Optimization with Potts Neural Networks
in Complexity in Physics and Technology, eds. M.S. Garrido and R. Vilela Mendes (World Scientific, Singapore, 1992), 181-190
The Potts Neural Network approach to non-binary discrete optimization problems is described. It applies to problems that can be described as a set of elementary 'multiple choice' options. Instead of the conventional binary (Ising) neurons, mean field Potts neurons, having several available states, are used to describe the elementary degrees of freedom of such problems. The dynamics consists of iterating the mean field equations with annealing until convergence. Due to its deterministic character, the method is quite fast. When applied to problems of graph partition and scheduling types, it produces very good solutions also for problems of considerable size.
LU TP 92-1