<div dir="ltr">Oops, "wildly feasible" in the first paragraph should be "wildly infeasible"</div><div class="gmail_extra"><br><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><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div>Short: </div><div><br></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><br></div><div>Longer:</div><div><br></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><br></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><br></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><br></div><div>John</div><div><br></div><div><br></div><div><br></div><div><br></div></font></span></div>
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