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.

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

DOI

10.1080/02331934.2016.1253694

Persistent Identifier

http://archives.pdx.edu/ds/psu/19364

Included in

Analysis Commons

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