Name
Abstract | ||
|---|---|---|
The satisfiability problem (SAT) of propositional calculus is one of important problems in the field of the information science. We proposed a neural network called LPPH for the SAT. The LPPH is based on the Lagrangian method and it is proved that every equilibrium point of the dynamics is a solution of the SAT and vice versa. Hence it is never trapped by any point which is not the solution of the SAT. From experiments it is known that the LPPH outperforms already proposed combinatorial algorithms even if it is executed by numerical simulations on conventional computers. The SAT is a very difficult problem in general and it needs a huge amount of execution time to solve when the size of the problem becomes large. In this paper we propose a method which executes multiple simulations of the LPPH for more than one initial value assignment in parallel. It is shown by experiments that high effectiveness of the parallelization is obtained with low overhead. |
Year | Venue | Keywords |
|---|---|---|
1998 | ICONIP'98: THE FIFTH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING JOINTLY WITH JNNS'98: THE 1998 ANNUAL CONFERENCE OF THE JAPANESE NEURAL NETWORK SOCIETY - PROCEEDINGS, VOLS 1-3 | neurocomputing, propositional calculus, satisfiability problem, combinatorial problem, Lagrangian method |
Field | DocType | Citations |
Computer science,Theoretical computer science,State space search,Artificial intelligence,Artificial neural network,Machine learning | Conference | 1 |
PageRank | References | Authors |
0.41 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masahiro Nagamatu | 1 | 5 | 4.64 |
| T. Yanaru | 2 | 16 | 6.03 |