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

[John Schulman]

Oops, "wildly feasible" in the first paragraph should be "wildly infeasible"

On Wed, Nov 12, 2014 at 10:06 PM, John Schulman <john.d.schulman at gmail.com>
wrote:

> 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
>
>
>
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://list.coin-or.org/pipermail/ipopt/attachments/20141112/bcb02988/attachment.html>