[Coin-ipopt] filter: current iterate feasible; can't do restoration. Abort

[Andreas Waechter]

Hi Matthew,

Given that your problem has only bound constraints, I suspect that there
is something wrong with the derivatives that Ipopt sees.  You might want
to check if the derivatives that you provide to the NLPAPI are correct.
Are you providing only first or also second derivatives?  Or are you using
some quasi-Newton option (either in Ipopt or in thew NLPAPI?).

When you write that your function is "fairly smooth", does it mean that
there are points where the first or second derivatives do not exist or are
not continuous?  In that case, you might just be stuck at a point where
your functions are not smooth, and then there is not much we can do about
it; Ipopt is written to solve problems where the functions are a least
twice differentiable.

I suggest you check the C code for the computation of the derivatives
(unless you are using the option to write the functions are strings in the
NLPAPI, in which case there might be a bug in the NLPAPI...).  If you
can't find anything wrong, you could send me the IPOPT.OUT file for a
print level iprint=3 (please to me directly to avoid spamming the mailing
list).  Maybe I can say more then.

Andreas


On Fri, 22 Apr 2005, Matthew Guthaus wrote:

> I am using IPopt through the C NLPAPI. I set up a problem successfully
> and get this output after a few iterations:
>
> ITER     ERR       MU      ||C||   ||YPY||  ||PZ||     ||D||   ALFA(V)
>   ALFA(X)     NU   #LS       F      #cor   Regu    CPU(s)
> ...
>    14 .104E+05d .100E+00 .000E+00 .000E+00 .000E+00  .342E-02 .447E+00d
> .100E+01f .000E+00  1 0.12190353E+02  0 .000E+00 .280E+00
> filter: current iterate feasible; can't do restoration. Abort
>   solve_barrier: filter returns IERR =  16
>   mainloop: Error: solve_barrier ends with IERR =  16
>
> I'm not quite sure what this means... I have tried playing with my
> tolerance and scaling factors, but this doesn't change much.
> The problem only has simple upper/lower bound constraints and is fairly
> smooth. It should have a minimum near 0. I have used the Matlab fmin
> function previously with good results, but with poor performance.
>
> Any insight from the IPOpt experts?
>
> Thanks,
>
> Matt
>
>
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