Efficient primal-dual fixed point algorithm with dynamic stepsize for convex problems with applications to imaging restoration
Optimization and Control
2016-04-19 v1
Abstract
We consider the problem of finding the minimization of the sum of a convex function and the composition of another convex function with a continuous linear operator from the view of fixed point algorithms based on proximity operators. We design a primal-dual fixed point algorithm with dynamic stepsize based on the proximity operator and obtain a scheme with a closed form solution for each iteration. Based on Modified Mann iteration and the firmly nonexpansive properties of the proximity operator, we achieve the convergence of the proposed algorithm.
Cite
@article{arxiv.1604.04852,
title = {Efficient primal-dual fixed point algorithm with dynamic stepsize for convex problems with applications to imaging restoration},
author = {Meng Wen and Shigang Yue and Yuchao Tang and Jigen Peng},
journal= {arXiv preprint arXiv:1604.04852},
year = {2016}
}
Comments
arXiv admin note: text overlap with arXiv:1104.1436 by other authors