[Ipopt] Adaptive mu update / degenerate problems

[herve.martin.sc@libertysurf.fr]

Hi all,

I have two questions regarding the use of Ipopt, which I
hope someone can answer.

The first one concerns the adaptive mu update. I found it a
good option, as it tends to reduce the number of nonlinear
iterations compared to the monotone update. As I solve a
problem on which the constraints are expensive to calculate,
this is very important. However, I saw in the implementation
paper that the minimum mu is set to tol/10 with the monotone
update, whereas it is 1e-11 (mu_min) for the adaptive
option. Why is it different? Knowing that I use 1e-6 as
tolerance with Ipopt, should I increase the option mu_min
(since then mu would converge to 1e-7 with the monotone update)?

My second questions is about degenerate jacobians. In my
problem, it can happen than the jacobian becomes degenerate
(this a which is really hard to solve by modifying the
model). In this case Ipopt has trouble to converge and even
sometimes diverge. I saw that with the option
always_pertub_cd, I can activate permanently the heuristics
to handle degenerate jacobians, which seems to solve most of
my divergence issues. Are there undesirable side effects to
this option? Should I let it always activated and play
further with the jacobian regularization values?

Thanks in advance for any help
Hervé











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