Coordinate Descent for MCP/SCAD Penalized Least Squares Converges Linearly
Machine Learning
2021-09-21 v1 Machine Learning
Computation
Abstract
Recovering sparse signals from observed data is an important topic in signal/imaging processing, statistics and machine learning. Nonconvex penalized least squares have been attracted a lot of attentions since they enjoy nice statistical properties. Computationally, coordinate descent (CD) is a workhorse for minimizing the nonconvex penalized least squares criterion due to its simplicity and scalability. In this work, we prove the linear convergence rate to CD for solving MCP/SCAD penalized least squares problems.
Keywords
Cite
@article{arxiv.2109.08850,
title = {Coordinate Descent for MCP/SCAD Penalized Least Squares Converges Linearly},
author = {Yuling Jiao and Dingwei Li and Min Liu and Xiliang Lu},
journal= {arXiv preprint arXiv:2109.08850},
year = {2021}
}