Frank Küster writes:
> I am wondering whether (and how) it is possible to use the least-squares
> fitting, or alternatively minimization functions of gsl with constrained
> parameters. What I mean with this is that I want to force the function
> into the correct local minimum by supplying info
The way I personally do it is as follows. Two examples given.
Let's imagine we have two values (functions of the parameter vector) we
want to constrain. F1(p) and F2(p). F1(p) must lie close to a value f1,
and F2(p) must lie between f2_min and f2_max.
We have a function chi_squared(p) we're mi
[EMAIL PROTECTED] wrote:
> Hi Frank,
> let me try to give you a general answer which has nothing to do with GSL
> library. The better, safest and probably faster way of introducing the
> kind of constraints you mention is through a re-parameterization of the
> problem. For instance, if you want to
Hi Frank,
let me try to give you a general answer which has nothing to do with GSL
library. The better, safest and probably faster way of introducing the
kind of constraints you mention is through a re-parameterization of the
problem. For instance, if you want to fit the sum of two exponentials
a_
Hi,
I am wondering whether (and how) it is possible to use the least-squares
fitting, or alternatively minimization functions of gsl with constrained
parameters. What I mean with this is that I want to force the function
into the correct local minimum by supplying information which parameter
valu
