“Optimal” Hopfield network for combinatorial optimization with linear cost function

Authors:
S. Matsuda
Affiliations:
Comput. & Commun. Res. Center, Tokyo Electr. Power Co. Inc.
Venue:
IEEE Transactions on Neural Networks
Year:
1998

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Abstract

An “optimal” Hopfield network is presented for combinatorial optimization problems with linear cost function. It is proved that a vertex of the network state hypercube is asymptotically stable if and only if it is an optimal solution to the problem. That is, one can always obtain an optimal solution whenever the network converges to a vertex. In this sense, this network can be called the “optimal” Hopfield network. It is also shown through simulations of assignment problems that this network obtains optimal or nearly optimal solutions more frequently than other familiar Hopfield networks