smritibhagat@gmail.com wrote:[color=blue]
> Hi Robert!
> Oh! Its not a homework problem...
> I read the Golub book, it tells me what an orthogonal complement is,
> however, I cannot understand how I can code it.
> I know about svd from numpy's mlab, but I what I want to know is how
> can I compute an orthogonal complement, using SVD or otherwise.[/color]
Assuming A is an array with the vectors as columns and has shape (m, n), then
the null space of A (= the orthogonal complement of the vectors assuming that
the set of vectors is linearly independent):
In [231]: A
Out[231]:
array([[ 0., 1.],
[ 1., 1.],
[ 2., 1.],
[ 3., 1.]])
In [232]: m, n = A.shape
In [233]: u, s, vh = numpy.linalg.svd(A)
In [234]: dot(transpose(u[:, n:]), A)
Out[234]:
array([[ 0.00000000e+00, -1.11022302e-16],
[ -1.42247325e-16, -5.65519853e-16]])
In [235]: ortho_complement = u[:, n:]
In [236]: ortho_complement
Out[236]:
array([[-0.38578674, -0.38880405],
[ 0.22458489, 0.80595386],
[ 0.70819044, -0.44549557],
[-0.54698859, 0.02834576]])
--
Robert Kern
robert.kern@gmail.com
"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco
