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High-dimensional Minimization without analytical derivatives
- From: "Anatoliy Belaygorod" <belaygorod at wustl dot edu>
- To: <gsl-discuss at sources dot redhat dot com>
- Date: Mon, 30 Aug 2004 17:11:11 -0500
- Subject: High-dimensional Minimization without analytical derivatives
Hello,
I need to find a (local) maximum of my likelihood function with 80 datapoints over the 17-dimensional parameter space. I want to use gsl_multimin_fdfminimizer_vector_bfgs, (or some other gradient-based algorithm), but I would really hate to specify 17 (or maybe much more if we change the model) analytic derivatives.
Can you please tell me if I have better options? Can I use the one-dimensional numerical derivatives gsl_diff_central instead of analytic ones when I write "my_df" function for BFGS? How would this approach (if it is feasible at all) compare to Nelder Mead Simplex algorithm provided in my version of GSL 1.4? Is there a better option that would involve numerical gradient evaluation coupled with BFGS method?
I really appreciate any help or advice you can give me.
Sincerely,
Anatoliy