On 01/12/2012 04:21 PM, Ivan Oseledets wrote:

Dear all!

I quite new to numpy and python. I am a matlab user, my work is mainly on multidimensional arrays, and I have a question on the svd function from numpy.linalg

It seems that

u,s,v=svd(a,full_matrices=False)

returns u and v in the F-contiguous format.

The reason for this is that the underlying computational routine is in Fortran (when using system lapack library, for instance) that requires and returns F-contiguous arrays and the current behaviour guarantees the most memory efficient computation of svd.

That is not in a good agreement with other numpy stuff, where C-ordering is default. For example, matrix multiplication, dot() ignores ordering and returns result always in C-ordering. (which is documented), but the svd feature is not documented.

In generic numpy operation, the particular ordering of arrays should not matter as the underlying code should know how to compute array operation results from different input orderings efficiently. This behaviour of svd should be documented. However, one should check that when using the svd from numpy lapack_lite (which is f2c code and could use also C-ordering, in principle), F-contiguous arrays are actually returned. Regards, Pearu