Regularized randomized iterative algorithms for factorized linear systems

TL;DR

This paper introduces two new regularized randomized iterative algorithms that efficiently find structured solutions to factorized linear systems, improving upon existing methods by leveraging regularization and convergence guarantees.

Contribution

It combines randomized Kaczmarz and Gauss-Seidel methods with regularization to find structured solutions in factorized linear systems, with proven linear convergence.

Findings

Algorithms can find sparse least squares solutions.

New methods outperform existing randomized algorithms.

Effective for structured solutions in factorized systems.

Abstract

Randomized iterative algorithms for solving a factorized linear system, $\mathbf A\mathbf B\mathbf x=\mathbf b$ with $\mathbf A\in{\mathbb{R}}^{m\times \ell}$, $\mathbf B\in{\mathbb{R}}^{\ell\times n}$, and $\mathbf b\in{\mathbb{R}}^m$, have recently been proposed. They take advantage of the factorized form and avoid forming the matrix $\mathbf C=\mathbf A\mathbf B$ explicitly. However, they can only find the minimum norm (least squares) solution. In contrast, the regularized randomized Kaczmarz (RRK) algorithm can find solutions with certain structures from consistent linear systems. In this work, by combining the randomized Kaczmarz algorithm or the randomized Gauss--Seidel algorithm with the RRK algorithm, we propose two novel regularized randomized iterative algorithms to find (least squares) solutions with certain structures of $\mathbf A\mathbf B\mathbf x=\mathbf b$. We prove linear convergence of the new algorithms. Computed examples are given to illustrate that the new algorithms can find sparse (least squares) solutions of $\mathbf A\mathbf B\mathbf x=\mathbf b$ and can be better than the existing randomized iterative algorithms for the corresponding full linear system $\mathbf C\mathbf x=\mathbf b$ with $\mathbf C=\mathbf A\mathbf B$.