Decreasing Weighted Sorted  ${\ell_1}$ Regularization
We consider a new family of regularizers, termed weighted sorted ℓ1 norms (WSL1), which generalizes the recently introduced octagonal shrinkage and clustering algorithm for regression (OSCAR) and also contains the ℓ1 and ℓ∞ norms as particular instances. We focus on a special case of the WSL1, the decreasing WSL1 (DWSL1), where the elements of the argument vector are sorted in non-increasing order and the weights are also non-increasing. In this letter, after showing that the DWSL1 is indeed a norm, we derive two key tools for its use as a regularizer: the dual norm and the Moreau proximity operator.