Paper Info
Title
Is the 1-norm the best convex sparse regularization?
Abstract
The 1-norm is a good convex regularization for the recovery of sparse vectors from under-determined linear measurements. No other convex regularization seems to surpass its sparse recovery performance. How can this be explained? To answer this question, we define several notions of best (convex) regulariza-tion in the context of general low-dimensional recovery and show that indeed the 1-norm is an optimal convex sparse regularization within this framework.
Year
Venue
Field
2018
arXiv: Information Theory
Mathematical optimization,Norm (social),Regular polygon,Regularization (mathematics),Mathematics
DocType
Volume
Citations 
Journal
abs/1806.08690
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Yann Traonmilin1263.22
Samuel Vaiter2508.39
Rémi Gribonval3120783.59