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Autores principales: Buehrle, Etienne, Stiller, Christoph
Formato: Preprint
Publicado: 2025
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Acceso en línea:https://arxiv.org/abs/2509.12859
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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