[Numpy-discussion] How to solve homogeneous linear equations with NumPy?
Charles R Harris
charlesr.harris@gmail....
Thu Dec 3 00:04:05 CST 2009
On Wed, Dec 2, 2009 at 10:40 PM, Peter Cai <newptcai@gmail.com> wrote:
> How to solve homogeneous linear equations with NumPy?
>
>
>
> If I have homogeneous linear equations like this
>
> array([[-0.75, 0.25, 0.25, 0.25],
> [ 1. , -1. , 0. , 0. ],
> [ 1. , 0. , -1. , 0. ],
> [ 1. , 0. , 0. , -1. ]])
>
> And I want to get a non-zero solution for it. How can it be done with
> NumPy?
>
> linalg.solve only works on A * x = b where b does not contains only 0.
>
>
>
One way is to use the singular value decomposition
In [16]: a = array([[-0.75, 0.25, 0.25, 0.25],
[ 1. , -1. , 0. , 0. ],
[ 1. , 0. , -1. , 0. ],
[ 1. , 0. , 0. , -1. ]])
In [20]: l,v,r = svd(a)
In [21]: v
Out[21]:
array([ 2.17944947e+00, 1.00000000e+00, 1.00000000e+00,
1.11022302e-16])
In [22]: dot(a,r[-1])
Out[22]:
array([ -6.93889390e-17, 5.55111512e-17, 1.11022302e-16,1.11022302e-16])
Chuck
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