Subgradient Algorithm, Stochastic Subgradient Algorithm, Incremental Subgradient Algorithm, and Set Location Problems
Abstract
In recent years, important progress has been made in applying methods and techniques of convex optimization to many fields of applications such as location science, engineering, computational statistics, and computer science. In this paper, we present some simple algorithms for solving a number of new models of facility location involving sets which generalize the classical Fermat-Torricelli problem and the smallest enclosing circle problem. The general nondifferentiability of the models prevents us from applying gradient-type algorithms, so our approach is to use subgradient-type algorithms instead. The algorithms presented in this paper are easy to implement and applicable for a broad range of problems that are also suitable for teaching and learning convex optimization.
Cite
@article{arxiv.1303.3735,
title = {Subgradient Algorithm, Stochastic Subgradient Algorithm, Incremental Subgradient Algorithm, and Set Location Problems},
author = {Nguyen Mau Nam and Nguyen Thai An and Han Le},
journal= {arXiv preprint arXiv:1303.3735},
year = {2013}
}
Comments
The results in this manuscript has been incorporated to another paper