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<DIV>Hi John, good to see groups next door also using Ipopt.</DIV>
<DIV><FONT style="size: +0"></FONT> </DIV>
<DIV>Generally speaking this is a pretty hard thing to do with an interior-point
method. Are you warm-starting the dual variables as well, or just the primal?
That may help, but it depends how closely related the subproblems are. I’d also
avoid doing quasi-newton hessian approximations if you have a speed-critical
application, you’ll get better convergence in most cases if you are using a
modeling tool that can provide exact Hessians. Have you looked at the breakdown
of computation time from print_timing_statistics?</DIV>
<DIV> </DIV>
<DIV>-Tony</DIV>
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<DIV style="font-color: black"><B>From:</B> <A title=john.d.schulman@gmail.com
href="mailto:john.d.schulman@gmail.com">John Schulman</A> </DIV>
<DIV><B>Sent:</B> Wednesday, November 12, 2014 10:14 PM</DIV>
<DIV><B>To:</B> <A title=ipopt@list.coin-or.org
href="mailto:ipopt@list.coin-or.org">ipopt@list.coin-or.org</A> </DIV>
<DIV><B>Subject:</B> Re: [Ipopt] Low # iters, ensuring that solution remains
feasible</DIV></DIV></DIV>
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<DIV dir=ltr>Oops, "wildly feasible" in the first paragraph should be "wildly
infeasible"</DIV>
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<DIV> </DIV>
<DIV class=gmail_quote>On Wed, Nov 12, 2014 at 10:06 PM, John Schulman <SPAN
dir=ltr><<A href="mailto:john.d.schulman@gmail.com"
target=_blank>john.d.schulman@gmail.com</A>></SPAN> wrote:<BR>
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style="PADDING-LEFT: 1ex; MARGIN: 0px 0px 0px 0.8ex; BORDER-LEFT: #ccc 1px solid">
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<DIV>Short: </DIV>
<DIV> </DIV>
<DIV>I am calling Ipopt repeatedly to solve a series of subproblems.</DIV>
<DIV>For each subproblem, Ipopt is initialized with a feasible solution, and
max_iter is set to 50 or so. </DIV>
<DIV>The optimization terminates early, and often this intermediate solution
is wildly feasible.</DIV>
<DIV>I'm wondering if there are any settings that will ensure that the result
is nearly feasible.</DIV>
<DIV> </DIV>
<DIV>Longer:</DIV>
<DIV> </DIV>I am using Ipopt to solve a series of subproblems of the form
<DIV>minimize f(x), subject to g(x) < delta,</DIV>
<DIV>Here g is a distance function of sorts, measuring Distance(x_0,x), where
x_0 is the initialization.</DIV>
<DIV>So the the initial point x_0 is feasible.</DIV>
<DIV>x has dimension 50000 or so, so I am using hessian_approximation with
limited memory.</DIV>
<DIV>
<DIV> </DIV>
<DIV>I need to keep to a low number of iterations, say 50 or 100, so the
overall computation time remains reasonable.</DIV></DIV>
<DIV>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.</DIV>
<DIV> </DIV>
<DIV>I tried fiddling with the barrier parameters but didn't have any
luck.</DIV>
<DIV>Any suggestions?<BR>Thanks in advance for your time.</DIV><SPAN
class=HOEnZb><FONT color=#888888>
<DIV> </DIV>
<DIV>John</DIV>
<DIV> </DIV>
<DIV> </DIV>
<DIV> </DIV>
<DIV> </DIV></FONT></SPAN></DIV></BLOCKQUOTE></DIV>
<DIV> </DIV></DIV>
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