  A new technique of global optimization and its applications in particular to
neural networks are presented. The algorithm is also compared to other global
optimization algorithms such as Gradient descent (GD), Monte Carlo (MC),
Genetic Algorithm (GA) and other commercial packages. This new optimization
technique proved itself worthy of further study after observing its accuracy of
convergence, speed of convergence and ease of use. Some of the advantages of
this new optimization technique are listed below: 1. Optimizing function does
not have to be continuous or differentiable. 2. No random mechanism is used,
therefore this algorithm does not inherit the slow speed of random searches. 3.
There are no fine-tuning parameters (such as the step rate of G.D. or
temperature of S.A.) needed for this technique. 4. This algorithm can be
implemented on parallel computers so that there is little increase in
computation time (compared to linear increase) as the number of dimensions
increases. The time complexity of O(n) is achieved.
