Smoothing ADMM for Sparse-Penalized Quantile Regression With Non-Convex Penalties
Reza Mirzaeifard,
Naveen K. D. Venkategowda,
Vinay Chakravarthi Gogineni
et al.
Abstract:This paper investigates quantile regression in the presence of non-convex and non-smooth sparse penalties, such as the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD). The non-smooth and non-convex nature of these problems often leads to convergence difficulties for many algorithms. While iterative techniques such as coordinate descent and local linear approximation can facilitate convergence, the process is often slow. This sluggish pace is primarily due to the need to run these a… Show more
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