The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints

This work shows that with a step size, the Mixing method converges to the global optimum of the semidefinite program almost surely in a locally linear rate under random initialization, and is the first low-rank semideFinite programming method to achieve a global optimum on the spherical manifold without assumption.