Abstract

 

In this short course, we consider the problem of how to recover a low rank matrix from its limited information, which arises from

numerous practical applications in machine learning, imaging, signal processing, computer vision, etc. Typically, the low rank

matrix to be recovered is of large scale and the number of linear measurements is small. We will start with some examples that

can be formulated to the low-rank matrix reconstruction problem. Then we will introduce both convex and non-convex algorithms,

as well as the theory on the guarantee of their convergence to the correct low-rank matrix.