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Sturla Kvamsdal

Norwegian School of Economics

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22 Feb 2011 Adaptive Robust Numerical Differentiation Numerical derivative of an analytically supplied function, also gradient, Jacobian & Hessian Author: John D'Errico

Hi John,

Thank you very much for the great suite!

The information on the Hessian-function explicitly states that it is not for frequent use on an expensive to evaluate objective function. I tried it anyway, because that is what I need. Most of the time, I get something reasonable (hard to check, but I'll worry about that later), but sometimes I get NaN's. Any idea what the problem might be?

Perhaps some more details are warranted: The function I want to differentiate is a likelihood-function. It is a function of time, and I need the Hessian at the maximum for each timestep. (The maximum is one point associated to the last time period.) The function takes 5 parameters.

Any help or ideas appreciated!

Thank you very much,

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