  The ordered weighted $\ell_1$ (OWL) norm is a newly developed generalization
of the Octogonal Shrinkage and Clustering Algorithm for Regression (OSCAR)
norm. This norm has desirable statistical properties and can be used to perform
simultaneous clustering and regression. In this paper, we show how to compute
the projection of an $n$-dimensional vector onto the OWL norm ball in
$O(n\log(n))$ operations. In addition, we illustrate the performance of our
algorithm on a synthetic regression test.
