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Coordinate Descent for MCP/SCAD Penalized Least Squares Converges Linearly

arXiv 2109.08850 ↗First posted 18 Sep 2021stat.ML cs.LG stat.COcited by 0

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Abstract (arXiv)

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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  1. 2016Calculus of the exponent of Kurdyka-Lojasiewicz inequality and its applications to linear convergence of first-order methodscited by 35

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