ANALYSIS OF DIFFERENT NORMS AND CORRESPONDING LIPSCHITZ CONSTANTS FOR GLOBAL OPTIMIZATION IN MULTIDIMENSIONAL CASE

Abstract

The influence of used norm and corresponding Lipschitz constant to the speed of branch and bound algorithm for multidimensional global optimization has been investigated. Lipschitz constants of different test functions for global optimization corresponding to different norms have been estimated. The test functions have been optimized using branch and bound algorithm for Lipschitz optimization with different norms. Experiments have shown that the best results are achieved when the combination of extreme (infinite and first) and Euclidean norms is used.