question about inverse_matrix() vs solve_linsys()

Hi all

The documentation for the inverse_matrix() function states that it uses LU factorisation to invert, but also states that for most solutions the function solve_linsys() is more efficient, but it also uses LU factorisation.

Theoretically, that means that for an invertible matrix A,

> Ainv = inverse_matrix(A)

is the same as

> I = conform(A, 0, -1) ;create identity matrix 'I'
> do i = 0, nrow-1
> I(i,i) = 1
> end do
> Ainv = solve_linsys(A, I)

Mathematically they're the same, but is there any way in which the functions are coded that would make one more efficient than the other? Or is the 'inverse_matrix()' function just doing what I did in the second case?

I'm dealing with large covariance matrices for a lot of model control runs, so even a modest difference in efficiency would be interesting to know.

Many thanks

Will

_______________________________________________
ncl-talk mailing list
List instructions, subscriber options, unsubscribe:
http://mailman.ucar.edu/mailman/listinfo/ncl-talk
Received on Thu Apr 25 18:56:25 2013