SparseNet: Coordinate Descent With Nonconvex Penalties
研究了线性模型中稀疏选择问题的非凸惩罚方法，采用坐标下降算法进行优化，分析了收敛性，并提出了适用于路径算法的标准化重参数化方法，以MC+惩罚为例展示了算法性能。
We address the problem of sparse selection in linear models. A number of nonconvex penalties have been proposed in the literature for this purpose, along with a variety of convex-relaxation algorithms for finding good solutions. In this article we pursue a coordinate-descent approach for optimization, and study its convergence properties. We characterize the properties of penalties suitable for this approach, study their corresponding threshold functions, and describe a df-standardizing reparametrization that assists our pathwise algorithm. The MC+ penalty is ideally suited to this task, and we use it to demonstrate the performance of our algorithm. Certain technical derivations and experiments related to this article are included in the Supplementary Materials section.