[Ipopt] Infeasible Problem Detected when the problem is actually feasible

Maxime Boulay mboulay at flogen.com
Fri Apr 6 10:25:14 EDT 2018

Hi Filip,


Thank you for your help. Is it possible that using a different linear solver
may help to avoid those local points of infeasibility on top of having  a
good initial guess?

I am presently using mumps.




From: Filip Jorissen [mailto:filip.jorissen at kuleuven.be] 
Sent: Friday, April 06, 2018 3:13 AM
To: Maxime Boulay
Cc: Ipopt at list.coin-or.org
Subject: Re: [Ipopt] Infeasible Problem Detected when the problem is
actually feasible




It sounds like IPOPT converges to a local point of infeasibility? I.e. your
equations are non-convex and you need a good/feasible initial guess to be
able to converge to a feasible (local) optimum.



Op 5 apr. 2018, om 17:27 heeft Maxime Boulay <mboulay at flogen.com> het
volgende geschreven:




I am using IpOpt to solve a problem with about 20 variables and 20
constraints. I know the solution to this problem, and I have verified that
my constraints are respected at the solution, there is also no error in the
Derivative Checker. However, IpOpt still ends up giving me the Infeasible
Problem Detected message when I run it. If I keep running it again with the
"warm_start_init_point", it eventually does find the solution. This happens
for many different entry values to this problem. Can anyone help me find a
clue as to why this would happen? 


Thank you.




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