Frameworks and Results in Distributionally Robust Optimization
The concepts of risk aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. The statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these concepts. A modeling framework, called distribut...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Université de Montpellier
2022-07-01
|
| Series: | Open Journal of Mathematical Optimization |
| Subjects: | |
| Online Access: | https://ojmo.centre-mersenne.org/articles/10.5802/ojmo.15/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The concepts of risk aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. The 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 relationships with robust optimization, risk aversion, chance-constrained optimization, and function regularization. Various approaches to model the distributional ambiguity and their calibrations are discussed. The paper also describes the main solution techniques used to the solve the resulting optimization problems. |
|---|---|
| ISSN: | 2777-5860 |