The Minimum Covering l_pb-Hypersphere Problem
Computational Optimization and Applications
Voronoi diagrams in the Moscow Metric
Voronoi diagrams in the Moscow Metric
Using Interval Analysis for Solving Planar Single-Facility Location Problems: New Discarding Tests
Journal of Global Optimization
The 1-center problem in the plane with independent random weights
Computers and Operations Research
Computers and Operations Research
Solving the multiple competitive facilities location and design problem on the plane
Evolutionary Computation
Computational Optimization and Applications
Upgradeability and predictability analysis for mesh topologies in optical distribution networks
WOCN'09 Proceedings of the Sixth international conference on Wireless and Optical Communications Networks
Newton’s method for the ellipsoidal lp norm facility location problem
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
The ellipsoidal lp norm obnoxious facility location problem
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
Hi-index | 0.00 |
The aim of this paper is two-fold. First, the weighted lp-norm, which has proved to be an accurate distance predicting function and has been proposed by several authors as the most suitable predictor of distances, is compared through an empirical study with the l2b-norm, a function with the same number of parameters as the first one. The results show that neither distance function dominates the other. On the contrary, depending on the region considered either norm may be significantly better than the other. The second aim is to investigate how the selection of the data set representing the network of the region affects the ability of the distance predicting function for predicting distances, and to try to deduce how to obtain a suitable data set which adequately represents a given geographical region. Through another empirical study it is shown that the selection of the data set dramatically affects the accuracy of the predictions. To obtain a suitable data set it is important to choose a good sample size, and more importantly, the cities should be chosen so that they are distributed all over the region and represent the density of the cities in the region.