Solving the L1 regularized least square problem via a box-constrained smooth minimization
Optimization and Control
2021-10-22 v3 Computer Vision and Pattern Recognition
Abstract
In this paper, an equivalent smooth minimization for the L1 regularized least square problem is proposed. The proposed problem is a convex box-constrained smooth minimization which allows applying fast optimization methods to find its solution. Further, it is investigated that the property "the dual of dual is primal" holds for the L1 regularized least square problem. A solver for the smooth problem is proposed, and its affinity to the proximal gradient is shown. Finally, the experiments on L1 and total variation regularized problems are performed, and the corresponding results are reported.
Cite
@article{arxiv.1704.03443,
title = {Solving the L1 regularized least square problem via a box-constrained smooth minimization},
author = {Majid Mohammadi and Wout Hofman and Yaohua Tan and S. Hamid Mousavi},
journal= {arXiv preprint arXiv:1704.03443},
year = {2021}
}
Comments
I stoped working on the paper and cannot guarantee its scientific correctness