Accelerating Power Method with Fast Sketching for Stronger Low-Rank Approximation

ArXi:2605.09755v1 Announce Type: cross The power method is one of the most fundamental tools for extracting top principal components from data through low-rank matrix approximation. Yet, when the target rank is large, the cost of matrix multiplication associated with this procedure becomes a major bottleneck. We develop an algorithmic and theoretical framework for accelerating the power method using fast sketching, which is a popular paradigm in randomized linear algebra.