Update for epf optimization

Author: Rolf Johan Lorentzen

popt/loop/optimize.py

@@ -102,6 +102,7 @@ def __init__(self, **options):
         self.epf_iteration = 0
 
         # Initialize variables (set in subclasses)
+        # TODO: these variables should be abstract properties that subclasses are forced to define 
         self.options = None
         self.mean_state = None
         self.obj_func_values = None
@@ -195,7 +196,7 @@ def run_loop(self):
                     logger.info(f'       -----> EPF-EnOpt: {self.epf_iteration}, {r} (outer iteration, penalty factor)')  # print epf info
                 else:
                     logger.info(f'       -----> EPF-EnOpt: converged, no variables changed more than {conv_crit*100} %')  # print epf info
-                    final_obj_no_penalty = str(round(float(self.fun(self.mean_state)),4))
+                    final_obj_no_penalty = str(round(float(np.mean(self.fun(self.mean_state))),4))
                     logger.info(f'       -----> EPF-EnOpt: objective value without penalty = {final_obj_no_penalty}') # print epf info
 
     def save(self):

popt/update_schemes/linesearch.py

@@ -283,9 +283,9 @@ def _fun(self, x):
         self.nfev += 1
         x = ot.clip_state(x, self.bounds) # ensure bounds are respected
         if self.args is None:
-            f = np.mean(self.function(x))
+            f = np.mean(self.function(x, **self.epf))
         else:
-            f = np.mean(self.function(x, *self.args))
+            f = np.mean(self.function(x, *self.args, **self.epf))
         return f
 
     def _jac(self, x):