An Accelerated Successive Convex Approximation Scheme With Exact Step Sizes for L1-Regression
We consider the minimization of <inline-formula><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula>-regularized least-squares problems. A recent optimization approach uses successive convex approximations with an exact line search, which is highly com...
Saved in:
Main Authors: | Lukas Schynol, Moritz Hemsing, Marius Pesavento |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Open Journal of Signal Processing |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10840211/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Sparse Deep Nonnegative Matrix Factorization
by: Zhenxing Guo, et al.
Published: (2020-03-01) -
Function approximation method based on weights gradient descent in reinforcement learning
by: Xiaoyan QIN, et al.
Published: (2023-08-01) -
RELIABILITY ASSESSMENT MODEL BASED ON RELATIVE ENTROPY INFORMATION FUSION WITH DOUBLE CONSTANT ACCELERATING STRESSES
by: YIN ZeKai, et al.
Published: (2021-01-01) -
Testing the Stability of Regression Parameters When Some Additional Data Sets Are Available
by: R. Radhakrishnan, et al.
Published: (1994-01-01) -
Localization algorithm based on support vector regression for wirless sensor networks
by: WEI Ye-hua1, et al.
Published: (2009-01-01)