For smooth optimization problems with a Hermitian positive semidefinite fixed-rank constraint, we consider three existing approaches including the simple Burer–Monteiro method, and Riemannian optimization over quotient geometry and the embedded geometry. These three methods can be all represented via quotient geometry with three Riemannian metrics \(g^i(\cdot , \cdot )\) \((i=1,2,3)\) . By taking the nonlinear conjugate gradient method (CG) as an example, we show that CG in the factor-based Burer–Monteiro approach is equivalent to Riemannian CG on the quotient geometry with the Bures–Wasserstein metric \(g^1\) . Riemannian CG on the quotient geometry with the metric \(g^3\) is equivalent to Riemannian CG on the embedded geometry. For comparing the three approaches, we analyze the condition number of the Riemannian Hessian near a minimizer under the three different metrics. Under certain assumptions, the condition number from the Bures–Wasserstein metric \(g^1\) is significantly worse than the other two metrics. Numerical experiments show that the Burer–Monteiro CG method has obviously slower asymptotic convergence rate either when the minimizer has a large condition number or when it is rank deficient, which is consistent with the condition number analysis.
