Fast Screening Rules for Optimal Design via Quadratic Lasso Reformulation

The problems of Lasso regression and optimal design of experiments share a critical property: their optimal solutions are typically sparse, i.e., only a small fraction of the optimal variables are non-zero. Therefore, the identification of the support of an optimal solution reduces the dimensionality of the problem and can yield a substantial simplification of the calculations. It has recently been shown that linear regression with a squared $\ell_1$-norm sparsity-inducing penalty is equivalent to an optimal experimental design problem. In this …

design dimensionality identification lasso property regression rules screening small solution solutions support variables