Improved Pathwise Coordinate Descent for Power Penalties
本文提出了一种改进的路径坐标下降算法，用于高效求解lq惩罚回归问题的完整解路径，相比冷启动算法速度更快，且能更可能达到更优解。
Pathwise coordinate descent algorithms have been used to compute entire solution paths for lasso and other penalized regression problems quickly with great success. They improve upon cold start algorithms by solving the problems that make up the solution path sequentially for an ordered set of tuning parameter values, instead of solving each problem separately. However, extending pathwise coordinate descent algorithms to more the general bridge or power family of lq penalties is challenging. Faster algorithms for computing solution paths for these penalties are needed because lq penalized regression problems can be nonconvex and especially burdensome to solve. In this article, we show that a reparameterization of lq penalized regression problems is more amenable to pathwise coordinate descent algorithms. This allows us to improve computation of the mode-thresholding function for lq penalized regression problems in practice and introduce two separate pathwise algorithms. We show that either pathwise algorithm is faster than the corresponding cold start alternative, and demonstrate that different pathwise algorithms may be more likely to reach better solutions. Supplemental materials for this article are available online.