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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2509.12859 |
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| _version_ | 1866911157465382912 |
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| author | Buehrle, Etienne Stiller, Christoph |
| author_facet | Buehrle, Etienne Stiller, Christoph |
| contents | We propose a black-box approach to reducing large semidefinite programs to a set of smaller semidefinite programs by projecting to random linear subspaces. We evaluate our method on a set of polynomial optimization problems, demonstrating improved scalability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12859 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Polynomial Optimization via Random Projection and Consensus Buehrle, Etienne Stiller, Christoph Optimization and Control 90C22, 93C10, 28A99 We propose a black-box approach to reducing large semidefinite programs to a set of smaller semidefinite programs by projecting to random linear subspaces. We evaluate our method on a set of polynomial optimization problems, demonstrating improved scalability. |
| title | Polynomial Optimization via Random Projection and Consensus |
| topic | Optimization and Control 90C22, 93C10, 28A99 |
| url | https://arxiv.org/abs/2509.12859 |