intermediate stage

demos/critical_filtering.py

@@ -115,10 +115,13 @@ if __name__ == "__main__":
                               iteration_limit=3,
                               callback=convergence_measure)
 
-    minimizer2 = VL_BFGS(convergence_tolerance=0,
-                       iteration_limit=7,
+    minimizer2 = VL_BFGS(convergence_tolerance=1e-3,
+                       iteration_limit=70,
                        callback=convergence_measure,
-                       max_history_length=3)
+                       max_history_length=10)
+    minimizer3 = SteepestDescent(convergence_tolerance=1e-3,
+                       iteration_limit=70,
+                       callback=convergence_measure)
 
     # Setting starting position
     flat_power = Field(p_space,val=1e-8)
@@ -140,7 +143,7 @@ if __name__ == "__main__":
         # Initializing the power energy with updated parameters
         power_energy = CriticalPowerEnergy(position=t0, m=m0, D=D0, smoothness_prior=10., samples=3)
 
-        (power_energy, convergence) = minimizer1(power_energy)
+        (power_energy, convergence) = minimizer2(power_energy)
 
 
         # Setting new power spectrum

nifty/minimization/descent_minimizer.py

@@ -121,18 +121,18 @@ class DescentMinimizer(Loggable, object):
 
         """
 
-        print "into line search:"
-        print " pos: ",energy.position.val[0]
-        print " ene: ",energy.value
+        #print "into line search:"
+        #print " pos: ",energy.position.val[0]
+        #print " ene: ",energy.value
         convergence = 0
         f_k_minus_1 = None
         step_length = 0
         iteration_number = 1
 
         while True:
-            print "line search next iteration:"
-            print " pos: ",energy.position.val[0]
-            print " ene: ",energy.value
+            #print "line search next iteration:"
+            #print " pos: ",energy.position.val[0]
+            #print " ene: ",energy.value
             if self.callback is not None:
                 try:
                     self.callback(energy, iteration_number)
@@ -153,7 +153,7 @@ class DescentMinimizer(Loggable, object):
 
             # current position is encoded in energy object
             descent_direction = self.get_descent_direction(energy)
-            print "descent direction:",descent_direction.val[0]
+            #print "descent direction:",descent_direction.val[0]
             # compute the step length, which minimizes energy.value along the
             # search direction
             step_length, f_k, new_energy = \
@@ -161,17 +161,17 @@ class DescentMinimizer(Loggable, object):
                                                energy=energy,
                                                pk=descent_direction,
                                                f_k_minus_1=f_k_minus_1)
-            print "out of wolfe:"
-            print " old pos: ",energy.position.val[0]
-            print " old ene: ",energy.value
-            print " new pos: ",new_energy.position.val[0]
-            print " new ene: ",new_energy.value
-            print " f_k: ",f_k
+            #print "out of wolfe:"
+            #print " old pos: ",energy.position.val[0]
+            #print " old ene: ",energy.value
+            #print " new pos: ",new_energy.position.val[0]
+            #print " new ene: ",new_energy.value
+            #print " f_k: ",f_k
             f_k_minus_1 = energy.value
-            print " step length: ", step_length
+            #print " step length: ", step_length
             tx1=energy.position-new_energy.position
-            print " step length 2: ", (energy.position-new_energy.position).norm()
-            print " step length 3: ", new_energy.position.val[0]-energy.position.val[0]
+            #print " step length 2: ", (energy.position-new_energy.position).norm()
+            #print " step length 3: ", new_energy.position.val[0]-energy.position.val[0]
             # check if new energy value is bigger than old energy value
             if (new_energy.value - energy.value) > 0:
                 print "Line search algorithm returned a new energy that was larger than the old one. Stopping."

nifty/minimization/line_searching/line_search_strong_wolfe.py

@@ -61,6 +61,9 @@ class LineSearchStrongWolfe(LineSearch):
 
     """
 
+#    def __init__(self, c1=1e-4, c2=0.9,
+#                 max_step_size=1000000000, max_iterations=100,
+#                 max_zoom_iterations=100):
     def __init__(self, c1=1e-4, c2=0.9,
                  max_step_size=50, max_iterations=10,
                  max_zoom_iterations=10):
@@ -111,6 +114,7 @@ class LineSearchStrongWolfe(LineSearch):
         le_0 = self.line_energy.at(0)
         phi_0 = le_0.value
         phiprime_0 = le_0.dd
+        assert phiprime_0<0, "input direction must be a descent direction"
 
         if phiprime_0 == 0:
             self.logger.warn("Flat gradient in search direction.")
@@ -133,12 +137,12 @@ class LineSearchStrongWolfe(LineSearch):
 
         # start the minimization loop
         for i in xrange(max_iterations):
-            print "a0a1:",alpha0, alpha1
-            print "line search outer iteration", i
+            #print "a0a1:",alpha0, alpha1
+            #print "line search outer iteration", i
             le_alpha1 = self.line_energy.at(alpha1)
-            print "position:", le_alpha1.energy.position.val[0]
+            #print "position:", le_alpha1.energy.position.val[0]
             phi_alpha1 = le_alpha1.value
-            print "energy:", le_alpha1.value
+            #print "energy:", le_alpha1.value
             if alpha1 == 0:
                 self.logger.warn("Increment size became 0.")
                 alpha_star = 0.
@@ -175,6 +179,7 @@ class LineSearchStrongWolfe(LineSearch):
             # update alphas
             alpha0, alpha1 = alpha1, min(2*alpha1, max_step_size)
             if alpha1 == max_step_size:
+                print "bailout"
                 alpha_star = alpha1
                 phi_star = phi_alpha1
                 le_star = le_alpha1
@@ -235,10 +240,10 @@ class LineSearchStrongWolfe(LineSearch):
             The new Energy object on the new position.
 
         """
-        print "entering zoom"
-        print alpha_lo, alpha_hi
-        print "pos1:",self.line_energy.at(alpha_lo).energy.position.val[0]
-        print "pos2:",self.line_energy.at(alpha_hi).energy.position.val[0]
+        #print "entering zoom"
+        #print alpha_lo, alpha_hi
+        #print "pos1:",self.line_energy.at(alpha_lo).energy.position.val[0]
+        #print "pos2:",self.line_energy.at(alpha_hi).energy.position.val[0]
         max_iterations = self.max_zoom_iterations
         # define the cubic and quadratic interpolant checks
         cubic_delta = 0.2  # cubic