ce
> {LU lu(C);
> Xc=lu.solve(c);
> cout << "LU con C: " << Xc <
> //LU with Varigram
> {LU lu(V);
> Xv=lu.solve(v);
> cout << "LU con V: " << Xv <
&
Adrian
It is a common misconception that using the covariance (total sill -
semi-variogram) rather than the semi-variogram brings more robust solutions.
You get exactly the same answer either way since one is just a constant minus
the other.
You can avoid solution problems by simple
What about to produce "pseudo covariance" to replace kriging matrix in term of
variogram to make more efficient the numerical solution of the system? The
ceros in the matrix diagonal are a problem in robustness and efficiency!
Some one knows how to implement something like that? Papers/books
