[Ipopt] Unconstrained optimization problems

[Drosos Kourounis]

Dear Andreas,

I have two questions which I could find by reading 
Ipopt's manual.

1) I didn't see in the manual how one can run
a simulation in the unconstrained case. Forgive
me if I skipped it. Is there any specific interface
for that class of problems or should we still use TNLP? 
If not, then setting m=0,  nnz_jac_g = 0,  
nnz_h_lag = 0 and returning false inside eval_g 
and eval_grad_g would it do the job?

2) This is what I am doing at the moment for the
unconstrained case in the simple hs071 example.
However, I see that eval_f is called 6 times before eval_grad_f
is called. When I change the initial guess and bounds
the number of objective function calls is reduced.
Is this expected or is it because I am doing something wrong?
I am asking because in the unconstrained case I would expect 
something like:

do until convergence
{
  eval_grad_f
  eval_f
  check_convergence
}

Best,
Drosos.