On the convergence of a randomized block coordinate descent algorithm for a matrix least squares problem

2022 ◽  
Vol 124 ◽  
pp. 107689
Author(s):  
Kui Du ◽  
Cheng-Chao Ruan ◽  
Xiao-Hui Sun
Keyword(s):  
Least Squares ◽  
Coordinate Descent ◽  
Block Coordinate Descent ◽  
Descent Algorithm ◽  
Least Squares Problem ◽  
Coordinate Descent Algorithm

scholarly journals The Sparse Group Log Ridge for the Selection of Variable Groups

2021 ◽  
Vol 2078 (1) ◽  
pp. 012012
Author(s):  
Song Yao ◽  
Lipeng Cui ◽  
Sining Ma
Keyword(s):  
Artificial Intelligence ◽  
Group Structure ◽  
Coordinate Descent ◽  
Block Coordinate Descent ◽  
Descent Algorithm ◽  
Mm Algorithm ◽  
Sparse Model ◽  
Coordinate Descent Algorithm ◽  
Variables Selection ◽  
Selection Of

Abstract In recent years, the sparse model is a research hotspot in the field of artificial intelligence. Since the Lasso model ignores the group structure among variables, and can only achieve the selection of scattered variables. Besides, Group Lasso can only select groups of variables. To address this problem, the Sparse Group Log Ridge model is proposed, which can select both groups of variables and variables in one group. Then the MM algorithm combined with the block coordinate descent algorithm can be used for solving. Finally, the advantages of the model in terms of variables selection and prediction are shown through the experiment.

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