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Abstract : |
Finding approximate solutions to hard combinatorial optimization problems by neural networks is a very attractive prospect. Many empirical studies have been done in the area. However, recent research about a neural network model indicates that for any NP-hard problem the existance of a polynomial size network that solves it implies that NP=co-NP, which is contrary to the well-known conjecture that NP6=co-NP. This paper shows that even finding approximate solutions with guaranteed performance to some NP-hard problems by a polynomial size network is also impossible unless NP=co-NP., |
