  Low-rank approximations are essential in modern data science. The
interpolative decomposition provides one such approximation. Its distinguishing
feature is that it reuses columns from the original matrix. This enables it to
preserve matrix properties such as sparsity and non-negativity. It also helps
save space in memory. In this work, we introduce two optimized algorithms to
construct an interpolative decomposition along with numerical evidence that
they outperform the current state of the art.
