On Polyhedral and Second-Order Cone Decompositions of Semidefinite Optimization Problems
arXiv:1910.03143 · doi:10.1016/j.orl.2019.12.003
Abstract
We study a cutting-plane method for semidefinite optimization problems (SDOs), and supply a proof of the method's convergence, under a boundedness assumption. By relating the method's rate of convergence to an initial outer approximation's diameter, we argue that the method performs well when initialized with a second-order-cone approximation, instead of a linear approximation. We invoke the method to provide bound gaps of 0.5-6.5% for sparse PCA problems with s of covariates, and solve nuclear norm problems over 500x500 matrices.
Submitted minor revision to Operations Research Letters; removed footnotes and corrected some minor typos in previous version