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.
@article{arxiv.2509.12859,
title = {Polynomial Optimization via Random Projection and Consensus},
author = {Etienne Buehrle and Christoph Stiller},
journal= {arXiv preprint arXiv:2509.12859},
year = {2025}
}