An efficient method for parallel interval global optimization
Adam Baldwin, Asai Asaithambi · 2011
Finding the global minimum for an arbitrary differentiable function over an n-dimensional rectangle is an important problem in computational science, with applications in many disciplines. We present a parallel depth-first algorithm along with a potential load balancing technique, and acceleration devices that provides a significant reduction in run time compared with a popular breadth-first search algorithm. Our algorithm reliably obtains global minima for test functions commonly used in the literature, with the highest speedup achieved for highly multimodal functions.