Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning

We present a new class of preconditioned iterative methods for solving linear systems of the form $Ax = b$. Our methods are based on constructing a low-rank Nystr\"om approximation to A using sparse random sketching. This approximation is used to construct a preconditioner, which itself is inverted quickly using additional levels of random sketching and preconditioning. We prove that the convergence of our methods depends on a natural average condition number of A, which improves as the rank of the Nystr\"om approximation increases. Concretely, this allows us to obtain faster runtimes for a number of fundamental linear algebraic problems: 1. We show how to solve any n× n linear system that is well-conditioned except for k outlying large singular values in Õ(n2.065 + k^ω) time, improving on a recent result of [Derezi\'nski, Yang, STOC 2024] for all k n0.78. 2. We give the first Õ(n² + d_λω) time algorithm for solving a regularized linear system (A + λ I)x = b, where A is positive semidefinite with effective dimension d_λ. This problem arises in applications like Gaussian process regression. 3. We give faster algorithms for approximating Schatten p-norms and other matrix norms. For example, for the Schatten 1 (nuclear) norm, we give an algorithm that runs in Õ(n2.11) time, improving on an Õ(n2.18) method of [Musco et al., ITCS 2018]. Interestingly, previous state-of-the-art algorithms for most of the problems above relied on stochastic iterative methods, like stochastic coordinate and gradient descent. Our work takes a completely different approach, instead leveraging tools from matrix sketching.