Mehrotra},
journal= {arXiv preprint arXiv:1908.05659},
year = {2022}
}