Timeline for Finding the smallest eigenvalues of a large, but structured, matrix
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
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| Aug 27, 2012 at 17:08 | vote | accept | Jeff | ||
| Aug 26, 2012 at 23:29 | comment | added | Jeff | @Federico -- It doesn't use shift-and-invert by default, at least when accessed through Scipy, but you're basically right -- once sigma is specified, shift-and-invert is used. However, with the default settings, I was encountering an error messages ("Error in inverting [A-sigma*M]: function gmres did not converge info=40"), even when run with a tiny (and invertible) 4 x 4 matrix. So I asked this question to figure out how to do it manually, though with the answers below, I'm starting to see how to work around the error, by overriding the defaults and substituting conjugate gradient for gmres. | |
| Aug 25, 2012 at 15:26 | comment | added | Andrew T. Barker | You might consider also asking this on scicomp.stackexchange.com | |
| Aug 25, 2012 at 13:34 | comment | added | Federico Poloni | Arpack should already use shift-and-invert if you set it to find the smallest eigenvalues of a matrix. Are you sure you are not trying to reinvent what is already implemented there? | |
| Aug 25, 2012 at 0:36 | answer | added | tergi | timeline score: 3 | |
| Aug 24, 2012 at 23:18 | history | edited | Jeff | CC BY-SA 3.0 |
deleted 4 characters in body
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| Aug 24, 2012 at 21:49 | answer | added | Igor Rivin | timeline score: 4 | |
| Aug 24, 2012 at 21:44 | history | asked | Jeff | CC BY-SA 3.0 |