We propose a new fast algorithm to estimate any sparse generalized linear model with convex or non-convex separable penalties. Our algorithm is able to solve problems with millions of samples and features in seconds, by relying on coordinate descent, working sets and Anderson acceleration. It handles previously unaddressed models, and is extensively shown to improve state-of-art algorithms. We provide a flexible, scikit-learn compatible package, which easily handles customized datafits and penalties.

翻译：我们提出了一种新快速算法，用于估计任意带有凸或非凸可分离惩罚项的稀疏广义线性模型。该算法通过结合坐标下降法、工作集策略和安德森加速技术，能够在数秒内处理包含数百万样本和特征的问题。它能够处理此前未被解决的模型类型，并广泛证明优于现有最优算法。我们提供了一个灵活的、兼容scikit-learn的工具包，可轻松实现自定义数据拟合函数与惩罚项。