Nonconvex fraction function recovery sparse signal by convex optimization algorithm
Abstract
In this paper, we will generate a convex iterative FP thresholding algorithm to solve the problem . Two schemes of convex iterative FP thresholding algorithms are generated. One is convex iterative FP thresholding algorithm-Scheme 1 and the other is convex iterative FP thresholding algorithm-Scheme 2. A global convergence theorem is proved for the convex iterative FP thresholding algorithm-Scheme 1. Under an adaptive rule, the convex iterative FP thresholding algorithm-Scheme 2 will be adaptive both for the choice of the regularized parameter and parameter . These are the advantages for our two schemes of convex iterative FP thresholding algorithm compared with our previous proposed two schemes of iterative FP thresholding algorithm. At last, we provide a series of numerical simulations to test the performance of the convex iterative FP thresholding algorithm-Scheme 2, and the simulation results show that our convex iterative FP thresholding algorithm-Scheme 2 performs very well in recovering a sparse signal.
Cite
@article{arxiv.1905.05436,
title = {Nonconvex fraction function recovery sparse signal by convex optimization algorithm},
author = {Angang Cui and Jigen Peng and Haiyang Li and Meng Wen},
journal= {arXiv preprint arXiv:1905.05436},
year = {2019}
}