An Effective Algorithm for the Minimum Set Cover Problem

In this paper, a learning algorithm of the Hopfield neural network, which can escape from local minimum, is proposed. The learning algorithm adjusts the balance between constraint term and cost term of the energy function so that the local minimum that the network once falls into vanishes and the network can continue updating in a gradient descent direction of energy. Approximation performance is experimentally determined on random instances of hypergraphs by comparing it to several known algorithms. The experimental results show that the proposed algorithm works much better than the existing algorithms for the problem