URL:
<http://gna.org/bugs/?13495>
Summary: Global optimisation (diffusion tensor + model-free
models) failures.
Project: relax
Submitted by: bugman
Submitted on: Thursday 05/07/2009 at 14:51
Category: relax's source code
Severity: 2 - Minor
Priority: 1 - Later
Status: Postponed
Privacy: Public
Assigned to: None
Originator Name:
Originator Email:
Open/Closed: Open
Discussion Lock: Any
Release: Repository: 1.3 line
Operating System: All systems
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Details:
This problem may be related to relax, or maybe minfx
(https://gna.org/projects/minfx/). It appears when using low quality data,
but the exact cause is unknown. Using the full_analysis.py script at the 1st
round for ellipsoidal diffusion, the global optimisation (diffusion tensor +
model-free models) fails. The number of iterations of the Method of
Multipliers was high, at k = 84, and the following traceback was produced:
Traceback (most recent call last):
File "/home/prakti1/bin/relax", line 418, in <module>
Relax()
File "/home/prakti1/bin/relax", line 127, in __init__
self.interpreter.run(self.script_file)
File "/home/nfs_home/prakti1/relax-1.3/prompt/interpreter.py", line 273, in
run
return run_script(intro=self.__intro_string, local=self.local,
script_file=script_file, quit=self.__quit_flag,
show_script=self.__show_script, raise_relax_error=self.__raise_relax_error)
File "/home/nfs_home/prakti1/relax-1.3/prompt/interpreter.py", line 534, in
run_script
return console.interact(intro, local, script_file, quit,
show_script=show_script, raise_relax_error=raise_relax_error)
File "/home/nfs_home/prakti1/relax-1.3/prompt/interpreter.py", line 430, in
interact_script
execfile(script_file, local)
File "full_analysis_ellipsoid.py", line 648, in <module>
Main(self.relax)
File "full_analysis_ellipsoid.py", line 281, in __init__
minimise(MIN_ALGOR)
File "/home/nfs_home/prakti1/relax-1.3/prompt/minimisation.py", line 362,
in minimise
minimise.minimise(min_algor=min_algor, min_options=min_options,
func_tol=func_tol, grad_tol=grad_tol, max_iterations=max_iterations,
constraints=constraints, scaling=scaling, verbosity=verbosity)
File "/home/nfs_home/prakti1/relax-1.3/generic_fns/minimise.py", line 239,
in minimise
minimise(min_algor=min_algor, min_options=min_options, func_tol=func_tol,
grad_tol=grad_tol, max_iterations=max_iterations, constraints=constraints,
scaling=scaling, verbosity=verbosity)
File
"/home/nfs_home/prakti1/relax-1.3/specific_fns/model_free/mf_minimise.py",
line 987, in minimise
results = generic_minimise(func=self.mf.func, dfunc=self.mf.dfunc,
d2func=self.mf.d2func, args=(), x0=param_vector, min_algor=min_algor,
min_options=min_options, func_tol=func_tol, grad_tol=grad_tol,
maxiter=max_iterations, A=A, b=b, full_output=1, print_flag=verbosity)
File "/usr/lib/python2.5/site-packages/minfx/generic.py", line 412, in
generic_minimise
results = method_of_multipliers(func=func, dfunc=dfunc, d2func=d2func,
args=args, x0=x0, min_options=min_options, A=A, b=b, l=l, u=u, c=c, dc=dc,
d2c=d2c, func_tol=func_tol, grad_tol=grad_tol, maxiter=maxiter,
full_output=full_output, print_flag=print_flag)
File "/usr/lib/python2.5/site-packages/minfx/method_of_multipliers.py",
line 143, in method_of_multipliers
results = min.minimise()
File "/usr/lib/python2.5/site-packages/minfx/method_of_multipliers.py",
line 403, in minimise
results = self.generic_minimise(func=self.func_LA, dfunc=self.func_dLA,
d2func=self.func_d2LA, args=self.args, x0=self.xk, min_algor=self.min_algor,
min_options=self.min_options, func_tol=None, grad_tol=self.tk,
maxiter=maxiter, full_output=1, print_flag=sub_print_flag,
print_prefix="\t")
File "/usr/lib/python2.5/site-packages/minfx/generic.py", line 343, in
generic_minimise
results = newton(func=func, dfunc=dfunc, d2func=d2func, args=args, x0=x0,
min_options=min_options, func_tol=func_tol, grad_tol=grad_tol,
maxiter=maxiter, full_output=full_output, print_flag=print_flag,
print_prefix=print_prefix)
File "/usr/lib/python2.5/site-packages/minfx/newton.py", line 44, in
newton
results = min.minimise()
File "/usr/lib/python2.5/site-packages/minfx/base_classes.py", line 233, in
minimise
self.new_param_func()
File "/usr/lib/python2.5/site-packages/minfx/newton.py", line 166, in
new_param_func
self.line_search()
File "/usr/lib/python2.5/site-packages/minfx/base_classes.py", line 339, in
backline
self.alpha, fc = backtrack(self.func, self.args, self.xk, self.fk,
self.dfk, self.pk, a_init=self.a0)
TypeError: 'NoneType' object is not iterable
This can also be manifested as in another calculation:
Entering sub-algorithm.
Newton minimisation
~~~~~~~~~~~~~~~~~~~
Line search: Backtracking line search.
Hessian modification: The Gill, Murray, and Wright modified Cholesky
algorithm.
k: 0 xk: array([ 2.17662440e+003, 1.44710895e+001,
1.65376556e+000,
2.41233697e+000, 2.24669487e+000, 1.31986242e+000,
1.46930548e+000, 6.56681102e+002, 5.62750293e+000,
-2.63968527e-003, 9.54183249e-002, 9.74136389e+000,
1.02472797e+001, 1.04623403e+001, 4.24968647e+000,
1.06635164e+001, 1.09780711e+000, 5.13404062e+002,
1.22515943e+001, 1.08250923e+001, 1.11921181e+001,
1.13681012e+001, 1.15991176e+001, 1.18902213e+001,
-4.13005850e-010, 1.72190205e-008, 1.41995273e+001,
1.00041549e+000, 1.04448474e+001, 1.50481160e-001,
-5.82101417e-004, 9.93905068e+000, 1.13403676e+000,
2.63071040e+002, 1.02361083e+001, 1.17166354e+000,
9.77679448e+000, 1.20233012e+000, 1.71947013e+001,
1.09310769e+001, 1.46729878e+000, 9.12608861e+002,
9.71411844e+000, -1.64656184e-004, 4.74870623e-003,
1.22081302e+001, 2.38866662e-012, -7.28214745e-011,
1.09673544e+001, -1.43808068e-009, 4.46753417e-008,
1.26703772e+001, 1.13255151e+001, 3.09984216e-112,
3.38022725e-101, 1.24265254e+001, 1.58315372e-010,
-4.94381770e-009, 1.13298546e+001, 1.15163864e+001,
1.24152516e+001, -3.05188515e-003, 9.47591667e-002,
1.30407519e+001, 1.67146472e+000, 3.64187130e+002,
1.05675919e+001, 1.00779933e+000, 1.08434130e+000,
1.25774726e+003, 1.14850359e+001, -2.70021149e-003,
9.96735643e-002, 9.58491767e+000, -3.69317123e-003,
9.96394244e-002, 1.03383006e+001, 1.09375786e+001,
9.39265520e-001, 6.98318292e-002, 1.17717420e+001,
-2.98638984e-003, 9.41663821e-002, 1.18660304e+001,
1.77508218e+000, 6.46394179e+002, 1.00332947e+001,
1.05528779e+000, 1.13637717e+001, 1.06109867e+000,
7.04690167e+001, 1.03393653e+001, 1.00712482e+000,
6.96349273e+000, 1.24050750e+000, 2.90261670e+002,
9.88367615e+000, 9.78610846e+000, 9.62294469e-001,
9.64463484e-001, 4.06203017e+003, 1.05269760e+001,
1.02184195e+000, 8.03371056e+000, 1.06931727e+001,
1.13996521e+000, 1.38220523e+002, 1.01874731e+001,
-2.93093611e-003, 9.55837219e-002, 1.27028606e+001,
1.00793663e+000, 2.90233390e+000, 1.17931595e+001,
1.72244901e-004, -4.34977469e-003, 1.50281761e+001,
1.03383291e+000, 1.60879995e+002, 9.71691317e-001,
2.32822107e+002, 1.28558569e+001, -8.06776351e-006,
2.33527949e-004, 1.18279883e+001]) fk: 3.92671786027e+32
k: 100 xk: array([ NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN,
NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN]) fk: nan
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[bug #13495] Global optimisation (diffusion tensor + model-free models) failures.