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arXiv · 2009.05361

Convergence Analysis of A Proximal Linearized ADMM Algorithm for Nonconvex Nonsmooth Optimization

Maryam Yashtini
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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.

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Maryam Yashtini. 2020-09-08. Convergence Analysis of A Proximal Linearized ADMM Algorithm for Nonconvex Nonsmooth Optimization. https://arxiv.org/abs/2009.05361

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