摘要：Sign consistency of the Lasso requires the stringent irrepresentable condition. This paper examines whether preconditioning can circumvent this condition. Let $\mathbf{X}\in\mathbb{R}^{n\times p}$ and $Y\in\mathbb{R}^{n}$ satisfy the standard linear regression equation. Instead of computing the Lasso with $(\mathbf{X},Y)$, preconditioning first left multiplies by $F\in\mathbb{R}^{n\times n}$ and then computes the Lasso with $(F\mathbf{X},FY)$.
