"Optimal" neural representation for combinatorial optimization with linear cost function
S. Matsuda · 2002
Taking assignment problems as examples of combinatorial optimization problems with linear cost function, we present an "optimal" neural representation for these problems. It is proved that a vertex of this network state hypercube is asymptotically stable iff it is an optimal solution to the problem. One can always obtain an optimal solution whenever the network converges to a vertex. We can also design such "optimal" neural representations for many combinatorial optimization problems with linear cost function, as well as for assignment problems.