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

NeurIPS 2018 Zhiqiang Xu

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. In this work, we adopt an inexact Riemannian gradient descent perspective to investigate this technique on the effect of the step-size scheme... (read more)

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