Computational comparison of two methods for constrained global optimization |
| |
Authors: | A. T. Phillips J. B. Rosen |
| |
Affiliation: | (1) Computer Science Department, United States Naval Academy, 21402 Annapolis, MD, USA;(2) Computer Science Department, University of Minnesota, 55455 Minneapolis, MN, USA |
| |
Abstract: | For constrained concave global minimization problems, two very different solution techniques have been investigated. The first such method is a stochastic mulitstart approach which typically finds, with high probability, all local minima for the problem. The second method is deterministic and guarantees a global minimum solution to within any user specified tolerance. It is the purpose of this paper to make a careful comparison of these two methods on a range of test problems using separable concave objectives over compact polyhedral sets, and to investigate in this way the advantages and disadvantages of each method. A direct computational comparison, on the same set of over 140 problems, is presented. |
| |
Keywords: | Global optimization stochastic methods deterministic methods |
本文献已被 SpringerLink 等数据库收录! |
|