SCG derives safe multi-variable screening cuts for sparse ridge regression from the perspective relaxation, with a sufficient condition that rules out binary combinations that cannot appear in any optimal solution.
Safe Screening for Logistic Regression with $\ell_0$-$\ell_2$ Regularization
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In logistic regression, it is often desirable to utilize regularization to promote sparse solutions, particularly for problems with a large number of features compared to available labels. In this paper, we present screening rules that safely remove features from logistic regression with $\ell_0-\ell_2$ regularization before solving the problem. The proposed safe screening rules are based on lower bounds from the Fenchel dual of strong conic relaxations of the logistic regression problem. Numerical experiments with real and synthetic data suggest that a high percentage of the features can be effectively and safely removed apriori, leading to substantial speed-up in the computations.
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Screening Cut Generation for Sparse Ridge Regression
SCG derives safe multi-variable screening cuts for sparse ridge regression from the perspective relaxation, with a sufficient condition that rules out binary combinations that cannot appear in any optimal solution.