Solving SATetworks with probabilistic attenuation coefficient generator
Kairong Zhang, Masahiro Nagamatu · 2005
We have proposed a neural network named LPPH for the SAT. In order to solve the SAT more efficiently, a parallel execution has been proposed. Experimental results show that higher ratio of speedup is obtained by using this parallel execution of the LPPH. There is an important parameter named attenuation coefficient in the dynamics of the LPPH, which affects strongly the speed of execution of the LPPH. In this paper, a method is proposed to generate attenuation coefficients for the dynamics of the LPPH by using probabilistic generating function. The experimental results show that this method is efficient.