Polyhedral annexaton,dualization and dimension reduction technique in global optimization

We demonstrate how the size of certain global optimization problems can substantially be reduced by using dualization and polyhedral annexation techniques. The results are applied to develop efficient algorithms for solving concave minimization problems with a low degree of nonlinearity. This class includes in particular nonconvex optimization problems involving products or quotients of affine functions in the objective function.This work was completed while the author was visiting the Department of Mathematics of Linköping University.