Mixed parallel execution of algorithms for satisfiability problem

被引：0
|
作者：
Zhang, KR [1 ]
Nagamatu, M [1 ]
机构：
[1] Kyushu Inst Technol, Grad Sch Life Sci & Syst Engn, Kitakyushu, Fukuoka 804, Japan
来源：
INTELLIGENT INFORMATION PROCESSING II | 2005年 / 163卷
关键词：
satisfiability problem; parallel execution; neural network; Lagrangian method;
D O I：
10.1007/0-387-23152-8_35
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
LPPH has been proposed to solve the satisfiability problem (SAT). In order to solve the SAT more efficiently, a parallel execution has been proposed. Experimental results show that higher speedup ratio is obtained by using this parallel execution of the LPPH. In this paper, we propose a method of mixed parallel execution of several algorithms for the SAT. "Mixed" means the parallel execution of the LPPH and local search algorithms. In the experiments, we used the LPPH with attenuation coefficient generating function and the GSAT. Results of experiments show mixing these two algorithms yield excellent performance.
引用
收藏
页码：273 / 277
页数：5
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