[Coin-ipopt] Some limitations with IPOPT

[jens.pettersson at se.abb.com]

Hi,

I'm running a project with process optimization where we solving large 
non-linear programs. The constraints are discretized DAE-models of the 
process and the objective is the squared difference between some process 
variables and their setpoints.Thus, the size of the NLP depends on both 
the size of the process model(no of equation and variables) and the number 
of discretization points.

As solvers we are using both SNOPT and IPOPT and we can easily switch 
between these solvers on the same application which makes up for a fair 
comparison. If we consider smaller models with a relatively large number 
of discretization points, IPOPT is performing very well. We have 
succesfully solved NLP:s with 80000 variables within a couple of minutes 
on a modern laptop. Typically the number of iterations for IPOPT in these 
cases are below 20.

However, for our larger process models with some 1900 
differential-algebraic equations IPOPT has some major problems already 
above some 10 discretization points. If we are lucky, it solves the 
problem under 100 iterations but many times it fails. SNOPT, on the other 
hand is very robust for these problems (se attached dump-files).

Which parameters may have an affect on the performance of IPOPT ?? I have 
enclosed the PARAMS-file as well.

//Jens
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