Hi,
I'm a computer scientist, not a mathematician and had a question
about the Levenberg Marquardt (LM) algorithm and hoped someone would
be able to help or provide some advice. I wanted to perform
weighted Least-Squares and I have the following equation:
[ yi - f(xi,a)]^T Vi [ yi - f(xi,a)]
where yi is the dependent variable, xi is the independent variable,
a are the model parameters to be estimated, and Vi is a covariance
matrix.
I have solved the problem using an unweighted Least-squares but
would prefer to use weighted as some of my data have larger relative
uncertainties. I have seen on the GSL reference manual that I can
perform weighted Least-Squares using a scalar, but I wanted to use
the full covariance matrix, Vi. I did think about using the trace
or determinant of Vi, but not sure if that is mathematically as
sound so wanted to use Vi. My problem is that I can't work out how
to extend my code to include the matrix weight (rather than the
scalar weight) and wondered if I needed to modify the internal gsl
LM algorithm (or maybe rewrite the algorithm myself) or can apply I
apply a matrix weight using the existing gsl LM.
I hope someone can help with this, my email is: tombanwell * at *
hotmail * dot * com
Thanks
Tom
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