On Sat, Feb 25, 2023 at 4:11 PM Louis Petingi
wrote:
> Hi Thanks
>
> Very simply one of the solutions for the zero eigenvalue is the 1
> eigenvector. If I get back this 1 vector, for the 0 eigenvalue then the
> other eigenvectors will be in the right format I am looking for. Once
> again, the
Petingi
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From: Robert Kern
Sent: Saturday, February 25, 2023 2:28:45 PM
To: Discussion of Numerical Python
Subject: [Numpy-discussion] Re: non normalised eigenvectors
On Sat, Feb 25, 2023 at 2:11 PM Louis P
NY
> --
> *From:* Ilhan Polat
> *Sent:* Saturday, February 25, 2023 11:46 AM
> *To:* Discussion of Numerical Python
> *Subject:* [Numpy-discussion] Re: non normalised eigenvectors
>
> Could you elaborate a bit more about what you mean with original
&
On Sat, Feb 25, 2023 at 2:11 PM Louis Petingi
wrote:
> Thank you for the reply. I am working with the Laplacian matrix of a graph
> which is the Degree matrix minus the adjacency matrix.
> The Laplacian is a symmetric matrix and the smallest eigenvalue is zero.
> As the rows add it to 0, Lx=0x,
College of Staten Island
City University of NY
From: Ilhan Polat
Sent: Saturday, February 25, 2023 11:46 AM
To: Discussion of Numerical Python
Subject: [Numpy-discussion] Re: non normalised eigenvectors
Could you elaborate a bit more about what you mean
On Sat, Feb 25, 2023 at 11:39 AM wrote:
> Dear all,
>
> I am not an expert in NumPy but my undergraduate student is having some
> issues with the way Numpy returns the normalized eigenvectors corresponding
> to the eigenvalues. We do understand that an eigenvector is divided by the
> norm to get
Could you elaborate a bit more about what you mean with original
eigenvectors? They denote the direction hence you can scale them to any
size anyways.
On Sat, Feb 25, 2023 at 5:38 PM wrote:
> Dear all,
>
> I am not an expert in NumPy but my undergraduate student is having some
> issues with the
