Gradient Descent Meets Shift-and-Invert Preconditioning for Eigenvector Computation

Shift-and-invert preconditioning, as a classic acceleration technique for the leading eigenvector computation, has received much attention again recently, owing to fast least-squares solvers for efficiently approximating matrix inversions in power iterations.