Convergence Analysis of a Proximal Point Algorithm for Minimizing Differences of Functions
Optimization and Control
2015-06-29 v4
Abstract
Several optimization schemes have been known for convex optimization problems. However, numerical algorithms for solving nonconvex optimization problems are still underdeveloped. A progress to go beyond convexity was made by considering the class of functions representable as differences of convex functions. In this paper, we introduce a generalized proximal point algorithm to minimize the difference of a nonconvex function and a convex function. We also study convergence results of this algorithm under the main assumption that the objective function satisfies the Kurdyka - \L ojasiewicz property.
Cite
@article{arxiv.1504.08079,
title = {Convergence Analysis of a Proximal Point Algorithm for Minimizing Differences of Functions},
author = {Nguyen Thai An and Nguyen Mau Nam},
journal= {arXiv preprint arXiv:1504.08079},
year = {2015}
}