  In this note we provide and analyze a simple method that given an $n \times
d$ matrix, outputs approximate $\ell_p$-Lewis weights, a natural measure of the
importance of the rows with respect to the $\ell_p$ norm, for $p \geq 2$. More
precisely, we provide a simple post-processing procedure that turns natural
one-sided approximate $\ell_p$-Lewis weights into two-sided approximations.
When combined with a simple one-sided approximation algorithm presented by Lee
(PhD thesis, `16) this yields an algorithm for computing two-sided
approximations of the $\ell_p$-Lewis weights of an $n \times d$-matrix using
$\mathrm{poly}(d,p)$ approximate leverage score computations. While efficient
high-accuracy algorithms for approximating $\ell_p$-Lewis had been established
previously by Fazel, Lee, Padmanabhan and Sidford (SODA `22), the simple
structure and approximation tolerance of our algorithm may make it of use for
different applications.
