An inertial primal-dual fixed point algorithm for composite optimization problems
Abstract
We consider an inertial primal-dual fixed point algorithm (IPDFP) to compute the minimizations of the following Problem (1.1). This is a full splitting approach, in the sense that the nonsmooth functions are processed individually via their proximity operators. The convergence of the IPDFP is obtained by reformulating the Problem (1.1) to the sum of three convex functions. This work brings together and notably extends several classical splitting schemes, like the primaldual method proposed by Chambolle and Pock, and the recent proximity algorithms of Charles A. et al designed for the L1/TV image denoising model. The iterative algorithm is used for solving nondifferentiable convex optimization problems arising in image processing. The experimental results indicate that the proposed IPDFP iterative algorithm performs well with respect to state-of-the-art methods.
Cite
@article{arxiv.1604.05299,
title = {An inertial primal-dual fixed point algorithm for composite optimization problems},
author = {Meng Wen and Yu-Chao Tang and Jigen Peng},
journal= {arXiv preprint arXiv:1604.05299},
year = {2016}
}
Comments
arXiv admin note: text overlap with arXiv:1604.04845, arXiv:1604.04172; text overlap with arXiv:1403.3522, arXiv:1407.0898 by other authors