A Sherman--Morrison--Woodbury approach to solving least squares problems with low-rank updates

We present a simple formula to update the pseudoinverse of a full-rank rectangular matrix that undergoes a low-rank modification, and demonstrate its utility for solving least squares problems. The resulting algorithm can be dramatically faster than solving the modified least squares problem from scratch, just like the speedup enabled by Sherman–Morrison–Woodbury for solving linear systems with low-rank modifications.