In order to solve large-scale lasso problems, screening algorithms have been developed that discard features with zero coefficients based on a computationally efficient screening rule. Most existing screening rules were developed from a spherical constraint and half-space constraints on a dual optimal solution. However, existing rules admit at most two half-space constraints due to the computational cost incurred by the half-spaces, even though additional constraints may be useful to discard more features. In this paper, we present AdaScreen, an adaptive lasso screening rule ensemble, which allows to combine any one sphere with multiple half-space constraints on a dual optimal solution. Thanks to geometrical considerations that lead to a simple closed form solution for AdaScreen, we can incorporate multiple half-space constraints at small computational cost. In our experiments, we show that AdaScreen with multiple half-space constraints simultaneously improves screening performance and speeds up lasso solvers.

中文翻译：

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Lasso 筛选规则的集合。

为了解决大规模套索问题，已经开发了基于计算有效的筛选规则丢弃系数为零的特征的筛选算法。大多数现有的筛选规则是根据对偶最优解的球形约束和半空间约束开发的。然而，由于半空间产生的计算成本，现有规则最多允许两个半空间约束，即使附加约束可能有助于丢弃更多特征。在本文中，我们提出了 AdaScreen，一种自适应套索筛选规则集合，它允许将任何一个球体与多个半空间约束组合到对偶最优解上。由于几何考虑因素导致了 AdaScreen 的简单封闭形式解决方案，我们可以以较小的计算成本合并多个半空间约束。在我们的实验中，我们表明具有多个半空间约束的 AdaScreen 同时提高了筛选性能并加快了套索求解器的速度。