Performance Comparison and Analysis of Proximal Gradient and ADMM for Solving LASSO
The regularized models play an important role in a lot of fields, such as: machine learning, compressing sensing, recommending system, and so on. With the ability of variable selection and generating sparse solution, the regularized models can avoid over-fitting. They may also be applied to signal reconstruction and matrix completion. Introduces the regularized models, and analyzes two recently developed al-gorithms:proximal gradient and ADMM, compares the performances on solving LASSO.
NETL
NSTL
万方数据
维普
