Simulations for Efficient Combination of Two Lower Bound Functions in Univariate Global Optimization.

We propose a new method for solving univariate global optimization problems by combining a lower bound function given in (αBB) method [1] [For more details (Algorithms,Methods) one see [1]], with the improved lower bound function of the method developed in [2]. The new lower bound function is better than the two lower bound functions by its construction. The complementarity of the two lower bound functions allows us to derive the convex/concave test and the pruning step which accelerate the convergence of the proposed method. Illustrative examples are treated efficiently. [ABSTRACT FROM AUTHOR]