Sequences of Discrete Hopfield’s Networks for the Maximum Clique Problem
We propose here a neural approximation technique for the Maximum Clique problem. The core of the method consists of a sequence of Hopfield’s networks that, in polynomial time, converge to a state representing a clique for a given graph. Some experiments made on the DIMACS benchmark show that the approximated solutions found are promising. Finally, the possibility to extend this technique to other NP-hard problems and to implement it onto neural hardware are discussed.
KeywordsMaximum Clique problem constrained optimization Hopfield’s networks
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