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| 2020-01-21 21:08:36 +0200 | received badge | ● Nice Question (source) |
| 2018-05-24 21:15:49 +0200 | commented question | Rank of float matrix @nbruin: I just want to compute dimension of the kernel, i don't want to compute the equation of the kernel. I haven't try the QR tech. I've try the following. I ask Sage to compute the singular value of A_float by asking Sage to compute the eigenvalue of the matrix A.transpose()*A. I've declare that a float less than 10^-10 is zero, and a float bigger than 0.01 is really no zero. And so,i can compute the rank by counting the eigenvalue which are bigger than 0.01. In practice, it work well. But, i wonder if Sage can't compute the singular value in a more efficient way. I've find than if my numbers where in RDF then just A_float.SVD() would do the job, but my float are in RR with 212 bits of precision, so i don't ... |
| 2018-05-24 17:15:34 +0200 | received badge | ● Student (source) |
| 2018-05-23 16:19:51 +0200 | asked a question | Rank of float matrix I want to compute the "numerical" rank of 9*18 FLOAT matrix A_float that i know is a good approximation of a REAL matrix A_real. Is there any method to do that ? More precisely, i want to know if A_real is of rank at most 8, so i compute all the minors of A_float which happen to be all less (in absolute value) than 10^-32, so i conclude that A_real has good chance to be of rank at most 8. Is there is any method better than computing the 3 000 000 9*9 minors of A_float (which takes a few minutes) but i want to run this computation over a few thousand of differents A_float. |
Copyright Sage, 2010. Some rights reserved under creative commons license. Content on this site is licensed under a Creative Commons Attribution Share Alike 3.0 license.