Yes that is possible. However you need to be very careful because lets
say you have a model:
sum_{lm} A_{lm} X_{lm} = Y_{lm}
and lets say that A_{lm} is real everytime m = 0 (this is quite common
in spherical harmonic expansions), then if you naively pack the matrix
as real imag..., the matrix will be singular because it will have 0 for
the imag part whenever m = 0.
This is why I suggest using the LAPACK complex routines, unless you have
a very simple problem and you know the matrix won't be singular.
On 08/07/2012 04:42 PM, Apurv Bhartia wrote:
"In principle you could order a real matrix and coeff and rhs vectors
as: real imag real imag, etc."
Sorry I'm new to GSL. In my dataset, X is real and Y, A are complex.
Just to make sure, do you mean that it can be done by having X, Y and
A as complex data sets, and packing them into a real vector by
alternating real and imaginary values?
This way, is it then possible to use gsl_multifit_linear(x, y, a, cov,
chisq, ws)?
Thanks,
Apurv
On Tue, Aug 7, 2012 at 4:56 PM, Patrick Alken
<[email protected] <mailto:[email protected]>> wrote:
There aren't any native complex implementations of the multifit
routines. In principle you could order a real matrix and coeff and
rhs vectors as: real imag real imag, etc. But its probably easiest
to use the complex LAPACK routines for this.
On 08/07/2012 03:29 PM, Apurv Bhartia wrote:
Hi,
Is there a way to use least squares fit for complex data sets?
All of the
*_multifit_* variants seem to require non-complex data. If
not, then any
advice on how I can somehow get this done?
Thanks,
Apurv
