On Stability in Optimistic Bilevel Optimization
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929725517070336 |
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| author | Royset, Johannes O. |
| author_facet | Royset, Johannes O. |
| contents | Solutions of bilevel optimization problems tend to suffer from instability under changes to problem data. In the optimistic setting, we construct a lifted formulation that exhibits desirable stability properties under mild assumptions that neither invoke convexity nor smoothness. The upper- and lower-level problems might involve integer restrictions and disjunctive constraints. In a range of results, we invoke at most pointwise and local calmness for the lower-level problem in a sense that holds broadly. The lifted formulation is computationally attractive with structural properties being brought out and an outer approximation algorithm becoming available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13323 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On Stability in Optimistic Bilevel Optimization Royset, Johannes O. Optimization and Control Machine Learning Systems and Control Solutions of bilevel optimization problems tend to suffer from instability under changes to problem data. In the optimistic setting, we construct a lifted formulation that exhibits desirable stability properties under mild assumptions that neither invoke convexity nor smoothness. The upper- and lower-level problems might involve integer restrictions and disjunctive constraints. In a range of results, we invoke at most pointwise and local calmness for the lower-level problem in a sense that holds broadly. The lifted formulation is computationally attractive with structural properties being brought out and an outer approximation algorithm becoming available. |
| title | On Stability in Optimistic Bilevel Optimization |
| topic | Optimization and Control Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2408.13323 |