Optimization: A Journal of Mathematical Programming and Operations Research
Convex programming, Optimization theory and applications, Nonconvex programming
Several optimization schemes have been known for convex optimization problems. However, numerical algorithms for solving nonconvex optimization problems are still underdeveloped. A significant 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– ᴌojasiewicz property.
Nguyen, Thai An and Nguyen, Mau Nam, "Convergence Analysis of a Proximal Point Algorithm for Minimizing Differences of Functions" (2017). Mathematics and Statistics Faculty Publications and Presentations. 167.