CG improvements

nifty/minimization/conjugate_gradient.py

@@ -20,6 +20,7 @@ from __future__ import division
 import numpy as np
 
 from keepers import Loggable
+from nifty import Field
 
 
 class ConjugateGradient(Loggable, object):
@@ -75,7 +76,7 @@ class ConjugateGradient(Loggable, object):
 
     """
 
-    def __init__(self, convergence_tolerance=1E-4, convergence_level=3,
+    def __init__(self, convergence_tolerance=1E-8, convergence_level=3,
                  iteration_limit=None, reset_count=None,
                  preconditioner=None, callback=None):
 
@@ -121,13 +122,13 @@ class ConjugateGradient(Loggable, object):
 
         r = b - A(x0)
         d = self.preconditioner(r)
-        previous_gamma = r.vdot(d)
+        previous_gamma = (r.vdot(d)).real
         if previous_gamma == 0:
             self.logger.info("The starting guess is already perfect solution "
                              "for the inverse problem.")
             return x0, self.convergence_level+1
-        norm_b = np.sqrt(b.vdot(b))
-        x = x0
+        norm_b = np.sqrt((b.vdot(b)).real)
+        x = x0.copy()
         convergence = 0
         iteration_number = 1
         self.logger.info("Starting conjugate gradient.")
@@ -137,7 +138,7 @@ class ConjugateGradient(Loggable, object):
                 self.callback(x, iteration_number)
 
             q = A(d)
-            alpha = previous_gamma/d.vdot(q)
+            alpha = previous_gamma/d.vdot(q).real
 
             if not np.isfinite(alpha):
                 self.logger.error("Alpha became infinite! Stopping.")
@@ -146,7 +147,7 @@ class ConjugateGradient(Loggable, object):
             x += d * alpha
 
             reset = False
-            if alpha.real < 0:
+            if alpha < 0:
                 self.logger.warn("Positive definiteness of A violated!")
                 reset = True
             if self.reset_count is not None:
@@ -156,24 +157,26 @@ class ConjugateGradient(Loggable, object):
                 r = b - A(x)
             else:
                 r -= q * alpha
+            #tmp=r.val.get_full_data()
+            #tmp.imag=0.
+            #r=Field(r.domain,val=tmp)
 
             s = self.preconditioner(r)
-            gamma = r.vdot(s)
+            gamma = r.vdot(s).real
 
-            if gamma.real < 0:
+            if gamma < 0:
                 self.logger.warn("Positive definitness of preconditioner "
                                  "violated!")
 
             beta = max(0, gamma/previous_gamma)
+            print "beta:",beta
 
             delta = np.sqrt(gamma)/norm_b
+            print "delta:",delta
 
             self.logger.debug("Iteration : %08u   alpha = %3.1E   "
                               "beta = %3.1E   delta = %3.1E" %
-                              (iteration_number,
-                               np.real(alpha),
-                               np.real(beta),
-                               np.real(delta)))
+                              (iteration_number, alpha, beta, delta))
 
             if gamma == 0:
                 convergence = self.convergence_level+1
@@ -196,6 +199,7 @@ class ConjugateGradient(Loggable, object):
 
             d = s + d * beta
 
+            print "iter:",iteration_number
             iteration_number += 1
             previous_gamma = gamma