Maybe this points in the right direction:
In all crashes so far at time of crash I have the largest negative
gradient component associated to a variable with a value equal to its
lower limit.
Sorry for bombarding you with mails, but being unable to see what
happens is really frustrating.
Thanks for your help!
mario
(previous mail)
Could a very big gradient component cause LBFGS and hence nlopt failure?
At time of crash gradient values are:
5.176e+04 7.228e+04 5.327e+04 3.118e+04 1.135e+05 7.503e+07 2.247e+05
2.538e+07 5.161e+04 1.341e+05 8.202e+04 1.382e+05 1.007e+07 2.192e+05
9.136e+06 1.3e+05 2.43e+06 4.379e+04 4.181e+04 3.196e+04 2.173e+04
4.488e+04 2.351e+04 1.457e+04 -3169 6.205e+04 1.295e+05 -4.859e+04
-2.23e+11 1469 1.653e+04
For variables in the range 0.001-1 are indeed high values, but how to be
sure the problem lies here?
Thanks for your help!
mario
(original mail)
I'm happily using NLopt for a computational evolution application I'm
developing. But now for a specific dataset it fails with "nlopt failure"
exception and I'm at a loss to understand why NLopt fails.
I'm using NLopt 2.2.2 on Linux through the C++ interface. The method is
nlopt::LD_LBFGS. My function returns reasonable values and the variables
seems ok at time of crash.
I disabled my try/catch blocks hoping to see where opt.optimize()
crashes, but the debugger always show I'm in mythrow().
Any suggestion on how to debug this?
To compare results with an old application I'm forced to stay with
LBFGS. Today I will upgrade to 2.2.4 and check my gradient function.
Beside these, I have no more ideas on what to check.
Thanks for your help!
mario
--
Ing. Mario Valle
Data Analysis and Visualization Group |
http://www.cscs.ch/~mvalle
Swiss National Supercomputing Centre (CSCS) | Tel: +41 (91) 610.82.60
v. Cantonale Galleria 2, 6928 Manno, Switzerland | Fax: +41 (91) 610.82.82
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