Relaxation labeling networks that solve the maximum clique problem

Relaxation labeling networks are a class of parallel distributed computational models extremely popular in computer vision and pattern recognition. Despite their original heuristic derivation, they possess in fact interesting dynamical properties and learning abilities, and exhibit also a certain biological plausibility. In this paper, it is shown how to take advantage of the properties of these models to solve the maximum clique problem, a well-known intractable optimization problem which has practical applications in various fields. The approach is based on a result by Motzkin and Straus which naturally leads to formulate the problem in a manner that is readily mapped onto a relaxation labeling network. Extensive simulations have practically demonstrated the validity of the proposed model