Author: tlinnet
Date: Mon Sep 1 15:09:41 2014
New Revision: 25502
URL: http://svn.gna.org/viewcvs/relax?rev=25502&view=rev
Log:
Another attempt to reach constrained method in minfx through relax.
I would need to specify:
l: Lower bound constraint vector (l <= x <= u).
u: Upper bound constraint vector (l <= x <= u).
c: User supplied constraint function.
dc: User supplied constraint gradient function.
Modified:
trunk/test_suite/system_tests/relax_disp.py
Modified: trunk/test_suite/system_tests/relax_disp.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/test_suite/system_tests/relax_disp.py?rev=25502&r1=25501&r2=25502&view=diff
==============================================================================
--- trunk/test_suite/system_tests/relax_disp.py (original)
+++ trunk/test_suite/system_tests/relax_disp.py Mon Sep 1 15:09:41 2014
@@ -8001,7 +8001,7 @@
# Set algorithm.
min_algor = 'Newton'
- constraints = False
+ constraints = True
if constraints:
min_options = ('%s'%(min_algor),)
#min_algor = 'Log barrier'
@@ -8011,12 +8011,12 @@
# Collect spins
all_spin_ids = []
for cur_spin, mol_name, resi, resn, spin_id in
spin_loop(full_info=True, return_id=True, skip_desel=True):
- all_spin_ids.append([spin_id])
+ all_spin_ids.append(spin_id)
spins = spin_ids_to_containers(all_spin_ids[:1])
# Get constraints
- A, b = linear_constraints(spins=spins,
scaling_matrix=scaling_matrix)
+ A, b = linear_constraints(spins=spins,
scaling_matrix=scaling_matrix[0])
else:
min_options = ()
A, b = None, None
@@ -8103,8 +8103,13 @@
# Ref input.
#def generic_minimise(func=None, dfunc=None, d2func=None,
args=(), x0=None, min_algor=None, min_options=None, func_tol=1e-25,
grad_tol=None, maxiter=1e6, A=None, b=None, l=None, u=None, c=None, dc=None,
d2c=None, print_flag=0, print_prefix="", full_output=False):
-
+ # l=l, u=u, c=c, dc=dc, d2c=d2c
+ # l: Lower bound constraint vector (l <= x <= u).
+ # u: Upper bound constraint vector (l <= x <= u).
+ # c: User supplied constraint function.
+ # dc: User supplied constraint gradient function.
params_minfx_sim_j, chi2_minfx_sim_j, iter_count, f_count,
g_count, h_count, warning = generic_minimise(func=func, dfunc=dfunc,
d2func=d2func, args=(), x0=x0, min_algor=min_algor, min_options=min_options,
A=A, b=b, full_output=True, print_flag=0)
+
R_m_sim_j, I0_m_sim_j = params_minfx_sim_j
R_m_sim_l.append(R_m_sim_j)
I0_m_sim_l.append(I0_m_sim_j)
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