An Interior-Point Method for Semidefinite Programming

We propose a new interior-point-based method to minimize a linear function of a matrix variable subject to linear equality and inequality constraints over the set of positive semidefinite matrices. We show that the approach is very efficient for graph bisection problems such as max–cut. Other applications include max–min eigenvalue problems and relaxations for the stable set problem.