[Coin-ipopt] question on "overall NLP error"

Andreas Waechter andreasw at watson.ibm.com
Mon Nov 5 15:33:06 EST 2007


You also asked about the meaning of the different error types.  Their 
definition can be found in the Ipopt implementation paper,


on page 3, Eq. (5).

The terms in the max are:

1. Dual infeasibility
2. Primal feasibility
3. Complementarity

And the quantity E_{\mu}(x,\lambda,z) is the overall NLP error (with 

Hope this clarifies,


On Sun, 4 Nov 2007, Peter Carbonetto wrote:

>> I suppose that the derivative check module is very useful, I used it to
>> fix gradient calculation and now my program runs quite well. But although
>> the obtained result is good, I found that "dual infeasibility" in the
>> output of the program is still high (around 1.0e02) (the number for
>> "overall NLP error" is the same).
>> So I wonder what is the role of "dual infeasibility" into the solution and
>> whether we can have a good result even this error is not perfect.
>> By the way, how do the "constraint violation" and "complementarity" are
>> calculated and their signification ? They are not cummulated to "overall
>> NLP error" ?
> You might find this previous discussion on the IPOPT mailing list useful:
> http://list.coin-or.org/pipermail/coin-ipopt/2007-February/000700.html
> Peter Carbonetto
> Ph.D. Candidate
> Dept. of Computer Science
> University of British Columbia
> _______________________________________________
> Coin-ipopt mailing list
> Coin-ipopt at list.coin-or.org
> http://list.coin-or.org/mailman/listinfo/coin-ipopt

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