Fast linear algebra is stable

TLDR: In this article, it was shown that a large class of fast recursive matrix multiplication algorithms are stable in a norm-wise sense, including LU decomposition, QR decomposition and linear equation solving, matrix inversion, solving least squares problems, eigenvalue problems and singular value decomposition.

Abstract: In an earlier paper, we showed that a large class of fast recursive matrix multiplication algorithms is stable in a normwise sense, and that in fact if multiplication of $n$-by-$n$ matrices can be done by any algorithm in $O(n^{\omega + \eta})$ operations for any $\eta > 0$, then it can be done stably in $O(n^{\omega + \eta})$ operations for any $\eta > 0$. Here we extend this result to show that essentially all standard linear algebra operations, including LU decomposition, QR decomposition, linear equation solving, matrix inversion, solving least squares problems, (generalized) eigenvalue problems and the singular value decomposition can also be done stably (in a normwise sense) in $O(n^{\omega + \eta})$ operations.