Scaling up Greedy Equivalence Search for Continuous Variables
Scaling up Greedy Equivalence Search for Continuous Variables
复制标题
扩大连续变量的贪婪等价搜索
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
J. Ramsey
中科院分区：
文献类型：
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作者：
J. Ramsey
作为R或Tetrad程序的标准实现，在科学问题中应用最广泛或最有效的因果搜索算法具有严格的维度约束。然而，实现改进是可能的，它将搜索问题的可行维度扩展了几个数量级。我们描述了贪婪等价搜索(GES)的优化，它允许在4处理器8G膝上型计算机上，在13分钟内搜索具有1000个样本的稀疏模型的50,000个变量问题，并且在匹兹堡超级计算中心具有40个处理器和384G RAM的超级计算机节点上，根据生成的I.D.数据，在18小时内搜索具有1000个样本的稀疏模型。从一个线性的高斯模型。
As standardly implemented in R or in the Tetrad program, causal search algorithms that have been most widely or effectively used in scientific problems have severe dimensionality constraints. However, implementation improvements are possible that extend the feasible dimensionality of search problems by several orders of magnitude. We describe optimizations for the Greedy Equivalence Search (GES) that allow search on 50,000 variable problems in 13 minutes for sparse models with 1000 samples, on a 4 processor 8G laptop computer, and in 18 hours for sparse models with 1000 samples on 1,000,000 variables on a supercomputer node at the Pittsburgh Supercomputing Center with 40 processors and 384 G RAM, on data generated i.i.d. from a linear, Gaussian model.