Decreasing Weighted Sorted ℓ₁ Regularization

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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 paper, 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.