On solving constrained optimization problems with neural networks

We analyze a class of neural networks that solve convex programming problems. In carrying out the analysis we use concepts from the theory of differential equations with discontinuous right-hand sides and Lyapunov stability theory. We show that irrespective of the initial state of the network the state converges to a solution of the convex programming problem. The dynamic behavior of the networks is illustrated by two numerical examples.<
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