  In this paper, a sequential search method for finding the global minimum of
an objective function is presented, The descent gradient search is repeated
until the global minimum is obtained. The global minimum is located by a
process of finding progressively better local minima. We determine the set of
points of intersection between the curve of the function and the horizontal
plane which contains the local minima previously found. Then, a point in this
set with the greatest descent slope is chosen to be a initial point for a new
descent gradient search. The method has the descent property and the
convergence is monotonic. To demonstrate the effectiveness of the proposed
sequential descent method, several non-convex multidimensional optimization
problems are solved. Numerical examples show that the global minimum can be
sought by the proposed method of sequential descent.
