Global optimization using diffusion perturbations with large noise intensity
The authors determine an algorithm for global optimization of gradient ascent type and use random perturbations. It shows by properly varying the noise intensity, approximations of the global maximum can be achieved. It builds algorithms in discrete-time, recursive algorithms with occasional perturbations involving large noise intensity.
scientific article; zbMATH DE number 5119461
scientific article