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

[Andreas Waechter]

Hi

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

http://www.research.ibm.com/people/a/andreasw/papers/ipopt.pdf

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 
\mu=0)

Hope this clarifies,

Andreas

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
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