[Ipopt] Good solutions with errors in derivative test

[Stefan Vigerske]

Hi,

there is a jacobian_approximation option in the TNLPAdapter. I think 
it's not part of the options documentation because it is strongly 
advised to provide exact first derivatives, often computed by some AD 
code. For Hessians, there is hessian_approximation,
https://www.coin-or.org/Ipopt/documentation/node53.html#SECTION0001113010000000000000

Stefan

On 02/01/2017 09:54 PM, Chunhua Men wrote:
> 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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>>
>
>
>
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