Hi everyone, I've been using NLopt to estimate the parameters of a system of differential equations by minimizing the least squares between my simulations and a set of experimental data.
The non-gradient based optimizations have worked very well. I started experimenting with using gradient based methods and estimating the jacobian from the sensitivity equations calculated by sundials, and I found that using the SciPy fmin_l_bfgs_b I'm able to obtain a local minima very quickly. All NLopt algorithms, however, quickly throw an NLopt error. The system I am working with is quite gnarly - some parameters are very, very sensitive, while others are completely robust. SciPy bfgs: http://pastie.org/8374252 NLopt bfgs: http://pastie.org/8374254 I've tried changing NLopt algorithms, but every single gradient based method I tried eventually throws an error. Any clue what might be causing it? I am happy to share the code, but this is a fairly large wrapper around SciPy and Assimulo ODEINT solvers, so posting a minimal example is not very easy. Federico
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