Everything's working now. I just messed up the rows and columns.
Thanks a lot, guys.
On Sun, Jun 29, 2008 at 10:35 PM, Robert Kern <[EMAIL PROTECTED]> wrote:
> On Sun, Jun 29, 2008 at 21:14, Saket <[EMAIL PROTECTED]> wrote:
>> Also, I'm getting different numbers when I do the above using eig(A)
On Sun, Jun 29, 2008 at 21:14, Saket <[EMAIL PROTECTED]> wrote:
> Also, I'm getting different numbers when I do the above using eig(A)
> and eigh(A). I thought eigh would be faster, but would still give the
> same results...
What different numbers? Remember, as Chuck pointed out, you have a
multip
On Sun, Jun 29, 2008 at 21:00, Saket <[EMAIL PROTECTED]> wrote:
> Hmm... so the relationship Ax = Lx should hold for every eigenvalue
> and corresponding eigenvector of A, right? But, consider the first
> eigenvalue,eigenvector pair:
>
>for i,eval in enumerate(d):
>print abs(numpy.dot(A
Also, I'm getting different numbers when I do the above using eig(A)
and eigh(A). I thought eigh would be faster, but would still give the
same results...
On Sun, Jun 29, 2008 at 10:00 PM, Saket <[EMAIL PROTECTED]> wrote:
> Hmm... so the relationship Ax = Lx should hold for every eigenvalue
> and
Hmm... so the relationship Ax = Lx should hold for every eigenvalue
and corresponding eigenvector of A, right? But, consider the first
eigenvalue,eigenvector pair:
for i,eval in enumerate(d):
print abs(numpy.dot(A,v[i]) - numpy.dot(eval,v[i])).max()
return
Outputs: 1.928
I th
On Sun, Jun 29, 2008 at 6:47 PM, Saket <[EMAIL PROTECTED]> wrote:
> Hi,
>
> I'm having this weird problem when computing eigenvalues/vectors with
> Numpy. I have the following symmetric matrix, B:
>
> -0.34620.65380.5385 -0.46150.6538 -0.3462 -0.3462
> -0.3462
>0.6538 -0.
On Sun, Jun 29, 2008 at 6:47 PM, Saket <[EMAIL PROTECTED]> wrote:
> Hi,
>
> I'm having this weird problem when computing eigenvalues/vectors with
> Numpy. I have the following symmetric matrix, B:
>
> -0.34620.65380.5385 -0.46150.6538 -0.3462 -0.3462
> -0.3462
>0.6538 -0.
Hi,
I'm having this weird problem when computing eigenvalues/vectors with
Numpy. I have the following symmetric matrix, B:
-0.34620.65380.5385 -0.46150.6538 -0.3462 -0.3462 -0.3462
0.6538 -0.34620.5385 -0.46150.6538 -0.3462 -0.3462 -0.3462
0.5385
