Lagrange programming neural networks

A class of neural networks appropriate for general nonlinear programming, i.e., problems including both equality and inequality constraints, is analyzed in detail. The methodology is based on the Lagrange multiplier theory in optimization and seeks to provide solutions satisfying the necessary conditions of optimality. The equilibrium point of the network satisfies the Kuhn-Tucker condition for the problem. No explicit restriction is imposed on the form of the cost function apart from some general regularity and convexity conditions. The stability of the neural networks is analyzed in detail. The transient behavior of the network is simulated and the validity of the approach is verified for a practical problem, maximum entropy image restoration.<
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