在协方差矩阵上计算SVD时的奇怪行为:Microsoft R和Vanilla R之间的结果不同

问题描述

当我得到一些奇怪的结果时，我正在对运行Microsoft R 3.3.0的Macbook进行一些主成分分析.经过与同事的双重检查，我已经意识到SVD函数的输出与使用Vanilla R可能获得的输出有所不同.

I was doing some principal component analysis on my macbook running Microsoft R 3.3.0 when I got some strange results. Double checking with a colleague, I've realised that the output of the SVD function was different from what I may get by using vanilla R.

这是可重复的结果，请在此处>> sv <- svd(Cx) >> print(sv$d[1:10]) [1] 122.73664 104.45759 90.52001 87.21890 81.28256 74.33418 73.29427 66.26472 63.51379 [10] 55.20763

而不是在普通R上(在两个不同的Linux机器上都具有R 3.3和R 3.3.1):

Instead on a vanilla R (both with R 3.3 and R 3.3.1 on two different linux machines):

>> sv <- svd(Cx)
>> print(sv$d[1:10])

 [1] 122.73664  34.67177  18.50610  14.04483   8.35690   6.80784   6.14566
 [8]   3.91788   3.76016   2.66381

并非所有数据都发生这种情况，如果我创建一些随机矩阵并对之应用svd，则会得到相同的结果.因此，看起来像是一种数值不稳定，不是吗?

This is not happening with all the data, if I create some random matrix and I apply svd on that, I get the same results. So, it looks like a sort of numerical instability, isn't it?

更新:我尝试使用svd包在具有相同R版本的同一台计算机(macbook)上的同一矩阵(Cx)上计算SVD，最后我得到了正确的"结果数字.然后，这似乎是由于Microsoft R Open使用的svd实现.

UPDATE: I've tried to compute the SVD on the same matrix (Cx) on the same machine (macbook) with the same version of R by using the svd package and finally I get the "right" numbers. Then it seems due to the svd implementation used by Microsoft R Open.

更新:该行为也在MRO 3.3.1上发生

UPDATE: The behaviour happens also on MRO 3.3.1

推荐答案

看来，这是一种错误，已确认在Microsoft-r-open的Github中.他们说这种行为正在调查中，并且与MacOs中的Accelerate库有关.

It seems this is a sort of bug, as confirmed in the Github of microsoft-r-open. They say this behaviour is under investigation and it's related with the Accelerate library in MacOs.

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