On solving a rank regularized minimization problem via equivalent factorized column-sparse regularized models

TL;DR

This paper develops a novel approach for low-rank matrix completion by transforming the rank regularized problem into a factorized column-sparse model, establishing equivalences, and proposing algorithms with convergence guarantees.

Contribution

It introduces an equivalent factorized column-sparse model for rank regularized minimization, along with algorithms and convergence analysis for efficient low-rank matrix completion.

Findings

Algorithms converge to strong stationary points.

Proposed methods outperform existing approaches in experiments.

The model effectively recovers low-rank matrices from incomplete data.

Abstract

Rank regularized minimization problem is an ideal model for the low-rank matrix completion/recovery problem. The matrix factorization approach can transform the high-dimensional rank regularized problem to a low-dimensional factorized column-sparse regularized problem. The latter can greatly facilitate fast computations in applicable algorithms, but needs to overcome the simultaneous non-convexity of the loss and regularization functions. In this paper, we consider the factorized column-sparse regularized model. Firstly, we optimize this model with bound constraints, and establish a certain equivalence between the optimized factorization problem and rank regularized problem. Further, we strengthen the optimality condition for stationary points of the factorization problem and define the notion of strong stationary point. Moreover, we establish the equivalence between the factorization problem and its a nonconvex relaxation in the sense of global minimizers and strong stationary points. To solve the factorization problem, we design two types of algorithms and give an adaptive method to reduce their computation. The first algorithm is from the relaxation point of view and its iterates own some properties from global minimizers of the factorization problem after finite iterations. We give some analysis on the convergence of its iterates to the strong stationary point. The second algorithm is designed for directly solving the factorization problem. We improve the PALM algorithm introduced by Bolte et al. (Math Program Ser A 146:459-494, 2014) for the factorization problem and give its improved convergence results. Finally, we conduct numerical experiments to show the promising performance of the proposed model and algorithms for low-rank matrix completion.