Optimization by neural networks

The ability to map and solve a number of interesting problems on neural networks motivates a proposal for using neural networks as a highly parallel model for general-purpose computing. The author review this proposal, showing how to map combinational optimization problems, including graph K-partitioning, vertex cover, maximum independent set, maximum clique, number partitioning, and maximum matching. They report that performance results are quite encouraging; the solutions for graph partitioning and task allocation problems are comparable to those obtained using heuristics and the running times are significantly lower than those required using simulated annealing.<>