[Ipopt] Good solutions with errors in derivative test

[Stefan Vigerske]

Hi,

Ipopt compares your derivatives with some it computed via finite 
differences. If the latter isn't much accurate, then it's still possible 
that your derivatives are correct.

Stefan

On 02/01/2017 03:22 PM, Andrew Spiteri wrote:
> Hi all,
>
> I am using IPOPT (MATLAB interface) to solve trajectory optimisation
> problems of medium complexity (roughly 100 variables with 300
> constraints). Due to this complexity, I am using INTLAB to automatically
> calculate the gradient, Jacobians and Hessians, however I am now
> noticing that the IPOPT derivative test is showing a considerable number
> of errors (in grad_f, jac_g and hess), with relative errors ranging from
> about 0.0001 to 1. I'm not sure why INTLAB seems to be giving inaccurate
> values, but I'm suspecting the various vector manipulations (scaling,
> splitting and merging) which occur in eval_f, eval_g and eval_hess.
>
> The confusing part is that the solutions actually appear as expected,
> despite these errors being present. Furthermore, solutions are typically
> found within 100 iterations, which seems to indicate a reasonably good
> performance. So my question is: is it possible to have good solutions
> with good performance when the aforementioned errors are present in the
> problem?
>
> Thanks in advance.
> Andrew Spiteri
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> Ipopt at list.coin-or.org
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>


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