@inbook{c474db8b1055401886e3d4dfa8eb2387,
title = "Random Projections for Semidefinite Programming",
abstract = "Random projections can reduce the dimensionality of point sets while keeping approximate congruence. Applying random projections to optimization problems raises many theoretical and computational issues. Most of the theoretical issues in the application of random projections to conic programming were addressed in Liberti et al. (Linear Algebr. Appl. 626:204–220, 2021) [1]. This paper focuses on semidefinite programming.",
author = "Leo Liberti and Benedetto Manca and Antoine Oustry and Poirion, \{Pierre Louis\}",
note = "Publisher Copyright: {\textcopyright} The Author(s).",
year = "2023",
month = jan,
day = "1",
doi = "10.1007/978-3-031-28863-0\_9",
language = "English",
series = "AIRO Springer Series",
publisher = "Springer Nature",
pages = "97--108",
booktitle = "AIRO Springer Series",
}