Distributionally Robust Optimization with Polynomial Robust Constraints
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910935902322688 |
|---|---|
| author | Nie, Jiawang Zhong, Suhan |
| author_facet | Nie, Jiawang Zhong, Suhan |
| contents | This paper studies distributionally robust optimization (DRO) with polynomial robust constraints. We give a Moment-SOS relaxation approach to solve the DRO. This reduces to solving linear conic optimization with semidefinite constraints. When the DRO problem is SOS-convex, we show that it is equivalent to the linear conic relaxation and it can be solved by the Moment-SOS algorithm. For nonconvex cases, we also give concrete conditions such that the DRO can be solved globally. Numerical experiments are given to show the efficiency of the method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_15591 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Distributionally Robust Optimization with Polynomial Robust Constraints Nie, Jiawang Zhong, Suhan Optimization and Control This paper studies distributionally robust optimization (DRO) with polynomial robust constraints. We give a Moment-SOS relaxation approach to solve the DRO. This reduces to solving linear conic optimization with semidefinite constraints. When the DRO problem is SOS-convex, we show that it is equivalent to the linear conic relaxation and it can be solved by the Moment-SOS algorithm. For nonconvex cases, we also give concrete conditions such that the DRO can be solved globally. Numerical experiments are given to show the efficiency of the method. |
| title | Distributionally Robust Optimization with Polynomial Robust Constraints |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2308.15591 |