r25502 - /trunk/test_suite/system_tests/relax_disp.py -- September 01, 2014 - 15:09

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)