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How can I get the (approximate) eigenvectors of a huge matrix?

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I have a huge symmetric matrix M and I want to get the eigenvectors to the k smallest eigenvalues of M (which have to be greater than 0). I know that the smallest eigenvalue is 0.
Currently I am using
but this results in memory consumption of over 100GB of RAM.
Steffen on 2 Dec 2014
Actually I have 128GB but the systems also needs some resources. Nonetheless my memory is not enough. It was swapping something like 50GB so I don't know how much memory there would be needed... (Even 200GB might be not enough.)
Therefore I am open for suggestions. M is a 150k x 150k matrix. Are there any approximate methods which need much less memory?
Matt J
Matt J on 2 Dec 2014
Edited: Matt J on 2 Dec 2014
I don't really understand why it's taking so much memory. What happens when you do

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Answers (2)

Thorsten on 2 Dec 2014
If M contains many 0's you can define M as a sparse matrix to speed up computation.

Andrew Knyazev
Andrew Knyazev on 15 May 2015


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