Analysis of different norms and corresponding lipschitz constants for global optimization

Abstract

The paper discusses how the used norm and corresponding Lipschitz constant influence the speed of algorithms for global optimization. For this reason Lipschitz constants corresponding to different norms were estimated. Different test functions for global optimization were solved using branch-and-bound algorithm for Lipschitz optimization with different norms. Experiments have shown that the best results are achieved when combination of extreme (infinite and first) and sometimes Euclidean norms is used.