Low-Rank Matrix Completion

Low-rank matrix completion is a crucial problem that aims to recover the low-rank structure of a matrix from partial entries, and it has wide applications in recommendation systems, sketching, quantum tomography, and other fields. The task involves accurately reconstructing the complete low-rank matrix using limited observed data, which holds significant practical value.