[Numpy-discussion] linear algebra help
Sat May 16 08:15:46 CDT 2009
On Sat, 16 May 2009 16:01:00 +0300
Quilby <email@example.com> wrote:
> This is what I need to do-
> I have this equation-
> Ax = y
> Where A is a rational m*n matrix (m<=n), and x and y are
> the right size. I know A and y, I don't know what x is
>equal to. I
> also know that there is no x where Ax equals exactly y.
>I want to find
> the vector x' such that Ax' is as close as possible to
>y. Meaning that
> (Ax' - y) is as close as possible to (0,0,0,...0).
> I know that I need to use either the lstsq function:
> or the svd function:
> I don't understand the documentation at all. Can someone
> me how to use these functions to solve my problem.
> Thanks a lot!!!
> Numpy-discussion mailing list
I guess you meant a rectangular matrix
from numpy.random import rand, seed
from numpy import dot, shape
from numpy.linalg import lstsq, norm
m = 10
n = 20
A = rand(m,n) # random matrix
b = rand(m) # rhs
x,residues,rank,s = lstsq(A,b)
print 'Singular values',s
print 'Numerical rank of A',rank
More information about the Numpy-discussion