Improving Clique Decompositions of Semidefinite Relaxations for Optimal Power Flow Problems
Optimization and Control
2019-12-20 v1 Robotics
Abstract
Semidefinite Programming (SDP) provides tight lower bounds for Optimal Power Flow problems. However, solving large-scale SDP problems requires exploiting sparsity. In this paper, we experiment several clique decomposition algorithms that lead to different reformulations and we show that the resolution is highly sensitive to the clique decomposition procedure. Our main contribution is to demonstrate that minimizing the number of additional edges in the chordal extension is not always appropriate to get a good clique decomposition.
Cite
@article{arxiv.1912.09232,
title = {Improving Clique Decompositions of Semidefinite Relaxations for Optimal Power Flow Problems},
author = {Julie Sliwak and Miguel Anjos and Lucas Létocart and Jean Maeght and Emiliano Traversi},
journal= {arXiv preprint arXiv:1912.09232},
year = {2019}
}