On Tuesday 01 February 2011 03:27:22 Sturla Molden wrote:
> Den 31.01.2011 03:05, skrev Algis Kabaila:
> > Actually, the structural engineer
> > has no interest in trying to invert a singular matrix.
> > However he/she is interested (or should be interested :)
> > ) when the square response matri
Den 31.01.2011 03:05, skrev Algis Kabaila:
> Actually, the structural engineer
> has no interest in trying to invert a singular matrix. However
> he/she is interested (or should be interested :) ) when the
> square response matrix might approach singularity for this would
> signal instability.
I
>
> And if you are trying to solve a least-squares, I think that
> you should be using a ridge (or Tikhonov) regularisation:
> http://en.wikipedia.org/wiki/Tikhonov_regularization
> read in particular the paragraph above the table of content:
> you are most likely interested in Gamma = alpha ident
On Sun, Jan 30, 2011 at 04:15:34PM +0100, Sturla Molden wrote:
> Den 30.01.2011 07:28, skrev Algis Kabaila:
> > Why not simply numply.linalg.cond? This gives the condition
> > number directly (and presumably performs the inspection of
> > sv's). Or do you think that sv's give more useful informatio
On Sun, Jan 30, 2011 at 9:15 AM, Sturla Molden wrote:
> Den 30.01.2011 07:28, skrev Algis Kabaila:
>> Why not simply numply.linalg.cond? This gives the condition
>> number directly (and presumably performs the inspection of
>> sv's). Or do you think that sv's give more useful information?
>
> You
Den 30.01.2011 07:28, skrev Algis Kabaila:
> Why not simply numply.linalg.cond? This gives the condition
> number directly (and presumably performs the inspection of
> sv's). Or do you think that sv's give more useful information?
You can use the singular value decomposition to invert the matrix,
On Sunday 30 January 2011 16:35:15 Charles R Harris wrote:
> On Sat, Jan 29, 2011 at 10:11 PM, Algis Kabaila
wrote:
> > On Sunday 30 January 2011 09:10:30 Sturla Molden wrote:
> > > Den 29.01.2011 12:40, skrev Algis Kabaila:
> > > > So my question is: how can one reliably detect
> > > > singularit
On Sat, Jan 29, 2011 at 10:11 PM, Algis Kabaila wrote:
> On Sunday 30 January 2011 09:10:30 Sturla Molden wrote:
> > Den 29.01.2011 12:40, skrev Algis Kabaila:
> > > So my question is: how can one reliably detect singularity
> > > (or near singularity) and raise an exception?
> >
> > Use an SVD, e
On Sunday 30 January 2011 09:10:30 Sturla Molden wrote:
> Den 29.01.2011 12:40, skrev Algis Kabaila:
> > So my question is: how can one reliably detect singularity
> > (or near singularity) and raise an exception?
>
> Use an SVD, examine the singular values.
I gather that SVD is the Singular Valu
On Saturday 29 January 2011 22:47:23 Stuart Brorson wrote:
> > So my question is: how can one reliably detect singularity
> > (or near singularity) and raise an exception?
>
> Matrix condition number:
>
> http://docs.scipy.org/doc/numpy/reference/generated/numpy.lin
> alg.cond.html http://en.wiki
Den 29.01.2011 12:40, skrev Algis Kabaila:
> So my question is: how can one reliably detect singularity (or
> near singularity) and raise an exception?
Use an SVD, examine the singular values. One or more small singular
values indicate ill-conditioning. (What constitutes a small singular
value i
> So my question is: how can one reliably detect singularity (or
> near singularity) and raise an exception?
Matrix condition number:
http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.cond.html
http://en.wikipedia.org/wiki/Condition_number
Stuart
__
Hi,
I am interested in determining if a matrix is singular or
"nearly singular" - very ill conditioned. The problem occurs in
structural engineering applications.
My OS is kubuntu 10.10 (32 bit)
Python 2.6.6
numpy and numpy.linalg binaries from ubuntu repositories.
The attached tar ball has
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