Now nonlinear CG does more exactly what is described in the cited reference....

Now nonlinear CG does more exactly what is described in the cited reference. Still does not do what we want though

nifty4/minimization/nonlinear_cg.py

@@ -40,7 +40,10 @@ class NonlinearCG(Minimizer):
     2006, Springer-Verlag New York
     """
 
-    def __init__(self, controller, line_searcher=LineSearchStrongWolfe()):
+    def __init__(self, controller, line_searcher=LineSearchStrongWolfe(c2=0.1), beta_heuristics = 'Polak-Ribiere'):
+        if (beta_heuristics != 'Polak-Ribiere') and (beta_heuristics != 'Polak-Ribiere'):
+            raise ValueError("beta heuristics must be either 'Polak-Ribiere' or 'Hestenes-Stiefel'")
+        self._beta_heuristic = beta_heuristics
         self._controller = controller
         self._line_searcher = line_searcher
 
@@ -63,6 +66,8 @@ class NonlinearCG(Minimizer):
             if status != controller.CONTINUE:
                 return energy, status
             grad_new = energy.gradient
-            gnnew = energy.gradient_norm
-            beta = gnnew*gnnew/(grad_new-grad_old).vdot(p).real
+            if self._beta_heuristic == 'Hestenes-Stiefel':
+                beta = grad_new.vdot(grad_new-grad_old)/(grad_new-grad_old).vdot(p).real
+            else:
+                beta = grad_new.vdot(grad_new-grad_old)/(grad_old.vdot(grad_old)).real
             p = beta*p - grad_new