The Generalized Matrix Separation Problem: Algorithms

TL;DR

This paper details efficient iterative algorithms and implementation strategies for solving a convex optimization problem in generalized matrix separation, with theoretical guarantees and improved performance in practical cases.

Contribution

It introduces a preconditioning technique that enhances the efficiency, accuracy, and robustness of algorithms for generalized matrix separation problems.

Findings

Preconditioning significantly improves algorithm performance.

Algorithms are effective for practical structured matrices.

Theoretical guarantees support the proposed methods.

Abstract

When given a generalized matrix separation problem, which aims to recover a low rank matrix $L_0$ and a sparse matrix $S_0$ from $M_0=L_0+HS_0$, the work \cite{CW25} proposes a novel convex optimization problem whose objective function is the sum of the $\ell_1$-norm and nuclear norm. In this paper we detail the iterative algorithms and its associated computations for solving this convex optimization problem. We present various efficient implementation strategies, with attention to practical cases where $H$ is circulant, separable, or block structured. Notably, we propose a preconditioning technique that drastically improved the performance of our algorithms in terms of efficiency, accuracy, and robustness. While this paper serves as an illustrative algorithm implementation manual, we also provide theoretical guarantee for our preconditioning strategy. Numerical results demonstrate the effectiveness of the proposed approach.