Re: [bug #6503] Uncaught nan in xh_vectRe: [bug #6503] Uncaught nan in xh_vectThis bug (http://gna.org/bugs/?func=detailitem&item_id=6503) could be a tough one, especially locating it within the minimisation code. Though it would be worthwhile to track down to prevent future issues. Don't forget that all the code within the 'minimise/' directory will eventually be split out into it's own software project (to become a relax dependency) - hence catching this would be useful to more than just relax users.
I have a feeling that the problem may be in the line search as all the
other algorithms - Augmented Lagrangian constraint algorithm (also
known as the Method of Multipliers) and Newton line search - have
iteration limits. The sub loop of the Method of Multipliers is
limited to 500. The upper limit in total is 1e7 although the
constraint algorithm causes this to terminate a little early. A
number of easy tests may pin-point the issue - turning constraints off
"constraints=0", changing the line search algorithm ("More-Thuente" is
a good one), changing the Hessian modification ("Chol" the cholesky
modification may work), or changing to a non line search algorithm
("fr" Fletcher-Reeves conjugate gradient or "dogleg" trust-region
algorithms for example). Another option is to increase the printed
output by increasing the "print_flag" flag value to 2, 3, etc. These
simple tests should hopefully pin-point the exact cause of the
minimisation issue.Also, which version of Python are you using? It could be an issue with how Python handles the value of NaN. The first catch should be easy although by definition NaN != NaN. I don't know if Python does this properly so it might be worth avoiding the NaN value to start with. As for the minimisation issue, it might be worth having a separate report for it.
Edward
URL: <http://gna.org/bugs/?func=detailitem&item_id=6503>
Summary: Uncaught nan in xh_vect Project: relax Submitted by: macraild Submitted on: Wednesday 08/02/2006 at 10:49 Category: None Priority: 5 - Normal Severity: 2 - Minor Status: None Privacy: Public Assigned to: macraild Originator Name: Originator Email: Open/Closed: Open Release: Repository: 1.2 line Operating System: None
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Details:
Through a typo in my script, I created a state in which the het nucleus and the proton were set to the same atom. This error was not caught, and xh_vect was set to array([nan, nan, nan]) Obviously, this caused problems, but again the errors were not caught:
Grid searches failed with:
Unconstrained grid search size: 11 (constraints may decrease this size).
Grid search ~~~~~~~~~~~
Searching the grid.
Parameter values: array([ 0.]) Function value: 1.0000000000000001e+300 Iterations: 11 Function calls: 11 Gradient calls: 0 Hessian calls: 0 Warning: None
and then minimisation gets to:
Method of Multipliers ~~~~~~~~~~~~~~~~~~~~~
k: 0 xk: array([ 0.]) fk: nan Entering sub-algorithm.
Newton minimisation ~~~~~~~~~~~~~~~~~~~ Line search: Backtracking line search. Hessian modification: The Gill, Murray, and Wright modified Cholesky algorithm.
k: 0 xk: array([ 0.]) fk: nan
and then seems stuck in a loop (I guess waiting for nan to reach some tolerance?)
Obviously the original error is easy to catch, but very different errors could easily have the same effect in the grid search and minimiser, so the question is how to catch things there?
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