A parallel neural network computing for the maximum clique problem

A novel computational model for large-scale maximum clique problems is proposed and tested. The maximum clique problem is first formulated as an unconstrained quadratic zero-one programming and it is solved by minimizing the weight summation over the same partition in a newly constructed graph. The proposed maximum neural network has the following advantages: (1) coefficient-parameter tuning in the motion equation is not required in the maximum neural network while the conventional neural networks suffer from it; (2) the equilibrium state of the maximum neural network is clearly defined in order to terminate the algorithm, while the existing neural networks do not have the clear definition; and (3) the maximum neural network always allows the state of the system to converge to the feasible solution, while the existing neural networks cannot guarantee it. The proposed parallel algorithm for large-size problems outperforms the best known algorithms in terms of computation time with much the same solution quality where the conventional branch-and-bound method cannot be used due to the exponentially increasing computation time.