On the Power of Neural Networks for Solving Hard Problems

Part of Neural Information Processing Systems 0 (NIPS 1987)

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Jehoshua Bruck, Joseph Goodman


This paper deals with a neural network model in which each neuron performs a threshold logic function. An important property of the model is that it always converges to a stable state when operating in a serial mode [2,5]. This property is the basis of the potential applications of the model such as associative memory devices and combinatorial optimization [3,6]. One of the motivations for use of the model for solving hard combinatorial problems is the fact that it can be implemented by optical devices and thus operate at a higher speed than conventional electronics. The main theme in this work is to investigate the power of the model for solving NP-hard problems [4,8], and to understand the relation between speed of operation and the size of a neural network. In particular, it will be shown that for any NP-hard problem the existence of a polynomial size network that solves it implies that NP=co-NP. Also, for Traveling Salesman Problem (TSP), even a polynomial size network that gets an €-approximate solution does not exist unless P=NP.

The above results are of great practical interest, because right now it is possible to build neural networks which will operate fast but are limited in the number of neurons.