Published In
Optimization: A Journal of Mathematical Programming and Operations Research
Document Type
Post-Print
Publication Date
2017
Subjects
Convex programming, Optimization theory and applications, Nonconvex programming
Abstract
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.
DOI
10.1080/02331934.2016.1253694
Persistent Identifier
http://archives.pdx.edu/ds/psu/19364
Citation Details
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.
http://archives.pdx.edu/ds/psu/19364
Description
This is the authors' version of a manuscript which was published online Nov. 24, 2016 in: Optimization: A Journal of Mathematical Programming and Operations Research.The final publication is available at Taylor & Francis via: https://dx.doi.org/10.1080/02331934.2016.1253694