On 17 Feb, 11:33, "francesco santi" <fpsa...@gmail.com> wrote:
> Hello to everybody.
> Here is my problem:
> I have to compute a covariance matrix S at each step of my algorithm.
> This is done over an image matrix.
> The image matrix values are normalized to [0,1].
> Problems arise when I have to compute the determinant of S or its inverse.
> Sometimes it happens that S is singular or close to singular so I get numerical instability.
If the matrix is singular, the determinant is 0, or
nearly 0. T
> Should it happen, all the formulas following the computation of S will have a Nan or Inf value and the algorithm would not converge.
In that case you either work with an illconditioned problem,
use the wrong algorithm, or use an unstable implementation.
Or all of the above.
> I've found out the svd Matlab builtin function and I was wondering if it might be helpful to solve my probem and how.
The SVD *might* be helpful for certain types of problems,
but not all. Learning what the SVD is, how it works and
how to use it is far beyond USENET, the ideal approach
is to take a class on linear algebra.
If that's not an option, find a copy of Strang's "Linear
Algebra and its Applications" and read.
Rune
