Merge branch 'newton' into 'master'

Newton

See merge request !45

demos/wiener_filter_hamiltonian.py

 
 from nifty import *
 
-import plotly.offline as pl
-import plotly.graph_objs as go
+#import plotly.offline as pl
+#import plotly.graph_objs as go
 
 from mpi4py import MPI
 comm = MPI.COMM_WORLD

nifty/minimization/quasi_newton_minimizer.py

@@ -80,9 +80,8 @@ class QuasiNewtonMinimizer(Loggable, object):
                 convergence = self.convergence_level+2
                 break
 
-            current_position = energy.position
-            descend_direction = self._get_descend_direction(current_position,
-                                                            gradient)
+            # current position is encoded in energy object
+            descend_direction = self._get_descend_direction(energy)
 
             # compute the step length, which minimizes energy.value along the
             # search direction
@@ -124,5 +123,5 @@ class QuasiNewtonMinimizer(Loggable, object):
         return energy, convergence
 
     @abc.abstractmethod
-    def _get_descend_direction(self, gradient, gradient_norm):
+    def _get_descend_direction(self, energy):
         raise NotImplementedError

nifty/minimization/relaxed_newton.py

+# -*- coding: utf-8 -*-
+
+from .quasi_newton_minimizer import QuasiNewtonMinimizer
+
+
+class RelaxedNewton(QuasiNewtonMinimizer):
+    def _get_descend_direction(self, energy):
+        gradient = energy.gradient
+        curvature = energy.curvature
+        descend_direction = curvature.inverse_times(gradient)
+        norm = descend_direction.norm()
+        if norm != 1:
+            return descend_direction / -norm
+        else:
+            return descend_direction * -1

nifty/minimization/steepest_descent.py

@@ -4,8 +4,8 @@ from .quasi_newton_minimizer import QuasiNewtonMinimizer
 
 
 class SteepestDescent(QuasiNewtonMinimizer):
-    def _get_descend_direction(self, x, gradient):
-        descend_direction = gradient
+    def _get_descend_direction(self, energy):
+        descend_direction = energy.gradient
         norm = descend_direction.norm()
         if norm != 1:
             return descend_direction / -norm

nifty/minimization/vl_bfgs.py

@@ -24,7 +24,9 @@ class VL_BFGS(QuasiNewtonMinimizer):
         self._information_store = None
         return super(VL_BFGS, self).__call__(energy)
 
-    def _get_descend_direction(self, x, gradient):
+    def _get_descend_direction(self, energy):
+        x = energy.position
+        gradient = energy.gradient
         # initialize the information store if it doesn't already exist
         try:
             self._information_store.add_new_point(x, gradient)