CUR approximation: computation and applications

Yuji Nakatsukasa explains how CUR decompositions approximate a matrix using selected columns and rows, without inspecting every entry once the indices are chosen. Near-optimal CUR approximations exist relative to the truncated singular value decomposition, and efficient algorithms make them useful for large problems. The talk covers computation and theoretical guarantees before exploring applications to approximation theory, model reduction, and parameter-dependent problems.