A parameterized Douglas-Rachford Splitting algorithm for nonconvex optimization
Optimization and Control
2020-06-17 v2
Abstract
In this paper, we study a parameterized Douglas-Rachford splitting method for a class of nonconvex optimization problem. A new merit function is constructed to establish the convergence of the whole sequence generated by the parameterized Douglas-Rachford splitting method. We then apply the parameterized Douglas-Rachford splitting method to three important classes of nonconvex optimization problems arising in data science: sparsity constrained least squares problem, feasibility problem and low rank matrix completion. Numerical results validate the effectiveness of the parameterized Douglas-Rachford splitting method compared with some other classical methods.
Cite
@article{arxiv.1910.05544,
title = {A parameterized Douglas-Rachford Splitting algorithm for nonconvex optimization},
author = {Fengmiao Bian and Xiaoqun Zhang},
journal= {arXiv preprint arXiv:1910.05544},
year = {2020}
}
Comments
26 pages. submitted. Some changes have made