The purpose of this paper is to demonstrate that, for globally minimize one dimensional nonconvex problems with
both twice differentiable function and constraint, we can propose an efficient
algorithm based on Branch and Bound techniques. The method is first
displayed in the simple case with an interval constraint. The extension is
displayed
afterwards to the general case with an additional nonconvex twice
differentiable constraint. A quadratic bounding function which is better
than the well known linear underestimator is proposed while w-subdivision
is added to support the branching procedure. Computational results on several and
various types of functions show the efficiency of our algorithms and their
superiority with respect to the existing methods.