Convergence Analysis of A Proximal Linearized ADMM Algorithm for Nonconvex Nonsmooth Optimization
Optimization and Control
2021-07-06 v2
Abstract
In this paper, we consider a proximal linearized alternating direction method of multipliers (PL-ADMM) for solving linearly constrained nonconvex and possibly nonsmooth optimization problems. The algorithm is generalized by using variable metric proximal terms in the primal updates and an over-relaxation stepsize in the multiplier update. We prove that the sequence generated by this method is bounded and its limit points are critical points. Under the powerful Kurdyka-{\L ojasiewicz} properties we prove that the sequence has a finite length thus converges, and we drive its convergence rates.
Cite
@article{arxiv.2009.05361,
title = {Convergence Analysis of A Proximal Linearized ADMM Algorithm for Nonconvex Nonsmooth Optimization},
author = {Maryam Yashtini},
journal= {arXiv preprint arXiv:2009.05361},
year = {2021}
}
Comments
arXiv admin note: text overlap with arXiv:2009.04014