Stability of linear programming neural network for problems with hypercube feasible region

Presents an analysis of the stability properties of a linear-programming neural network used for minimizing a linear cost function under the constraint that the feasible region is a hypercube in Rⁿ, i.e. [0,1]ⁿ. A properly designed neural network structure can possess both the Lagrangian function method and the penalty function method concepts. The equilibrium of the network is asymptotically stable and is in the neighborhood of a corner of the hypercube, where the corner is the point minimizing the cost function. The equilibrium point of the network can be made arbitrarily close to the minimizer of the cost function by varying a factor introduced into the network