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arXiv · 2109.08850

Coordinate Descent for MCP/SCAD Penalized Least Squares Converges Linearly

Yuling Jiao·Dingwei Li·Min Liu·Xiliang Lu
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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.

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Yuling Jiao, Dingwei Li, Min Liu, Xiliang Lu. 2021-09-18. Coordinate Descent for MCP/SCAD Penalized Least Squares Converges Linearly. https://arxiv.org/abs/2109.08850

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