Neural networks for the set covering problem: an application to the test vector compaction

A new neural network architecture is proposed and evaluated for the set covering problem. The network is designed by using the connections between nonlinear and integer programming problems. This connection is based on the concavity and penalty function methods. The general objective function obtained, which combines the objective function and constraints, is fixed as the energy of the system. The network obtained was then applied to a practical problem in VLSI systems. The simulation results for the network show that the system can converge rapidly within a few neural time constants even for large scale problems