[Ipopt] Good solutions with errors in derivative test

[Chunhua Men]

Since IPOPT has derivative test, is it possible to use it to compute
derivatives (in grad_f, jac_g and hess), instead of implementing them by
our own?

Chunhua

On Wed, Feb 1, 2017 at 6:22 AM, Andrew Spiteri <andrew.spiteri at um.edu.mt>
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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