[Ipopt] Very slow convergence

[Andreas Waechter]

Hi Nicolas,

Even 2000 iterations sound like a lot.  If you problem has hightly 
nonlinear constraints you might see a lot of jumps to the restoration 
phase, and you might want to experiment with using different formulations 
of the constraints.  (In general, a modeing language like AMPL and GAMS 
are very handy for this, before you sit down and write matlab code...)

But maybe the issue is just that your Hessian is not implemented 
correctly.  Did you verify them with the derivative checker?

Hope this helps,

Andreas

On Wed, 13 May 2009, Nicolas Gachadoit wrote:

> Hello,
>
> I use Ipopt for Optimal Control (minimum time control) and in one of my 
> applications, the convergence is very slow.
> It is a robotic application (4 dof), the equations (constraints and gradient 
> of constraints, automatically generated by Maple) are very big so it could be 
> the reason but on another hand, in another application (5 dof), the 
> convergence is fast (< 2000 iterations, less than 2 minutes).
> In this application (4 dof), I tried up to 20000 iterations and it did not 
> converge yet. Each time I increase max_iter, it is better (the minimum time 
> decreases and the controls are closer to saturations) so a possibility would 
> be to try to put a very high max_iter and wait for a few hours.
> But I would like to know if another option could make the convergence faster. 
> Maybe it is a problem of scaling or something else ?
>
> Thanks in advance,
> Best regards,
>
> Nicolas Gachadoit
>