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I am running the FEAST method option for Eigenvalues on sparse matrices of dimension around 500,000. I look for about 50 eigenvalues. I have noticed a massive sensitivity of performance on what the entries are.

For example, I can have two sparse matrices with exactly the same entries non-zero. I simply modify the complex phase of some entries. This can result in runtimes of 5x longer or more.

Has anyone encountered this problem or any possible solutions?

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    $\begingroup$ Hello, this sounds like an interesting question. Obviously you can't post your actual data if it's that large, but have you managed to isolate the issue to a small matrix that has the same sensitivity of performance to small changes? $\endgroup$ – Verbeia Sep 17 '15 at 1:41
  • $\begingroup$ I will be on the lookout for that, but I only really noticed for dimension greater than 10^5. $\endgroup$ – user16316 Sep 17 '15 at 13:54

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