Deterministic methods in constrained global optimization: Some recent advances and new fields of application

Recent developments in deterministic global optimization methods have considerably enlarged the fields of optimization where those methods can be successfully applied. It is the purpose of the present article to give a brief survey of both some of the most promising methods and new fields of application. The methods considered comprise branch and bound and outer approximation as well as combinations of branch and bound with outer approximation. The fields of applications to be discussed include concave minimization, reverse convex programming, d.c. programming, Lipschitzian optimization, systems of equations, and (or) inequalities and global integer programming.