Send in epf dictionary to objective function

Author: Rolf Johan Lorentzen

popt/loop/optimize.py

@@ -185,7 +185,7 @@ def run_loop(self):
                     previous_state = self.mean_state
                     self.epf['r'] *= self.epf['r_factor']  # increase penalty factor
                     self.obj_func_tol *= self.epf['tol_factor']  # decrease tolerance
-                    self.obj_func_values = self.fun(self.mean_state, **self.epf)
+                    self.obj_func_values = self.fun(self.mean_state, epf = self.epf)
                     self.iteration = 0
                     self.epf_iteration += 1
                     optimize_result = ot.get_optimize_result(self)

popt/update_schemes/enopt.py

@@ -112,7 +112,7 @@ def __set__variable(var_name=None, defalut=None):
         # Calculate objective function of startpoint
         if not self.restart:
             self.start_time = time.perf_counter()
-            self.obj_func_values = self.fun(self.mean_state, **self.epf)
+            self.obj_func_values = self.fun(self.mean_state, epf=self.epf)
             self.nfev += 1
             self.optimize_result = ot.get_optimize_result(self)
             ot.save_optimize_results(self.optimize_result)
@@ -158,9 +158,9 @@ def calc_update(self):
             # Calculate gradient
             if self.nesterov:
                 gradient = self.jac(self.mean_state + self.beta*self.state_step,
-                                    shrink*(self.cov + self.beta*self.cov_step), **self.epf)
+                                    shrink*(self.cov + self.beta*self.cov_step), epf=self.epf)
             else:
-                gradient = self.jac(self.mean_state, shrink*self.cov, **self.epf)
+                gradient = self.jac(self.mean_state, shrink*self.cov, epf=self.epf)
             self.njev += 1
 
             # Compute the hessian
@@ -184,7 +184,7 @@ def calc_update(self):
                 new_state = ot.clip_state(new_state, self.bounds)
 
                 # Calculate new objective function
-                new_func_values = self.fun(new_state, **self.epf)
+                new_func_values = self.fun(new_state, epf=self.epf)
                 self.nfev += 1
 
                 if np.mean(self.obj_func_values) - np.mean(new_func_values) > self.obj_func_tol: