|Re: [eigen] HELP : SVD too slow|
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On 06/29/2012 10:29:06 AM, Gaurav Gupta wrote:
The maximum dimension is 8 x 6, if bordered matrix approach can
speed, I am very much interested
So, you're interested in cases where such an 8 x 6 matrix doesn't have
full column rank.
If the rank defect is about 1 or 2, bordered system could be a speed up.
For a first impression you might have a look at
W. Govaerts, J.D. Pryce "Mixed block elimination for linear systems
with wider borders"
IMA J. Numer. Anal. 13 (1993), pp 161-180
or chapter 3 in
FORTRAN software is here
If you like I can send you my Scilab application which tries to find
a parameter-dependend matrix of prescribed rank (defect).
On Fri, Jun 29, 2012 at 1:55 PM, Helmut Jarausch <
> On 06/29/2012 10:21:25 AM, Gaurav Gupta wrote:
>> I opted for eigen3 to improve speed of my program than on MATLAB,
>> taking more time than MATLAB.
>> I am using JacobiSVD for calculating nullspace, which is taking
>> time, any suggestions for improving speed,
>> or any way to implement SVD faster.
> What dimension will the nullspace have?
> If not too large you could consider using a bordered matrix
> If you're interested I'll be happy to give to some references an
> Scilab implementation
> of this.