[Ipopt] Low # iters, ensuring that solution remains feasible

[John Schulman]

Short:

I am calling Ipopt repeatedly to solve a series of subproblems.
For each subproblem, Ipopt is initialized with a feasible solution, and
max_iter is set to 50 or so.
The optimization terminates early, and often this intermediate solution is
wildly feasible.
I'm wondering if there are any settings that will ensure that the result is
nearly feasible.

Longer:

I am using Ipopt to solve a series of subproblems of the form
minimize f(x), subject to g(x) < delta,
Here g is a distance function of sorts, measuring Distance(x_0,x), where
x_0 is the initialization.
So the the initial point x_0 is feasible.
x has dimension 50000 or so, so I am using hessian_approximation with
limited memory.

I need to keep to a low number of iterations, say 50 or 100, so the overall
computation time remains reasonable.
It's not essential at all that the solution generated is optimal; I just
want to improve the objective as much as possible while remaining feasible.

I tried fiddling with the barrier parameters but didn't have any luck.
Any suggestions?
Thanks in advance for your time.

John
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