Scalable Binary CUR Low-Rank Approximation Algorithm

This paper proposes a scalable binary CUR low-rank approximation algorithm that leverages parallel selection of representative rows and columns within a deterministic framework. By employing a blockwise adaptive cross approximation strategy, the algorithm efficiently identifies dominant components in large-scale matrices, thereby reducing computational costs. Numerical experiments on $16,384 \times 16,384$ matrices demonstrate remarkable scalability, with execution time decreasing from $12.37$ seconds using $2$ processes to $1.02$ seconds using $64$ processes. The tests on Hilbert matrices and synthetic low-rank matrices across various sizes demonstrate a near-optimal reconstruction accuracy. These results suggest a potential for practical application in large-scale matrix low-rank approximation.