Hello Anja,
On 06/03/2013 06:05 PM, Anja Schäfer wrote:
Hello,
I'm currently implementing a variation of the classical ICP algorithm
and it works fine with artificial data. If I use the real-world data I
actually have, the solution produced by the SLSQP algorithm is
infeasible (although there definitely IS a feasible solution).
I double-checked my constraint functions, I start with feasible data and
"I start with feasible data": Does this mean that your initial solution
is in the feasible region? Otherwise, SQP cannot guarantee to converge
to a feasible solution.
Also, remember that SQP still only works for convex problems. Your
objective function is very likely convex (standard ICP is a least
squares problem), but are your constraints? Could you describe what your
constraints are in your application (I know a little bit of ICP and
registration problems.)
Best regards,
Julius
I tried setting max_eval and max_time but nothing seems to work.
Is there anything else I can do?
Thanks
Anja
--
Dipl.-Inform. Julius Ziegler <[email protected]>
Institut für Mess- und Regelungstechnik
Karlsruher Institut für Technologie
Department of Measurement and Control
Karlsruhe Institute of Technology
Engler-Bunte-Ring 21
76131 Karlsruhe
Tel. +49 721 608 47146
_______________________________________________
NLopt-discuss mailing list
[email protected]
http://ab-initio.mit.edu/cgi-bin/mailman/listinfo/nlopt-discuss
