The algorithm does not require differentiability. For differentiable problems, however, improvements in efficiency are achieved using interval Newton methods, monotonicity tests, and convexity tests. For further improvements in efficiency, the algorithm is parallelized . The parallelization task is accomplished by distributing processes over a network of workstations. Each process performs the interval global optimization algorithm but with a different subregion of the initial search space.
Communication overhead is minimized in order to maximize the speedup.
Issues of distributed initialization, load balancing, and global termination detection are addressed. Finally, an analysis of the speedup is determined.