Armijo: Work with ISTA and FISTA and new default

Wrappers/Python/cil/optimisation/algorithms/FISTA.py

@@ -213,8 +213,19 @@ def update_objective(self):
         .. math:: f(x) + g(x)
 
         """
-        self.loss.append(self.f(self.x_old) + self.g(self.x_old))
+        self.loss.append(self.objective_function(self.x_old))
 
+    def objective_function(self, x):
+        """ Calculates the objective
+
+        .. math:: f(x) + g(x)
+
+        Parameters
+        ----------
+        x : DataContainer
+
+        """
+        return self.f(x) + self.g(x)
 
 class FISTA(ISTA):
 

Wrappers/Python/cil/optimisation/utilities/StepSizeMethods.py

@@ -82,6 +82,9 @@ class ArmijoStepSizeRule(StepSizeRule):
         The amount the step_size is reduced if the criterion is not met
     max_iterations: integer, optional, default is numpy.ceil (2 * numpy.log10(alpha) / numpy.log10(2))
         The maximum number of iterations to find a suitable step size 
+    warmstart: Boolean, default is True
+        If `warmstart = True` the initial step size at each Armijo iteration is the calculated step size from the last iteration. If `warmstart = False` at each  Armijo iteration, the initial step size is reset to the original, large `alpha`. 
+        In the case of *well-behaved* convex functions, `warmstart = True` is likely to be computationally less expensive. In the case of non-convex functions, or particularly tricky functions, setting `warmstart = False` may be beneficial. 
 
     Reference
     ------------
@@ -91,7 +94,7 @@ class ArmijoStepSizeRule(StepSizeRule):
 
     """
 
-    def __init__(self, alpha=1e6, beta=0.5, max_iterations=None):
+    def __init__(self, alpha=1e6, beta=0.5, max_iterations=None, warmstart=True):
         '''Initialises the step size rule 
         '''
 
@@ -106,6 +109,8 @@ def __init__(self, alpha=1e6, beta=0.5, max_iterations=None):
         self.max_iterations = max_iterations
         if self.max_iterations is None:
             self.max_iterations = numpy.ceil(2 * numpy.log10(self.alpha_orig) / numpy.log10(2))
+
+        self.warmstart=warmstart
 
     def get_step_size(self, algorithm):
         """
@@ -137,6 +142,8 @@ def get_step_size(self, algorithm):
         if k == self.max_iterations:
             raise ValueError(
                 'Could not find a proper step_size in {} loops. Consider increasing alpha or max_iterations.'.format(self.max_iterations))
+        if self.warmstart:
+            self.alpha_orig= self.alpha 
         return self.alpha