Timeline for Computing signature
Current License: CC BY-SA 3.0
11 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Sep 20, 2012 at 12:14 | answer | added | Bazin | timeline score: 2 | |
| Sep 20, 2012 at 6:54 | comment | added | Terry Loring | If you have a reasonable gap in the spectrum at $0$ then I think 1000 by 1000 matrices will be no trouble, working in double floating point, even in Matlab on a basic recent desktop. If you have access to a parallelized code of inversion then the Newton's method I mention should get you up to 3000 by 3000 matrices in under an hour, on an appropriate machine with lots of RAM and cores. This is for dense matrices. For sparse matrices, I don't know. | |
| Sep 20, 2012 at 0:53 | comment | added | Igor Rivin | @Terry: There is a chicken and egg aspect here; the hidden agenda of my question is how big matrices can I deal with... | |
| Sep 19, 2012 at 21:24 | answer | added | Suvrit | timeline score: 4 | |
| Sep 19, 2012 at 21:14 | comment | added | Terry Loring | Can you say something about the matrix? When I needed signatures of matrices back in the 1990s, the matrices were on the order of 400 by 400. Today this is trivially small for eigensolvers. Also, if you know how big a spectral gap you have, you know how much error you can tolerate in the computations. Finally, did you mean compute as in on a von Neumann computing machine, or compute as in paper and pencil? | |
| Sep 19, 2012 at 20:25 | answer | added | Denis Serre | timeline score: 5 | |
| Sep 19, 2012 at 19:02 | answer | added | Ian Agol | timeline score: 6 | |
| Sep 19, 2012 at 18:57 | answer | added | Ryan Budney | timeline score: 9 | |
| Sep 19, 2012 at 17:21 | answer | added | Terry Loring | timeline score: 3 | |
| Sep 19, 2012 at 17:03 | comment | added | Fernando Muro | Sure, Sylvester inertia law en.wikipedia.org/wiki/Sylvester's_law_of_inertia | |
| Sep 19, 2012 at 16:57 | history | asked | Igor Rivin | CC BY-SA 3.0 |