Minimizing Differences of Convex Functions with Applications to Facility Location and Clustering

Authors

Nguyen Mau Nam, Portland State UniversityFollow
R. Blake Rector, Portland State UniversityFollow
Daniel Giles, Portland State UniversityFollow

Published In

Journal of Optimization Theory and Applications

Document Type

Citation

Publication Date

4-1-2017

Abstract

In this paper, we develop algorithms to solve generalized Fermat–Torricelli problems with both positive and negative weights and multifacility location problems involving distances generated by Minkowski gauges. We also introduce a new model of clustering based on squared distances to convex sets. Using the Nesterov smoothing technique and an algorithm for minimizing differences of convex functions introduced by Tao and An, we develop effective algorithms for solving these problems. We demonstrate the algorithms with a variety of numerical examples.

Locate the Document

http://dx.doi.org/10.1007/s10957-017-1075-6

DOI

10.1007/s10957-017-1075-6

Persistent Identifier

https://archives.pdx.edu/ds/psu/25852

Citation Details

Nam N.M., Rector R.B., Giles D. 2017. Minimizing Differences of Convex Functions with Applications to Facility Location and Clustering. Journal of Optimization Theory and Applications, 173(1):255-278.