# Low-rank matrix recovery with Ky Fan 2-k-norm

## TL;DR

This paper introduces Ky Fan 2-k-norm models for low-rank matrix recovery, utilizing a difference of convex algorithm to enhance recoverability, with promising numerical results demonstrating effectiveness.

## Contribution

The paper presents a novel Ky Fan 2-k-norm-based approach and a DCA algorithm for nonconvex low-rank matrix recovery, improving upon existing methods.

## Key findings

- High recoverability rates achieved in numerical experiments
- Effective application of Ky Fan 2-k-norm models to matrix recovery
- Successful implementation of DCA for nonconvex optimization

## Abstract

We propose Ky Fan 2-k-norm-based models for the nonconvex low-rank matrix recovery problem. A general difference of convex algorithm (DCA) is developed to solve these models. Numerical results show that the proposed models achieve high recoverability rates.