Efficient sparse semismooth Newton methods for the clustered lasso problem

22 Aug 2018Meixia LinYong-Jin LiuDefeng SunKim-Chuan Toh

We focus on solving the clustered lasso problem, which is a least squares problem with the $\ell_1$-type penalties imposed on both the coefficients and their pairwise differences to learn the group structure of the regression parameters. Here we first reformulate the clustered lasso regularizer as a weighted ordered-lasso regularizer, which is essential in reducing the computational cost from $O(n^2)$ to $O(n\log (n))$... (read more)

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