Efficient Designs of SLOPE Penalty Sequences in Finite Dimension

14 Feb 2021 Yiliang Zhang Zhiqi Bu

In linear regression, SLOPE is a new convex analysis method that generalizes the Lasso via the sorted L1 penalty: larger fitted coefficients are penalized more heavily. This magnitude-dependent regularization requires an input of penalty sequence $\lambda$, instead of a scalar penalty as in the Lasso case, thus making the design extremely expensive in computation... (read more)

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