论文信息 - Theoretical study on mixed parallel execution for solving SAT

Theoretical study on mixed parallel execution for solving SAT

Abstract. For the satisfiability problem (SAT), we have proposed a neural network, called LPPH, and a parallelization method, called “mixed parallel execution,” in which LPPH and other algorithms are executed simultaneously. In this paper we study the CPU time of the mixed parallel execution, and prove that the bigger the difference between distribution functions of CPU time of the algorithms used in the parallel execution, the more effective the mixed parallel execution is.

Masahiro Nagamatu | Kairong Zhang

[1] Masahiro Nagamatu,et al. Parallel State Space Search for SAT with Lagrange Programming Neural Network , 1998, ICONIP.

[2] Masahiro Nagamatu,et al. On the stability of Lagrange programming neural networks for satisfiability problems of propositional calculus , 1996, Neurocomputing.

[3] Hector J. Levesque,et al. A New Method for Solving Hard Satisfiability Problems , 1992, AAAI.