  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.
