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Exploiting Low-Rank Structure in Semidefinite Programming by Approximate Operator Splitting
[article]
2018
arXiv
pre-print
In contrast with many other convex optimization classes, state-of-the-art semidefinite programming solvers are yet unable to efficiently solve large scale instances. This work aims to reduce this scalability gap by proposing a novel proximal algorithm for solving general semidefinite programming problems. The proposed methodology, based on the primal-dual hybrid gradient method, allows the presence of linear inequalities without the need of adding extra slack variables and avoids solving a
arXiv:1810.05231v3
fatcat:3uk6cjpedfcofjdcc5dahzkxjm