{"ID":2870301,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.12859","arxiv_id":"2509.12859","title":"Polynomial Optimization via Random Projection and Consensus","abstract":"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.","short_abstract":"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.","url_abs":"https://arxiv.org/abs/2509.12859","url_pdf":"https://arxiv.org/pdf/2509.12859v1","authors":"[\"Etienne Buehrle\",\"Christoph Stiller\"]","published":"2025-09-16T09:17:27Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}