diff --git a/main.py b/main.py
index 720b3a8..3152815 120000
--- a/main.py
+++ b/main.py
@@ -1 +1 @@
-main_ista.py
\ No newline at end of file
+main_ISTA.py
\ No newline at end of file
diff --git a/main_ista.py b/main_ISTA.py
similarity index 77%
rename from main_ista.py
rename to main_ISTA.py
index c088ec8..eeda971 100644
--- a/main_ista.py
+++ b/main_ISTA.py
@@ -5,13 +5,13 @@
 >>> algorithm = Submission(data)
 >>> algorithm.run(np.inf, callbacks=metrics + submission_callbacks)
 """
-from cil.optimisation.algorithms import GD, ISTA, Algorithm
-from cil.optimisation.functions import SGFunction, IndicatorBox
+from cil.optimisation.algorithms import ISTA, Algorithm
+from cil.optimisation.functions import IndicatorBox, SGFunction
 from cil.optimisation.utilities import ConstantStepSize, Sampler, callbacks
 from petric import Dataset
 from sirf.contrib.partitioner import partitioner
 
-assert issubclass(GD, Algorithm)
+assert issubclass(ISTA, Algorithm)
 
 
 class MaxIteration(callbacks.Callback):
@@ -29,7 +29,7 @@ def __call__(self, algorithm: Algorithm):
 
 
 class Submission(ISTA):
-    # note that `issubclass(GD, Algorithm) == True`
+    # note that `issubclass(ISTA, Algorithm) == True`
     def __init__(self, data: Dataset, num_subsets: int = 7, step_size: float = 1e-6,
                  update_objective_interval: int = 10):
         """
@@ -45,13 +45,11 @@ def __init__(self, data: Dataset, num_subsets: int = 7, step_size: float = 1e-6,
             f.set_prior(data.prior)
 
         sampler = Sampler.random_without_replacement(len(obj_funs))
-        F = -SGFunction(obj_funs, sampler=sampler)   # negative to turn minimiser into maximiser
-        step_size_rule = ConstantStepSize(step_size) # ISTA default step_size is 0.99*2.0/F.L
+        F = -SGFunction(obj_funs, sampler=sampler)      # negative to turn minimiser into maximiser
+        step_size_rule = ConstantStepSize(step_size)    # ISTA default step_size is 0.99*2.0/F.L
+        g = IndicatorBox(lower=1e-6, accelerated=False) # "non-negativity" constraint
 
-        g = IndicatorBox(lower=1e-5, accelerated=False)
-
-        super().__init__(initial=data.OSEM_image.get_uniform_copy(1e-5),
-                         f=F, g=g, step_size=step_size_rule,
+        super().__init__(initial=data.OSEM_image, f=F, g=g, step_size=step_size_rule,
                          update_objective_interval=update_objective_interval)
 
 
diff --git a/main_SGD.py b/main_SGD.py
deleted file mode 100644
index 070cdaa..0000000
--- a/main_SGD.py
+++ /dev/null
@@ -1,55 +0,0 @@
-"""Main file to modify for submissions. It is used by e.g. example.ipynb and petric.py as follows:
-
->>> from main import Submission, submission_callbacks
->>> from petric import data, metrics
->>> algorithm = Submission(data)
->>> algorithm.run(np.inf, callbacks=metrics + submission_callbacks)
-"""
-from cil.optimisation.algorithms import GD, Algorithm
-from cil.optimisation.functions import SGFunction
-from cil.optimisation.utilities import ConstantStepSize, Sampler, callbacks
-from petric import Dataset
-from sirf.contrib.partitioner import partitioner
-
-assert issubclass(GD, Algorithm)
-
-
-class MaxIteration(callbacks.Callback):
-    """
-    The organisers try to `Submission(data).run(inf)` i.e. for infinite iterations (until timeout).
-    This callback forces stopping after `max_iteration` instead.
-    """
-    def __init__(self, max_iteration: int, verbose: int = 1):
-        super().__init__(verbose)
-        self.max_iteration = max_iteration
-
-    def __call__(self, algorithm: Algorithm):
-        if algorithm.iteration >= self.max_iteration:
-            raise StopIteration
-
-
-class Submission(GD):
-    # note that `issubclass(GD, Algorithm) == True`
-    def __init__(self, data: Dataset, num_subsets: int = 7, step_size: float = 1e-10,
-                 update_objective_interval: int = 10):
-        """
-        Initialisation function, setting up data & (hyper)parameters.
-        NB: in practice, `num_subsets` should likely be determined from the data.
-        WARNING: we also currently ignore the non-negativity constraint here.
-        This is just an example. Try to modify and improve it!
-        """
-        data_sub, acq_models, obj_funs = partitioner.data_partition(data.acquired_data, data.additive_term,
-                                                                    data.mult_factors, num_subsets, mode='staggered',
-                                                                    initial_image=data.OSEM_image)
-        for f in obj_funs: # add prior to every objective function
-            f.set_prior(data.prior)
-
-        sampler = Sampler.random_without_replacement(len(obj_funs))
-        F = -SGFunction(obj_funs, sampler=sampler)   # negative to turn minimiser into maximiser
-        step_size_rule = ConstantStepSize(step_size) # ISTA default step_size is 0.99*2.0/F.L
-
-        super().__init__(initial=data.OSEM_image, objective_function=F, step_size=step_size_rule,
-                         update_objective_interval=update_objective_interval)
-
-
-submission_callbacks = [MaxIteration(1000)]