Efficient Minimal Model Generation Using Branching Lemmas
Efficient Minimal Model Generation Using Branching Lemmas
复制标题
使用分支引理生成高效的最小模型
DOI：
10.1007/10721959_15
复制
发表时间：
2000
期刊：
影响因子：
--
通讯作者：
Miyuki Koshimura
中科院分区：
文献类型：
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作者：
R. Hasegawa;H. Fujita;Miyuki Koshimura
提出了一种有效的最小模型生成方法。该方法采用分支假设和引理来修剪导致非最小模型的分支，并减少对所获得模型的最小性测试。该方法适用于其他方法，例如Bry的补体分裂和约束搜索或Niemela的接地测试，并大大提高了它们的效率。我们基于该方法实现了MM-MGTP。 MM-MGTP 的实验结果表明，与 MM-SATCHMO 相比，速度显着提高。
An efficient method for minimal model generation is presented. The method employs branching assumptions and lemmas so as to prune branches that lead to nonminimal models, and to reduce minimality tests on obtained models. This method is applicable to other approaches such as Bry’s complement splitting and constrained search or Niemela’s groundedness test, and greatly improves their efficiency. We implemented MM-MGTP based on the method. Experimental results with MM-MGTP show a remarkable speedup compared to MM-SATCHMO.