函数这样用的： svd(x, nu = min(n, p), nv = min(n, p), LINPACK = FALSE)
nu和nv分别代表：
nu: the number of left singular vectors to be computed. This
must between '0' and 'n = nrow(x)'.
nv: the number of right singular vectors to be computed. This
must be between '0' and 'p = ncol(x)'.
不明白的是下面的说明：
Computing the singular vectors is the slow part for large
matrices. The computation will be more efficient if 'nu <= min(n,
p)' and 'nv <= min(n, p)', and even more efficient if one or both
are zero.
为什么nu <= min(n,p)' and 'nv <= min(n, p)'以及one or both are zero的时候效率更高？
如果nu=0代表什么？怎么理解：the number of (left) singular vectors to be computed.
