Global optimization methods aim at finding the global optimum of a nonlinear and nonconvex function subject to nonlinear and nonconvex constraints. The main approaches to global optimization, i.e., branch-and-bound, Lipschitz underestimation, outer approximation, polyhedral annexation, linearization and decomposition are briefly reviewed and illustrated through their application to a series of extensions of Weber's problem.