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Sep, 2023
非凸惩罚稀疏量化回归的平滑ADMM算法
Smoothing ADMM for Sparse-Penalized Quantile Regression with Non-Convex Penalties
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Reza Mirzaeifard, Naveen K. D. Venkategowda, Vinay Chakravarthi Gogineni, Stefan Werner
TL;DR
研究了非凸非光滑稀疏惩罚条件下的分位数回归,介绍了交替方向乘法器方法中的新型单循环平滑 ADMM 算法,称为 SIAD 算法,可加快收敛速度并提供稀疏惩罚分位数回归的更快、更稳定的解。
Abstract
This paper investigates
quantile regression
in the presence of
non-convex
and
non-smooth sparse penalties
, such as the minimax concave pen
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