Many important classes of decision models give rise to the problem of finding a global minimum of a concave function over a convex set. Since such a function may have many local minima, finding the global minimum is a computationally difficult problem, where standard nonlinear programming procedures fail. The two proposed methods are simple and quick, using the largest inscribed ball and the minimal enclosing box as approximation for cutting-plane method.
