Numpy- Find Corresponding Eigenvectors of a Particular Set of Eigenvalues

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I'm trying to figure out how to find the corresponding eigenvectors of a particular set of eigenvalues with numpy.

I'm working on a project using Singular Value Decomposition, and I need to find Truncated SVD, which is the SVD with the k-largest Singular Values.

desired_singular_values = sorted_singular_values[:desired_num_singular]

Thanks in advance!

1
numpysvdeigenvalue
How do you compute sorted_singular_values? – Alberto Garcia-Raboso

1 Answers

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In response to the first question. If you need to implement the algorithm it depends on how you want to do it. I have some pages here for eigenvalue algorithms. If you want the eigenvalues in python. It is the following. 

w,v = numpy.linalg.eig(A)

in response the truncated SVD. There is another command within the SVD

u,s,vh = numpy.linalg.svd(A,fullmatrices=False)

Seen here Which gives the reduced SVD