[Ipopt] Ipopt Digest, Vol 85, Issue 2

[Edson Cordeiro do Valle]

  Juan Carlos
This behavior is normal.
Ipopt tries to minimize a function subjected to constraints. Sometimes 
objective function is minimized, but constraints are violated, then it 
is necessary to "honor" the constraints, then objective function may 
increase. If should pay attention to final values and compare with 
initial guess.
Regards

                                                  Edson C. do Valle
                                                edsoncv at enq.ufrgs.br
                                                   Skype: edson.cv



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> Today's Topics:
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>    1. Convergence Problem!!! (Juan Carlos Lopez Alfonso)
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> ----------------------------------------------------------------------
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> Message: 1
> Date: Mon, 2 Jan 2012 13:23:31 +0100
> From: Juan Carlos Lopez Alfonso <jc.atlantis at gmail.com>
> To: ipopt at list.coin-or.org
> Subject: [Ipopt] Convergence Problem!!!
> Message-ID:
> 	<CAARD0fA5fb2fVjMcf_qmUa4dC8JNETcTOJv4YxGJyWmAoodd4w at mail.gmail.com>
> Content-Type: text/plain; charset="iso-8859-1"
>
> Hi all:
>
> First of all, may I wish you a happy new year 2012.
>
> I have a question related with the convergence of the IPopt optimization
> library. I have programmed my minimization problem and all its methods
> (hessian and gradient of the objetive function and constraints). On the
> other hand, my problem has a unique solution. When I execute my algorithm
> the value of the objetive function increases and decreases after different
> number of iterations. Is this behavior normal? Because, I thought that
> after several iterations, the value of the objective function should always
> decrease to converge to a minimum.
>
> Please, could anyone give me any idea of this issue?
> Thank you in advance,
> Best regards
> Juan Carlos
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> End of Ipopt Digest, Vol 85, Issue 2
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